mvp_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8d52675-d24d-4f54-8e15-4774f3fb4738",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fff3380f-f622-47b0-82f7-198f8d5da452",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"nba_stats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ebd1b18-32b3-440f-adc7-e3fc1a8301e4",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>season</th>\n",
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       "      <th>age</th>\n",
       "      <th>experience</th>\n",
       "      <th>lg_x</th>\n",
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       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>Alexander Volkov</td>\n",
       "      <td>C</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>72</td>\n",
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       "      <td>937.0</td>\n",
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       "      <td>0.547</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.277</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
       "      <td>Cliff Levingston</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>Doc Rivers</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>48</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.547</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>Dominique Wilkins</td>\n",
       "      <td>SF</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>80</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>111.9</td>\n",
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       "      <td>36.3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990</td>\n",
       "      <td>Duane Ferrell</td>\n",
       "      <td>SF</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NBA</td>\n",
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       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>113.0</td>\n",
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       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>2023</td>\n",
       "      <td>Monte Morris</td>\n",
       "      <td>PG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NBA</td>\n",
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       "      <td>62</td>\n",
       "      <td>61.0</td>\n",
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       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>2023</td>\n",
       "      <td>Quenton Jackson</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NBA</td>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>15611</th>\n",
       "      <td>2023</td>\n",
       "      <td>Taj Gibson</td>\n",
       "      <td>C</td>\n",
       "      <td>37.0</td>\n",
       "      <td>14</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>49</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>2023</td>\n",
       "      <td>Vernon Carey Jr.</td>\n",
       "      <td>C</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>114.4</td>\n",
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       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>2023</td>\n",
       "      <td>Xavier Cooks</td>\n",
       "      <td>PF</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.4</td>\n",
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       "      <td>0.258</td>\n",
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       "      <td>12.7</td>\n",
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       "      <td>0.202</td>\n",
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       "<p>15614 rows × 53 columns</p>\n",
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      ],
      "text/plain": [
       "       season             player pos   age  experience lg_x   tm   g    gs  \\\n",
       "0        1990   Alexander Volkov   C  25.0           1  NBA  ATL  72   4.0   \n",
       "1        1990   Cliff Levingston  PF  29.0           8  NBA  ATL  75   5.0   \n",
       "2        1990         Doc Rivers  PG  28.0           7  NBA  ATL  48  44.0   \n",
       "3        1990  Dominique Wilkins  SF  30.0           8  NBA  ATL  80  79.0   \n",
       "4        1990      Duane Ferrell  SF  24.0           2  NBA  ATL  14   0.0   \n",
       "...       ...                ...  ..   ...         ...  ...  ...  ..   ...   \n",
       "15609    2023       Monte Morris  PG  27.0           6  NBA  WAS  62  61.0   \n",
       "15610    2023    Quenton Jackson  PG  24.0           1  NBA  WAS   9   0.0   \n",
       "15611    2023         Taj Gibson   C  37.0          14  NBA  WAS  49   2.0   \n",
       "15612    2023   Vernon Carey Jr.   C  21.0           3  NBA  WAS  11   0.0   \n",
       "15613    2023       Xavier Cooks  PF  27.0           1  NBA  WAS  10   1.0   \n",
       "\n",
       "           mp  ...  o_rtg  d_rtg  n_rtg  pace   f_tr  x3p_ar  ts_percent  \\\n",
       "0       937.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "1      1706.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "2      1526.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "3      2888.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "4        29.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "...       ...  ...    ...    ...    ...   ...    ...     ...         ...   \n",
       "15609  1695.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15610   135.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15611   480.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15612    28.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15613   126.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "\n",
       "       tov_percent  orb_percent  ft_fga  \n",
       "0             13.5         36.3   0.277  \n",
       "1             13.5         36.3   0.277  \n",
       "2             13.5         36.3   0.277  \n",
       "3             13.5         36.3   0.277  \n",
       "4             13.5         36.3   0.277  \n",
       "...            ...          ...     ...  \n",
       "15609         12.7         22.6   0.202  \n",
       "15610         12.7         22.6   0.202  \n",
       "15611         12.7         22.6   0.202  \n",
       "15612         12.7         22.6   0.202  \n",
       "15613         12.7         22.6   0.202  \n",
       "\n",
       "[15614 rows x 53 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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     "data": {
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       "season            0\n",
       "player            0\n",
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       "dtype: int64"
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     "execution_count": 4,
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   "source": [
    "df.isnull().sum()"
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       "      <th></th>\n",
       "      <th>season</th>\n",
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       "      <th>0</th>\n",
       "      <td>1990</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.547</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7</td>\n",
       "      <td>48</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>218</td>\n",
       "      <td>480</td>\n",
       "      <td>0.454</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.547</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>810</td>\n",
       "      <td>1672</td>\n",
       "      <td>0.484</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.547</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>113.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>95.8</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.547</td>\n",
       "      <td>13.5</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>2023</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6</td>\n",
       "      <td>62</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>247</td>\n",
       "      <td>515</td>\n",
       "      <td>0.480</td>\n",
       "      <td>78.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.4</td>\n",
       "      <td>115.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.585</td>\n",
       "      <td>12.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>2023</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>19</td>\n",
       "      <td>42</td>\n",
       "      <td>0.452</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.4</td>\n",
       "      <td>115.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.585</td>\n",
       "      <td>12.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15611</th>\n",
       "      <td>2023</td>\n",
       "      <td>37.0</td>\n",
       "      <td>14</td>\n",
       "      <td>49</td>\n",
       "      <td>2.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>65</td>\n",
       "      <td>125</td>\n",
       "      <td>0.520</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.4</td>\n",
       "      <td>115.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.585</td>\n",
       "      <td>12.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>2023</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.4</td>\n",
       "      <td>115.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.585</td>\n",
       "      <td>12.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>2023</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>114.4</td>\n",
       "      <td>115.6</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.585</td>\n",
       "      <td>12.7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15614 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season   age  experience   g    gs      mp   fg   fga  fg_percent  \\\n",
       "0        1990  25.0           1  72   4.0   937.0  137   284       0.482   \n",
       "1        1990  29.0           8  75   5.0  1706.0  216   424       0.509   \n",
       "2        1990  28.0           7  48  44.0  1526.0  218   480       0.454   \n",
       "3        1990  30.0           8  80  79.0  2888.0  810  1672       0.484   \n",
       "4        1990  24.0           2  14   0.0    29.0    5    14       0.357   \n",
       "...       ...   ...         ...  ..   ...     ...  ...   ...         ...   \n",
       "15609    2023  27.0           6  62  61.0  1695.0  247   515       0.480   \n",
       "15610    2023  24.0           1   9   0.0   135.0   19    42       0.452   \n",
       "15611    2023  37.0          14  49   2.0   480.0   65   125       0.520   \n",
       "15612    2023  21.0           3  11   0.0    28.0    2     8       0.250   \n",
       "15613    2023  27.0           1  10   1.0   126.0   17    28       0.607   \n",
       "\n",
       "        x3p  ...  o_rtg  d_rtg  n_rtg  pace   f_tr  x3p_ar  ts_percent  \\\n",
       "0      13.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "1       1.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "2      24.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "3      59.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "4       0.0  ...  113.0  111.9    1.1  95.8  0.362   0.059       0.547   \n",
       "...     ...  ...    ...    ...    ...   ...    ...     ...         ...   \n",
       "15609  78.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15610   1.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15611   8.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15612   0.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "15613   0.0  ...  114.4  115.6   -1.2  98.5  0.258   0.365       0.585   \n",
       "\n",
       "       tov_percent  orb_percent  ft_fga  \n",
       "0             13.5         36.3   0.277  \n",
       "1             13.5         36.3   0.277  \n",
       "2             13.5         36.3   0.277  \n",
       "3             13.5         36.3   0.277  \n",
       "4             13.5         36.3   0.277  \n",
       "...            ...          ...     ...  \n",
       "15609         12.7         22.6   0.202  \n",
       "15610         12.7         22.6   0.202  \n",
       "15611         12.7         22.6   0.202  \n",
       "15612         12.7         22.6   0.202  \n",
       "15613         12.7         22.6   0.202  \n",
       "\n",
       "[15614 rows x 48 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(exclude = \"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ccf97b8-b9f1-47d0-aef4-6a982ff0b882",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>pos</th>\n",
       "      <th>lg_x</th>\n",
       "      <th>tm</th>\n",
       "      <th>team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alexander Volkov</td>\n",
       "      <td>C</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cliff Levingston</td>\n",
       "      <td>PF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Doc Rivers</td>\n",
       "      <td>PG</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dominique Wilkins</td>\n",
       "      <td>SF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Duane Ferrell</td>\n",
       "      <td>SF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>Monte Morris</td>\n",
       "      <td>PG</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Washington Wizards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>Quenton Jackson</td>\n",
       "      <td>PG</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Washington Wizards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15611</th>\n",
       "      <td>Taj Gibson</td>\n",
       "      <td>C</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Washington Wizards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>Vernon Carey Jr.</td>\n",
       "      <td>C</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Washington Wizards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>Xavier Cooks</td>\n",
       "      <td>PF</td>\n",
       "      <td>NBA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>Washington Wizards</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15614 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  player pos lg_x   tm                team\n",
       "0       Alexander Volkov   C  NBA  ATL       Atlanta Hawks\n",
       "1       Cliff Levingston  PF  NBA  ATL       Atlanta Hawks\n",
       "2             Doc Rivers  PG  NBA  ATL       Atlanta Hawks\n",
       "3      Dominique Wilkins  SF  NBA  ATL       Atlanta Hawks\n",
       "4          Duane Ferrell  SF  NBA  ATL       Atlanta Hawks\n",
       "...                  ...  ..  ...  ...                 ...\n",
       "15609       Monte Morris  PG  NBA  WAS  Washington Wizards\n",
       "15610    Quenton Jackson  PG  NBA  WAS  Washington Wizards\n",
       "15611         Taj Gibson   C  NBA  WAS  Washington Wizards\n",
       "15612   Vernon Carey Jr.   C  NBA  WAS  Washington Wizards\n",
       "15613       Xavier Cooks  PF  NBA  WAS  Washington Wizards\n",
       "\n",
       "[15614 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(exclude = [np.number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f5a4dd9-b650-4bd5-a6bb-19771d6303d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#select train_test dataset\n",
    "train = df[df[\"season\"] <= 2022]\n",
    "test = df[df[\"season\"] == 2023]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc88a260-0685-42a0-9f5d-a5a6b5a6593f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['season', 'age', 'experience', 'g', 'gs', 'mp', 'fg', 'fga',\n",
    "       'fg_percent', 'x3p', 'x3pa', 'x3p_percent', 'x2p', 'x2pa',\n",
    "       'x2p_percent', 'e_fg_percent_x', 'ft', 'fta', 'ft_percent', 'orb',\n",
    "       'drb', 'trb', 'ast', 'stl', 'blk', 'tov', 'pf', 'pts', 'w', 'l', 'pw', 'pl', 'mov', 'sos', 'srs', 'o_rtg',\n",
    "       'd_rtg', 'n_rtg', 'pace', 'f_tr', 'x3p_ar', 'ts_percent', 'tov_percent',\n",
    "       'orb_percent', 'ft_fga']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "893361cc-5d75-4cda-9710-a0aab55a6add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e525370f-9487-4b41-8e3c-3f24edd70095",
   "metadata": {},
   "source": [
    "TRAINING THE MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4d534e5-c80e-4347-a2c0-e6f8f31d9c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import our estimators\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7244a154-76f4-45bd-84e1-944d5e619e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = LinearSVR(C=1.0, random_state = 42, dual = 'auto')\n",
    "knn = make_pipeline(StandardScaler(),KNeighborsRegressor(weights = 'distance', n_jobs=2, n_neighbors=10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dc1f01d8-0de7-472f-8f2b-aa854433cc59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(C=1, dual=&#x27;auto&#x27;, max_iter=3000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearSVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVR.html\">?<span>Documentation for LinearSVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR(C=1, dual=&#x27;auto&#x27;, max_iter=3000, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVR(C=1, dual='auto', max_iter=3000, random_state=42)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.set_params(C=1, max_iter =3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1e2fa31b-6a42-4a30-bd5e-31407eebc58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;,\n",
       "                 KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10,\n",
       "                                     weights=&#x27;distance&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;,\n",
       "                 KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10,\n",
       "                                     weights=&#x27;distance&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10, weights=&#x27;distance&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('kneighborsregressor',\n",
       "                 KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10,\n",
       "                                     weights='distance'))])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.set_params(kneighborsregressor__leaf_size =20,\n",
    "  kneighborsregressor__n_neighbors = 10,\n",
    "  kneighborsregressor__weights = 'distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "00df0e8d-2a2a-4c71-8034-a85bd1b70e16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVR(C=1, dual=&#x27;auto&#x27;, max_iter=3000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearSVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVR.html\">?<span>Documentation for LinearSVR</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR(C=1, dual=&#x27;auto&#x27;, max_iter=3000, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVR(C=1, dual='auto', max_iter=3000, random_state=42)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.fit(train[predictors], train['share'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "884dd268-878b-4c05-90a0-53eeebd1538d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-11 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;,\n",
       "                 KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10,\n",
       "                                     weights=&#x27;distance&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;,\n",
       "                 KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10,\n",
       "                                     weights=&#x27;distance&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10, weights=&#x27;distance&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('kneighborsregressor',\n",
       "                 KNeighborsRegressor(leaf_size=20, n_jobs=2, n_neighbors=10,\n",
       "                                     weights='distance'))])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(train[predictors], train['share'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "56d4fb8e-09f7-4158-84f5-7918b4b63964",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_1 = svr.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a72d24db-e3fd-4068-9d63-e0889b7ef447",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_2 = knn.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a945d330-25a0-4f96-baf2-1f7e5a51a38b",
   "metadata": {},
   "source": [
    "predicted_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ec17a974-1887-4cc7-959d-8f51209a0649",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_1 = pd.DataFrame(predicted_1, columns = ['predicted'], index = test.index)\n",
    "predicted_2 = pd.DataFrame(predicted_2, columns = ['predicted'], index = test.index)\n",
    "\n",
    "result_knn = pd.concat([test[['player', 'share']], predicted_2], axis =1)\n",
    "result_svr = pd.concat([test[['player', 'share']], predicted_1], axis=1)\n",
    "\n",
    "result_knn = result_knn.sort_values('share', ascending=False)\n",
    "result_knn[\"Rk\"] = list(range(1, result_knn.shape[0] + 1))\n",
    "\n",
    "result_svr = result_svr.sort_values('share', ascending=False)\n",
    "result_svr[\"Rk\"] = list(range(1, result_svr.shape[0] + 1))\n",
    "\n",
    "result_svr = result_svr.sort_values('predicted', ascending = False)\n",
    "result_svr['predicted_Rk']= list(range(1,result_svr.shape[0]+1))\n",
    "result_knn = result_knn.sort_values('predicted', ascending = False)\n",
    "result_knn['predicted_Rk']= list(range(1,result_knn.shape[0]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bb754daa-685e-4135-b89e-48458989b011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>share</th>\n",
       "      <th>predicted</th>\n",
       "      <th>Rk</th>\n",
       "      <th>predicted_Rk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15362</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.567172</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15214</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.384324</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15520</th>\n",
       "      <td>Domantas Sabonis</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.261102</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15466</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.242073</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15100</th>\n",
       "      <td>Jayson Tatum</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.166166</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15439</th>\n",
       "      <td>Shai Gilgeous-Alexander</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.137310</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15254</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.069204</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15418</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.051418</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15495</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.047637</td>\n",
       "      <td>225</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15196</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.042531</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        player  share  predicted   Rk  predicted_Rk\n",
       "15362    Giannis Antetokounmpo  0.606   0.567172    3             1\n",
       "15214             Nikola Jokić  0.674   0.384324    2             2\n",
       "15520         Domantas Sabonis  0.027   0.261102    7             3\n",
       "15466              Joel Embiid  0.915   0.242073    1             4\n",
       "15100             Jayson Tatum  0.280   0.166166    4             5\n",
       "15439  Shai Gilgeous-Alexander  0.046   0.137310    5             6\n",
       "15254            Stephen Curry  0.005   0.069204    9             7\n",
       "15418            Julius Randle  0.000   0.051418  111             8\n",
       "15495           Damian Lillard  0.000   0.047637  225             9\n",
       "15196              Luka Dončić  0.010   0.042531    8            10"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_knn= result_knn.sort_values('predicted', ascending=False).head(10)\n",
    "result_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c1e0ffbe-eb92-4d23-86e6-a916c8075021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>share</th>\n",
       "      <th>predicted</th>\n",
       "      <th>Rk</th>\n",
       "      <th>predicted_Rk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15362</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.188760</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15466</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.176192</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15196</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.174055</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15214</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.161685</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15100</th>\n",
       "      <td>Jayson Tatum</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.139362</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15439</th>\n",
       "      <td>Shai Gilgeous-Alexander</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.136500</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15520</th>\n",
       "      <td>Domantas Sabonis</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.119165</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15418</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.108362</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15307</th>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.107425</td>\n",
       "      <td>413</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15495</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.106548</td>\n",
       "      <td>225</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        player  share  predicted   Rk  predicted_Rk\n",
       "15362    Giannis Antetokounmpo  0.606   0.188760    3             1\n",
       "15466              Joel Embiid  0.915   0.176192    1             2\n",
       "15196              Luka Dončić  0.010   0.174055    8             3\n",
       "15214             Nikola Jokić  0.674   0.161685    2             4\n",
       "15100             Jayson Tatum  0.280   0.139362    4             5\n",
       "15439  Shai Gilgeous-Alexander  0.046   0.136500    5             6\n",
       "15520         Domantas Sabonis  0.027   0.119165    7             7\n",
       "15418            Julius Randle  0.000   0.108362  111             8\n",
       "15307            Anthony Davis  0.000   0.107425  413             9\n",
       "15495           Damian Lillard  0.000   0.106548  225            10"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_svr = result_svr.sort_values('predicted', ascending=False).head(10)\n",
    "result_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12762a43-4117-4396-b006-f65dad0ed0d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "347a04c0-4a44-42d0-8bc4-ca40f89e426c",
   "metadata": {},
   "source": [
    "METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e63d4dd2-ed1f-4ed3-b22b-428c824db362",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c7eb68c4-888c-4398-bae2-df5ab354b5e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse_knn = mean_squared_error(result_knn['share'], result_knn['predicted'])\n",
    "mse_svr = mean_squared_error(result_svr['share'], result_svr['predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6ae91ac7-6e29-4b8f-98a0-1e6db4e2322c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mae_knn = mean_absolute_error(result_knn['share'], result_knn['predicted'])\n",
    "mae_svr = mean_absolute_error(result_svr['share'], result_svr['predicted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "df208e7c-0038-48d4-87be-52864ad4c6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.062444417470786814, 0.10804060865215574)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_knn, mse_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c31f400d-c690-4b19-9901-18059f523dc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1636467110444247, 0.24780561356134076)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_knn, mae_svr\n",
    "#The nearest neighbor model shows more promising accuracy over LinearSVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "76dbef48-45f7-4d0b-9d3e-96d6f6fe2dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evalauting Top Six predictions' accuracy using custom Average Precision(ap)\n",
    "def find_ap(result):\n",
    "    actual = result.sort_values('share', ascending = False).head(6)\n",
    "    predicted = result.sort_values('predicted', ascending = False)\n",
    "    ps = []\n",
    "    found = 0\n",
    "    seen = 1\n",
    "    for index, row in predicted.iterrows():\n",
    "        if row['player'] in actual['player'].values:\n",
    "            found += 1\n",
    "            ps.append(found/seen)\n",
    "\n",
    "        seen += 1\n",
    "    return sum(ps)/len(ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b4abb862-55db-47df-833c-e52c08f8a7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.8734126984126984)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing the top-range of the actual result vs the predicted results sees the best result interchange between the two models\n",
    "find_ap(result_knn), find_ap(result_svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6346ac9a-80fc-4141-8b03-818a09ced026",
   "metadata": {},
   "outputs": [],
   "source": [
    "#HYPER TUNNING\n",
    "from sklearn.model_selection import GridSearchCV, RepeatedKFold\n",
    "\n",
    "cv = RepeatedKFold(n_splits =2, n_repeats =5, random_state=42)\n",
    "\n",
    "svr_params ={\n",
    "    'C' : [1,2,5,10],\n",
    "    'max_iter' : [1000, 1500,2000, 3000],\n",
    "}\n",
    "\n",
    "knn_params = {\n",
    "    'kneighborsregressor__leaf_size' : [20,30,50,100],\n",
    "    'kneighborsregressor__n_neighbors' : [2,5,10],\n",
    "    'kneighborsregressor__weights': ['distance', 'uniform']\n",
    "}\n",
    "\n",
    "grid_k = GridSearchCV(knn, param_grid = knn_params, cv=cv)\n",
    "\n",
    "grid_s = GridSearchCV(svr, param_grid = svr_params, cv=cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "51ad8dcc-58c3-4901-bbdc-e6b17603a37b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=2, random_state=42),\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;kneighborsregressor&#x27;,\n",
       "                                        KNeighborsRegressor(n_jobs=2,\n",
       "                                                            n_neighbors=10,\n",
       "                                                            weights=&#x27;distance&#x27;))]),\n",
       "             param_grid={&#x27;kneighborsregressor__leaf_size&#x27;: [20, 30, 50, 100],\n",
       "                         &#x27;kneighborsregressor__n_neighbors&#x27;: [2, 5, 10],\n",
       "                         &#x27;kneighborsregressor__weights&#x27;: [&#x27;distance&#x27;,\n",
       "                                                          &#x27;uniform&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=2, random_state=42),\n",
       "             estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;kneighborsregressor&#x27;,\n",
       "                                        KNeighborsRegressor(n_jobs=2,\n",
       "                                                            n_neighbors=10,\n",
       "                                                            weights=&#x27;distance&#x27;))]),\n",
       "             param_grid={&#x27;kneighborsregressor__leaf_size&#x27;: [20, 30, 50, 100],\n",
       "                         &#x27;kneighborsregressor__n_neighbors&#x27;: [2, 5, 10],\n",
       "                         &#x27;kneighborsregressor__weights&#x27;: [&#x27;distance&#x27;,\n",
       "                                                          &#x27;uniform&#x27;]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
       "                (&#x27;kneighborsregressor&#x27;,\n",
       "                 KNeighborsRegressor(n_jobs=2, n_neighbors=10,\n",
       "                                     weights=&#x27;distance&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor(n_jobs=2, n_neighbors=10, weights=&#x27;distance&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=2, random_state=42),\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('kneighborsregressor',\n",
       "                                        KNeighborsRegressor(n_jobs=2,\n",
       "                                                            n_neighbors=10,\n",
       "                                                            weights='distance'))]),\n",
       "             param_grid={'kneighborsregressor__leaf_size': [20, 30, 50, 100],\n",
       "                         'kneighborsregressor__n_neighbors': [2, 5, 10],\n",
       "                         'kneighborsregressor__weights': ['distance',\n",
       "                                                          'uniform']})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_k.fit(train[predictors], train['share'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "846bc492-82e3-41e5-a881-98e1a27ea343",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=2, random_state=42),\n",
       "             estimator=LinearSVR(dual=&#x27;auto&#x27;, random_state=42),\n",
       "             param_grid={&#x27;C&#x27;: [1, 2, 5, 10],\n",
       "                         &#x27;max_iter&#x27;: [1000, 1500, 2000, 3000]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=2, random_state=42),\n",
       "             estimator=LinearSVR(dual=&#x27;auto&#x27;, random_state=42),\n",
       "             param_grid={&#x27;C&#x27;: [1, 2, 5, 10],\n",
       "                         &#x27;max_iter&#x27;: [1000, 1500, 2000, 3000]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: LinearSVR</label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR(dual=&#x27;auto&#x27;, random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearSVR<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVR.html\">?<span>Documentation for LinearSVR</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVR(dual=&#x27;auto&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=RepeatedKFold(n_repeats=5, n_splits=2, random_state=42),\n",
       "             estimator=LinearSVR(dual='auto', random_state=42),\n",
       "             param_grid={'C': [1, 2, 5, 10],\n",
       "                         'max_iter': [1000, 1500, 2000, 3000]})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_s.fit(train[predictors], train['share'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "86a43e23-c039-4b50-a6d4-70443a7a1f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'max_iter': 3000}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_s.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f9f248f0-7ee2-4ec6-9a71-96bc21d6f576",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kneighborsregressor__leaf_size': 20,\n",
       " 'kneighborsregressor__n_neighbors': 10,\n",
       " 'kneighborsregressor__weights': 'distance'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_k.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e64b6a89-6fdf-43ac-aad4-93685b8bc323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1454383748495769, 0.511324877996787)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grid_s.best_score_, grid_k.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128c1baf-026a-4b7a-ba58-c4d10a7042b6",
   "metadata": {},
   "source": [
    "BACKTESTING\n",
    "\n",
    "#backtesting the models across different seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da323a62-d490-4997-9350-22f5ad78c440",
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list(range(1990, 2024))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "674d7ab6-9788-4b73-933e-4ce6aaab16d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(df, model, year, predictors):\n",
    "    aps = []\n",
    "    all_predictions = []\n",
    "    for year in years[5:]:\n",
    "        train = df[df['season'] < year]\n",
    "        test = df[df['season'] == year]\n",
    "        model.fit(train[predictors], train['share'])\n",
    "        prediction = model.predict(test[predictors])\n",
    "        prediction  = pd.DataFrame(prediction, columns = ['predicted'], index = test.index)\n",
    "        result = pd.concat([test[['player', 'season', 'share']], prediction], axis =1)\n",
    "\n",
    "        result = result.sort_values('share', ascending= False)\n",
    "        result['Rk'] = list(range(1, result.shape[0] +1))\n",
    "        result = result.sort_values('predicted', ascending= False).head(10)\n",
    "        result['predicted_Rk'] = list(range(1, result.shape[0] +1))\n",
    "        result['diff'] = result['Rk'] - result['predicted_Rk']\n",
    "    \n",
    "        all_predictions.append(result)\n",
    "        aps.append(find_ap(result))\n",
    "\n",
    "    return sum(aps)/len(aps), aps, pd.concat(all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f1e5b7d-b5f6-472c-924d-48137e6fd3ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "93ba53ee-ac8a-4b54-a413-2cb55152e647",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "mean_aps, aps, predictions = backtest(df, pipe_1, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "896a8555-1b46-4958-8e1d-8f4d50a9dcce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8641420361247947"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_aps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e0ccd2cf-0d92-4e6a-b2b4-bbd752e0d541",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = predictions.groupby('season', group_keys = False)\n",
    "#How do i group predictions by first_ten predicted_Rk, then the next ten??. Through this i can each seasson's prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd1858e0-c59e-4531-ad5d-11ec543a302c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>share</th>\n",
       "      <th>predicted</th>\n",
       "      <th>Rk</th>\n",
       "      <th>predicted_Rk</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>David Robinson</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.295284</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.286748</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.279029</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>Hakeem Olajuwon</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>Patrick Ewing</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.223371</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>Scottie Pippen</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.195908</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>Charles Barkley</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.193691</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2310</th>\n",
       "      <td>Gary Payton</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.171331</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>Alonzo Mourning</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.167909</td>\n",
       "      <td>298</td>\n",
       "      <td>9</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>Clyde Drexler</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.160807</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>David Robinson</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.090117</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2742</th>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.072778</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Hakeem Olajuwon</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.063936</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2627</th>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.057775</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>Michael Jordan</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.056142</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>Charles Barkley</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.040740</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2613</th>\n",
       "      <td>Anfernee Hardaway</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.036800</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2663</th>\n",
       "      <td>Arvydas Sabonis</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.034951</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>Alonzo Mourning</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.032505</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>Patrick Ewing</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.028741</td>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 player  share  predicted   Rk  predicted_Rk  diff\n",
       "2293     David Robinson  0.858   0.295284    1             1     0\n",
       "2229   Shaquille O'Neal  0.576   0.286748    2             2     0\n",
       "2330        Karl Malone  0.507   0.279029    3             3     0\n",
       "2088    Hakeem Olajuwon  0.140   0.254265    5             4     1\n",
       "2214      Patrick Ewing  0.219   0.223371    4             5    -1\n",
       "2002     Scottie Pippen  0.079   0.195908    7             6     1\n",
       "2251    Charles Barkley  0.091   0.193691    6             7    -1\n",
       "2310        Gary Payton  0.032   0.171331    9             8     1\n",
       "1976    Alonzo Mourning  0.000   0.167909  298             9   289\n",
       "2087      Clyde Drexler  0.003   0.160807   14            10     4\n",
       "2694     David Robinson  0.508   0.090117    2             1     1\n",
       "2742        Karl Malone  0.075   0.072778    7             2     5\n",
       "2488    Hakeem Olajuwon  0.211   0.063936    4             3     1\n",
       "2627   Shaquille O'Neal  0.056   0.057775    9             4     5\n",
       "2405     Michael Jordan  0.986   0.056142    1             5    -4\n",
       "2647    Charles Barkley  0.007   0.040740   13             6     7\n",
       "2613  Anfernee Hardaway  0.319   0.036800    3             7    -4\n",
       "2663    Arvydas Sabonis  0.000   0.034951   59             8    51\n",
       "2543    Alonzo Mourning  0.005   0.032505   14             9     5\n",
       "2610      Patrick Ewing  0.000   0.028741   98            10    88"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.groupby(['season'], group_keys = False).apply(\n",
    "    lambda x: x.sort_values('predicted_Rk').head(10),\n",
    "    include_groups = False\n",
    "    ).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "03e24926-fba9-45b5-a901-db344d38d521",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>season</th>\n",
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       "  <tbody>\n",
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       "      <th>14794</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.168803</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14920</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.151238</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>14627</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.145582</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14602</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.107743</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14490</th>\n",
       "      <td>Trae Young</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.106885</td>\n",
       "      <td>384</td>\n",
       "      <td>5</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14535</th>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.106346</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14736</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.088731</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14524</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.087997</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14753</th>\n",
       "      <td>Ja Morant</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.083553</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14502</th>\n",
       "      <td>Jayson Tatum</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.082106</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      player  season  share  predicted   Rk  predicted_Rk  \\\n",
       "14794  Giannis Antetokounmpo    2022  0.595   0.168803    3             1   \n",
       "14920            Joel Embiid    2022  0.706   0.151238    2             2   \n",
       "14627           Nikola Jokić    2022  0.875   0.145582    1             3   \n",
       "14602            Luka Dončić    2022  0.146   0.107743    5             4   \n",
       "14490             Trae Young    2022  0.000   0.106885  384             5   \n",
       "14535          DeMar DeRozan    2022  0.001   0.106346   10             6   \n",
       "14736           LeBron James    2022  0.001   0.088731   12             7   \n",
       "14524           Kevin Durant    2022  0.001   0.087997   11             8   \n",
       "14753              Ja Morant    2022  0.010   0.083553    7             9   \n",
       "14502           Jayson Tatum    2022  0.043   0.082106    6            10   \n",
       "\n",
       "       diff  \n",
       "14794     2  \n",
       "14920     0  \n",
       "14627    -2  \n",
       "14602     1  \n",
       "14490   379  \n",
       "14535     4  \n",
       "14736     5  \n",
       "14524     3  \n",
       "14753    -2  \n",
       "14502    -4  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre.get_group(2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2ac7236-6000-4ae0-8561-e7bf3879b79a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d250be2-2921-46ff-b19d-bf2418233f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_aps_2, aps_2, predictions_2 = backtest(df, pipe_2, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "718ea6f0-250b-4d24-abb9-3238612495d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8641420361247947, 0.893044152526911)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#backtesting the model on past data shows that over the range of seasons,\n",
    "#knn model has a better rate of accuracy in predicting the top 6 MVP players for each season.\n",
    "\n",
    "mean_aps, mean_aps_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "38c73bd5-d5e3-4492-82e7-5b45dd26e268",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player</th>\n",
       "      <th>share</th>\n",
       "      <th>predicted</th>\n",
       "      <th>Rk</th>\n",
       "      <th>predicted_Rk</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>Hakeem Olajuwon</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.413811</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2293</th>\n",
       "      <td>David Robinson</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.346073</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.337022</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>Patrick Ewing</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.328380</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>Detlef Schrempf</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.305630</td>\n",
       "      <td>123</td>\n",
       "      <td>5</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.254393</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>Scottie Pippen</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.200003</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>Alonzo Mourning</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.185535</td>\n",
       "      <td>298</td>\n",
       "      <td>8</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>Charles Barkley</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.184747</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>Shawn Kemp</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.176188</td>\n",
       "      <td>116</td>\n",
       "      <td>10</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>Michael Jordan</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.633348</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>David Robinson</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.487327</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2742</th>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.339405</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>Patrick Ewing</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.277151</td>\n",
       "      <td>98</td>\n",
       "      <td>4</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2488</th>\n",
       "      <td>Hakeem Olajuwon</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.227550</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2711</th>\n",
       "      <td>Shawn Kemp</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.133437</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2408</th>\n",
       "      <td>Scottie Pippen</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.130937</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2647</th>\n",
       "      <td>Charles Barkley</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.072805</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>Alonzo Mourning</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.046550</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>Grant Hill</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.045767</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                player  share  predicted   Rk  predicted_Rk  diff\n",
       "2088   Hakeem Olajuwon  0.140   0.413811    5             1     4\n",
       "2293    David Robinson  0.858   0.346073    1             2    -1\n",
       "2229  Shaquille O'Neal  0.576   0.337022    2             3    -1\n",
       "2214     Patrick Ewing  0.219   0.328380    4             4     0\n",
       "2307   Detlef Schrempf  0.000   0.305630  123             5   118\n",
       "2330       Karl Malone  0.507   0.254393    3             6    -3\n",
       "2002    Scottie Pippen  0.079   0.200003    7             7     0\n",
       "1976   Alonzo Mourning  0.000   0.185535  298             8   290\n",
       "2251   Charles Barkley  0.091   0.184747    6             9    -3\n",
       "2315        Shawn Kemp  0.000   0.176188  116            10   106\n",
       "2405    Michael Jordan  0.986   0.633348    1             1     0\n",
       "2694    David Robinson  0.508   0.487327    2             2     0\n",
       "2742       Karl Malone  0.075   0.339405    7             3     4\n",
       "2610     Patrick Ewing  0.000   0.277151   98             4    94\n",
       "2488   Hakeem Olajuwon  0.211   0.227550    4             5    -1\n",
       "2711        Shawn Kemp  0.065   0.133437    8             6     2\n",
       "2408    Scottie Pippen  0.200   0.130937    5             7    -2\n",
       "2647   Charles Barkley  0.007   0.072805   13             8     5\n",
       "2543   Alonzo Mourning  0.005   0.046550   14             9     5\n",
       "2458        Grant Hill  0.056   0.045767   10            10     0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_2.groupby(['season'], group_keys = False).apply(\n",
    "    lambda x: x.sort_values('predicted_Rk').head(10),\n",
    "    include_groups = False\n",
    "    ).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d369fa9-2e70-442e-ae72-a50de38286c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d156e654-ef04-4f83-9628-a70f253a4eb6",
   "metadata": {},
   "source": [
    "EVALUATING THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ac9098b5-36d7-4d4f-ae5f-3129bdfc491f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Permutation Importance\n",
    "from sklearn.inspection import permutation_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "64ff5569-4cbf-40e4-a565-a2ac8f979793",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#LinearSVR\n",
    "train_importance = permutation_importance(\n",
    "    svr, train[predictors], train['share'], random_state = 0, n_repeats = 42, n_jobs = 2\n",
    ")\n",
    "\n",
    "test_importance = permutation_importance(\n",
    "    svr, test[predictors], test['share'], random_state = 0, n_repeats = 42, n_jobs = 2\n",
    ")\n",
    "\n",
    "sorted_importance = train_importance.importances_mean.argsort()\n",
    "train_importances = pd.DataFrame(\n",
    "    train_importance.importances[sorted_importance].T, \n",
    "    columns = train[predictors].columns[sorted_importance]\n",
    ")\n",
    "\n",
    "test_importances = pd.DataFrame(\n",
    "    test_importance.importances[sorted_importance].T, \n",
    "    columns = train[predictors].columns[sorted_importance]\n",
    ")\n",
    "\n",
    "\n",
    "for name, importances in zip(['train', 'test'],[train_importances, test_importances]):\n",
    "    labels = importances.columns\n",
    "    plt.figure(figsize = (15,13))\n",
    "    plt.boxplot(importances, vert=False, whis =10, label = labels.any())\n",
    "    #can't seem to obtain the  column names for the permutation importance\n",
    "    #using labels.all() or importances.columns raises a ValueError\n",
    "    plt.title(f\"Permutation Importance ({name}) set\")\n",
    "    plt.xlabel(\"Decrease in accuracy score\")\n",
    "    plt.axvline(x=0, color = \"k\", linestyle = \"--\")\n",
    "    plt.show()\n",
    "    #ax = importances.plot.box(vert = False, whis=10)\n",
    "    #ax.set_title(f\"Permutation Importance ({name} set)\")\n",
    "    #ax.set_xlabel(\"Decrease in accuracy score\")\n",
    "    #ax.axvline(x=0, color =\"k\", linestyle = \"--\")\n",
    "    #ax.figure.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "253b703b-e43e-48f8-ae99-a1aedfcdcec6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'importances_mean': array([ 4.32201786e-05,  1.38809083e-03,  3.59081481e-04, -2.15133709e-03,\n",
       "        -2.13239582e-03,  1.11926725e+00,  8.91460678e-02,  2.38910944e-01,\n",
       "         1.57208050e-04, -7.08211368e-05,  6.67321482e-04,  9.41343273e-06,\n",
       "         5.48043684e-02,  1.42227660e-01,  1.13105811e-04,  1.01173296e-04,\n",
       "         7.28353690e-02,  2.39394400e-01,  9.45182814e-05,  2.88717350e-02,\n",
       "         6.22593703e-02, -3.54143003e-03,  7.93386463e-02,  8.87399005e-04,\n",
       "         2.76056352e-03,  6.63915391e-02,  9.32132190e-03,  6.75715213e-01,\n",
       "         2.54539320e-03,  3.59678808e-03,  3.03166211e-04, -8.85706221e-04,\n",
       "         5.24712963e-04,  1.71630591e-05, -4.51383884e-05,  1.68315503e-04,\n",
       "         2.93115134e-03,  5.54779234e-03,  1.85737235e-03,  1.26679009e-07,\n",
       "         1.07153386e-07, -4.27852696e-08, -4.37343236e-06,  4.96178567e-04,\n",
       "         3.40428958e-08]),\n",
       " 'importances_std': array([4.32011400e-05, 2.86267838e-04, 1.64286842e-04, 1.08871806e-03,\n",
       "        1.74070399e-04, 1.50974441e-02, 3.17039456e-03, 4.70979837e-03,\n",
       "        1.84098716e-05, 5.88853831e-05, 1.78336180e-04, 5.56207229e-06,\n",
       "        2.82225993e-03, 3.63242330e-03, 1.82526094e-05, 1.48320319e-05,\n",
       "        2.47375326e-03, 4.85960004e-03, 1.62065414e-05, 1.30171928e-03,\n",
       "        3.06712259e-03, 6.74236141e-04, 2.93536929e-03, 8.17993747e-04,\n",
       "        1.03518677e-03, 2.19516021e-03, 4.99806742e-04, 9.52767546e-03,\n",
       "        2.39729204e-04, 2.96695260e-04, 5.52458330e-04, 5.32112871e-04,\n",
       "        5.53712225e-05, 6.50081933e-06, 5.27947474e-06, 4.40412390e-05,\n",
       "        3.26866893e-04, 3.80650574e-04, 3.29594916e-04, 2.51906214e-07,\n",
       "        4.91902684e-08, 1.25007817e-08, 2.87583556e-05, 2.02681519e-04,\n",
       "        1.19312300e-07]),\n",
       " 'importances': array([[ 5.34246583e-05,  1.06124796e-04,  4.47700584e-05, ...,\n",
       "          3.61744267e-05, -3.64593051e-06,  6.11230661e-05],\n",
       "        [ 1.44917749e-03,  1.05279475e-03,  1.80987199e-03, ...,\n",
       "          1.43092078e-03,  1.23103964e-03,  1.73487492e-03],\n",
       "        [ 1.41686126e-04,  2.98145303e-04,  1.90219850e-04, ...,\n",
       "          2.37950500e-04,  5.28293631e-04,  3.19867980e-04],\n",
       "        ...,\n",
       "        [-3.08421585e-05, -1.19646639e-05, -2.33087387e-05, ...,\n",
       "          9.07834836e-06, -1.35880812e-05, -5.42621239e-05],\n",
       "        [ 4.68126496e-04,  2.96285688e-04,  4.89936605e-04, ...,\n",
       "          5.30272525e-04,  8.62623434e-04,  4.72737054e-04],\n",
       "        [ 1.47964667e-07, -9.19277504e-08, -2.02355759e-08, ...,\n",
       "          1.01305061e-07,  2.18916709e-08, -1.61674343e-07]])}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4b248820-3b83-4ef7-9ba1-59bdc6c714e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trb</th>\n",
       "      <th>g</th>\n",
       "      <th>gs</th>\n",
       "      <th>pl</th>\n",
       "      <th>x3p</th>\n",
       "      <th>srs</th>\n",
       "      <th>tov_percent</th>\n",
       "      <th>ts_percent</th>\n",
       "      <th>ft_fga</th>\n",
       "      <th>x3p_ar</th>\n",
       "      <th>...</th>\n",
       "      <th>drb</th>\n",
       "      <th>tov</th>\n",
       "      <th>ft</th>\n",
       "      <th>ast</th>\n",
       "      <th>fg</th>\n",
       "      <th>x2pa</th>\n",
       "      <th>fga</th>\n",
       "      <th>fta</th>\n",
       "      <th>pts</th>\n",
       "      <th>mp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.004407</td>\n",
       "      <td>-0.002424</td>\n",
       "      <td>-0.002191</td>\n",
       "      <td>-0.002154</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000057</td>\n",
       "      <td>-3.084216e-05</td>\n",
       "      <td>-6.387600e-08</td>\n",
       "      <td>1.479647e-07</td>\n",
       "      <td>1.528023e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059032</td>\n",
       "      <td>0.066049</td>\n",
       "      <td>0.072738</td>\n",
       "      <td>0.081890</td>\n",
       "      <td>0.088949</td>\n",
       "      <td>0.142912</td>\n",
       "      <td>0.237568</td>\n",
       "      <td>0.239427</td>\n",
       "      <td>0.674960</td>\n",
       "      <td>1.121591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.002906</td>\n",
       "      <td>-0.000961</td>\n",
       "      <td>-0.001986</td>\n",
       "      <td>-0.000976</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>-1.196466e-05</td>\n",
       "      <td>-4.150120e-08</td>\n",
       "      <td>-9.192775e-08</td>\n",
       "      <td>1.654627e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066523</td>\n",
       "      <td>0.065251</td>\n",
       "      <td>0.073863</td>\n",
       "      <td>0.079977</td>\n",
       "      <td>0.094673</td>\n",
       "      <td>0.141861</td>\n",
       "      <td>0.236960</td>\n",
       "      <td>0.239861</td>\n",
       "      <td>0.695771</td>\n",
       "      <td>1.119493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.004140</td>\n",
       "      <td>-0.004694</td>\n",
       "      <td>-0.002236</td>\n",
       "      <td>-0.000498</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-2.330874e-05</td>\n",
       "      <td>-5.849698e-08</td>\n",
       "      <td>-2.023558e-08</td>\n",
       "      <td>1.386966e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059300</td>\n",
       "      <td>0.067605</td>\n",
       "      <td>0.074979</td>\n",
       "      <td>0.079400</td>\n",
       "      <td>0.088215</td>\n",
       "      <td>0.144105</td>\n",
       "      <td>0.244252</td>\n",
       "      <td>0.238607</td>\n",
       "      <td>0.679539</td>\n",
       "      <td>1.139046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.003269</td>\n",
       "      <td>-0.001624</td>\n",
       "      <td>-0.002050</td>\n",
       "      <td>-0.000763</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>-3.371681e-05</td>\n",
       "      <td>-4.595353e-08</td>\n",
       "      <td>6.630952e-09</td>\n",
       "      <td>1.129371e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064307</td>\n",
       "      <td>0.066415</td>\n",
       "      <td>0.070550</td>\n",
       "      <td>0.079850</td>\n",
       "      <td>0.089716</td>\n",
       "      <td>0.141138</td>\n",
       "      <td>0.237994</td>\n",
       "      <td>0.242508</td>\n",
       "      <td>0.676736</td>\n",
       "      <td>1.124524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.003386</td>\n",
       "      <td>-0.002440</td>\n",
       "      <td>-0.002322</td>\n",
       "      <td>-0.000969</td>\n",
       "      <td>-0.000112</td>\n",
       "      <td>-0.000050</td>\n",
       "      <td>1.288092e-05</td>\n",
       "      <td>-4.532003e-08</td>\n",
       "      <td>7.421866e-08</td>\n",
       "      <td>1.204109e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>0.069292</td>\n",
       "      <td>0.075567</td>\n",
       "      <td>0.073915</td>\n",
       "      <td>0.086814</td>\n",
       "      <td>0.147016</td>\n",
       "      <td>0.247201</td>\n",
       "      <td>0.236640</td>\n",
       "      <td>0.677323</td>\n",
       "      <td>1.142930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.003737</td>\n",
       "      <td>-0.002171</td>\n",
       "      <td>-0.002116</td>\n",
       "      <td>-0.000117</td>\n",
       "      <td>-0.000087</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-3.197952e-05</td>\n",
       "      <td>-2.456329e-08</td>\n",
       "      <td>-1.501305e-07</td>\n",
       "      <td>1.112375e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060945</td>\n",
       "      <td>0.067591</td>\n",
       "      <td>0.072909</td>\n",
       "      <td>0.076738</td>\n",
       "      <td>0.089630</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.235373</td>\n",
       "      <td>0.239570</td>\n",
       "      <td>0.672708</td>\n",
       "      <td>1.118997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.002990</td>\n",
       "      <td>-0.000523</td>\n",
       "      <td>-0.002002</td>\n",
       "      <td>-0.001259</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>4.295216e-06</td>\n",
       "      <td>-4.906512e-08</td>\n",
       "      <td>1.024654e-07</td>\n",
       "      <td>1.069692e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066184</td>\n",
       "      <td>0.063413</td>\n",
       "      <td>0.070446</td>\n",
       "      <td>0.082497</td>\n",
       "      <td>0.094040</td>\n",
       "      <td>0.137989</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>0.244641</td>\n",
       "      <td>0.690309</td>\n",
       "      <td>1.109096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.003927</td>\n",
       "      <td>-0.002943</td>\n",
       "      <td>-0.002248</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>3.680670e-05</td>\n",
       "      <td>-2.014373e-08</td>\n",
       "      <td>8.283051e-08</td>\n",
       "      <td>1.075540e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060340</td>\n",
       "      <td>0.067221</td>\n",
       "      <td>0.072192</td>\n",
       "      <td>0.081079</td>\n",
       "      <td>0.087191</td>\n",
       "      <td>0.141567</td>\n",
       "      <td>0.238255</td>\n",
       "      <td>0.233637</td>\n",
       "      <td>0.667799</td>\n",
       "      <td>1.125712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.004231</td>\n",
       "      <td>-0.003288</td>\n",
       "      <td>-0.002291</td>\n",
       "      <td>-0.000778</td>\n",
       "      <td>-0.000063</td>\n",
       "      <td>-0.000047</td>\n",
       "      <td>1.771102e-05</td>\n",
       "      <td>-4.827286e-08</td>\n",
       "      <td>-8.780145e-09</td>\n",
       "      <td>2.046685e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059726</td>\n",
       "      <td>0.069494</td>\n",
       "      <td>0.075523</td>\n",
       "      <td>0.074144</td>\n",
       "      <td>0.083615</td>\n",
       "      <td>0.148449</td>\n",
       "      <td>0.245707</td>\n",
       "      <td>0.233719</td>\n",
       "      <td>0.663117</td>\n",
       "      <td>1.134115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.003372</td>\n",
       "      <td>-0.002624</td>\n",
       "      <td>-0.002220</td>\n",
       "      <td>-0.000933</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>5.088562e-07</td>\n",
       "      <td>-5.746523e-08</td>\n",
       "      <td>1.196683e-07</td>\n",
       "      <td>1.161387e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062992</td>\n",
       "      <td>0.067204</td>\n",
       "      <td>0.073924</td>\n",
       "      <td>0.077188</td>\n",
       "      <td>0.087392</td>\n",
       "      <td>0.142798</td>\n",
       "      <td>0.239641</td>\n",
       "      <td>0.234248</td>\n",
       "      <td>0.664902</td>\n",
       "      <td>1.112501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.002843</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>-0.002141</td>\n",
       "      <td>-0.000868</td>\n",
       "      <td>-0.000116</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>-1.089358e-05</td>\n",
       "      <td>-3.667534e-08</td>\n",
       "      <td>7.041264e-08</td>\n",
       "      <td>4.305631e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064752</td>\n",
       "      <td>0.066901</td>\n",
       "      <td>0.074343</td>\n",
       "      <td>0.077114</td>\n",
       "      <td>0.089580</td>\n",
       "      <td>0.141458</td>\n",
       "      <td>0.239216</td>\n",
       "      <td>0.241190</td>\n",
       "      <td>0.677586</td>\n",
       "      <td>1.112018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.003982</td>\n",
       "      <td>-0.002129</td>\n",
       "      <td>-0.002472</td>\n",
       "      <td>-0.001832</td>\n",
       "      <td>-0.000123</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>-1.845405e-05</td>\n",
       "      <td>-3.742842e-08</td>\n",
       "      <td>9.781160e-08</td>\n",
       "      <td>6.746401e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061030</td>\n",
       "      <td>0.068117</td>\n",
       "      <td>0.074722</td>\n",
       "      <td>0.076725</td>\n",
       "      <td>0.088317</td>\n",
       "      <td>0.146601</td>\n",
       "      <td>0.247341</td>\n",
       "      <td>0.239235</td>\n",
       "      <td>0.681313</td>\n",
       "      <td>1.152863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.004014</td>\n",
       "      <td>-0.003073</td>\n",
       "      <td>-0.002103</td>\n",
       "      <td>-0.001373</td>\n",
       "      <td>-0.000080</td>\n",
       "      <td>-0.000050</td>\n",
       "      <td>-8.984191e-06</td>\n",
       "      <td>-5.301368e-08</td>\n",
       "      <td>1.574417e-07</td>\n",
       "      <td>6.400890e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059729</td>\n",
       "      <td>0.067710</td>\n",
       "      <td>0.073644</td>\n",
       "      <td>0.079040</td>\n",
       "      <td>0.087969</td>\n",
       "      <td>0.143766</td>\n",
       "      <td>0.241733</td>\n",
       "      <td>0.239004</td>\n",
       "      <td>0.673919</td>\n",
       "      <td>1.122819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.004194</td>\n",
       "      <td>-0.003120</td>\n",
       "      <td>-0.002451</td>\n",
       "      <td>-0.001369</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>-0.000049</td>\n",
       "      <td>3.005552e-05</td>\n",
       "      <td>-4.743699e-08</td>\n",
       "      <td>1.308537e-07</td>\n",
       "      <td>1.413947e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059842</td>\n",
       "      <td>0.068414</td>\n",
       "      <td>0.078001</td>\n",
       "      <td>0.075549</td>\n",
       "      <td>0.083290</td>\n",
       "      <td>0.148597</td>\n",
       "      <td>0.246173</td>\n",
       "      <td>0.229568</td>\n",
       "      <td>0.660404</td>\n",
       "      <td>1.133607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.004068</td>\n",
       "      <td>-0.001790</td>\n",
       "      <td>-0.002217</td>\n",
       "      <td>-0.000514</td>\n",
       "      <td>-0.000078</td>\n",
       "      <td>-0.000039</td>\n",
       "      <td>-2.838480e-05</td>\n",
       "      <td>-3.653353e-08</td>\n",
       "      <td>6.880962e-08</td>\n",
       "      <td>4.669906e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058583</td>\n",
       "      <td>0.064905</td>\n",
       "      <td>0.071860</td>\n",
       "      <td>0.077433</td>\n",
       "      <td>0.088760</td>\n",
       "      <td>0.140567</td>\n",
       "      <td>0.236763</td>\n",
       "      <td>0.238664</td>\n",
       "      <td>0.672664</td>\n",
       "      <td>1.115294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.002942</td>\n",
       "      <td>-0.002219</td>\n",
       "      <td>-0.002226</td>\n",
       "      <td>-0.000944</td>\n",
       "      <td>-0.000065</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>3.015799e-05</td>\n",
       "      <td>-3.043198e-08</td>\n",
       "      <td>2.330999e-07</td>\n",
       "      <td>1.957479e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065478</td>\n",
       "      <td>0.066998</td>\n",
       "      <td>0.072088</td>\n",
       "      <td>0.075222</td>\n",
       "      <td>0.089464</td>\n",
       "      <td>0.142409</td>\n",
       "      <td>0.238462</td>\n",
       "      <td>0.241167</td>\n",
       "      <td>0.675687</td>\n",
       "      <td>1.118188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.005283</td>\n",
       "      <td>-0.003204</td>\n",
       "      <td>-0.002553</td>\n",
       "      <td>-0.000571</td>\n",
       "      <td>-0.000075</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-3.426144e-05</td>\n",
       "      <td>-5.518300e-08</td>\n",
       "      <td>-1.943246e-07</td>\n",
       "      <td>2.001463e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054032</td>\n",
       "      <td>0.067274</td>\n",
       "      <td>0.074415</td>\n",
       "      <td>0.080369</td>\n",
       "      <td>0.084062</td>\n",
       "      <td>0.145299</td>\n",
       "      <td>0.241358</td>\n",
       "      <td>0.230065</td>\n",
       "      <td>0.657347</td>\n",
       "      <td>1.123285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.003687</td>\n",
       "      <td>-0.001656</td>\n",
       "      <td>-0.001962</td>\n",
       "      <td>-0.000604</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-2.307940e-05</td>\n",
       "      <td>-2.447523e-08</td>\n",
       "      <td>-1.252657e-07</td>\n",
       "      <td>1.271588e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062078</td>\n",
       "      <td>0.066781</td>\n",
       "      <td>0.073656</td>\n",
       "      <td>0.082105</td>\n",
       "      <td>0.089511</td>\n",
       "      <td>0.143534</td>\n",
       "      <td>0.237962</td>\n",
       "      <td>0.240250</td>\n",
       "      <td>0.678694</td>\n",
       "      <td>1.120082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.003961</td>\n",
       "      <td>-0.002601</td>\n",
       "      <td>-0.002261</td>\n",
       "      <td>-0.001087</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-4.949141e-05</td>\n",
       "      <td>-3.853923e-08</td>\n",
       "      <td>-6.095328e-09</td>\n",
       "      <td>1.123940e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061237</td>\n",
       "      <td>0.067016</td>\n",
       "      <td>0.074477</td>\n",
       "      <td>0.082087</td>\n",
       "      <td>0.087757</td>\n",
       "      <td>0.144494</td>\n",
       "      <td>0.239600</td>\n",
       "      <td>0.238860</td>\n",
       "      <td>0.674386</td>\n",
       "      <td>1.116119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.004351</td>\n",
       "      <td>-0.001863</td>\n",
       "      <td>-0.002229</td>\n",
       "      <td>-0.000840</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>4.892949e-06</td>\n",
       "      <td>-4.625499e-08</td>\n",
       "      <td>8.117369e-08</td>\n",
       "      <td>5.977706e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057544</td>\n",
       "      <td>0.067065</td>\n",
       "      <td>0.073583</td>\n",
       "      <td>0.075656</td>\n",
       "      <td>0.085235</td>\n",
       "      <td>0.140983</td>\n",
       "      <td>0.233474</td>\n",
       "      <td>0.230611</td>\n",
       "      <td>0.654600</td>\n",
       "      <td>1.086482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.003003</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.002152</td>\n",
       "      <td>-0.001470</td>\n",
       "      <td>-0.000184</td>\n",
       "      <td>-0.000049</td>\n",
       "      <td>-3.869052e-05</td>\n",
       "      <td>-5.845776e-08</td>\n",
       "      <td>-3.660324e-09</td>\n",
       "      <td>1.009811e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063399</td>\n",
       "      <td>0.065624</td>\n",
       "      <td>0.072961</td>\n",
       "      <td>0.077757</td>\n",
       "      <td>0.089400</td>\n",
       "      <td>0.140096</td>\n",
       "      <td>0.240186</td>\n",
       "      <td>0.241006</td>\n",
       "      <td>0.675398</td>\n",
       "      <td>1.125013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.004153</td>\n",
       "      <td>-0.003859</td>\n",
       "      <td>-0.002247</td>\n",
       "      <td>-0.001198</td>\n",
       "      <td>-0.000059</td>\n",
       "      <td>-0.000047</td>\n",
       "      <td>3.824006e-05</td>\n",
       "      <td>-5.656236e-08</td>\n",
       "      <td>2.679822e-07</td>\n",
       "      <td>1.338426e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059275</td>\n",
       "      <td>0.071306</td>\n",
       "      <td>0.076859</td>\n",
       "      <td>0.075235</td>\n",
       "      <td>0.082185</td>\n",
       "      <td>0.150913</td>\n",
       "      <td>0.248380</td>\n",
       "      <td>0.232137</td>\n",
       "      <td>0.661277</td>\n",
       "      <td>1.131535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.002045</td>\n",
       "      <td>-0.001324</td>\n",
       "      <td>-0.001950</td>\n",
       "      <td>-0.001075</td>\n",
       "      <td>-0.000152</td>\n",
       "      <td>-0.000047</td>\n",
       "      <td>-3.693688e-05</td>\n",
       "      <td>-6.151187e-08</td>\n",
       "      <td>-2.936780e-07</td>\n",
       "      <td>1.623283e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068430</td>\n",
       "      <td>0.063129</td>\n",
       "      <td>0.068033</td>\n",
       "      <td>0.078296</td>\n",
       "      <td>0.090039</td>\n",
       "      <td>0.136957</td>\n",
       "      <td>0.234778</td>\n",
       "      <td>0.244665</td>\n",
       "      <td>0.672631</td>\n",
       "      <td>1.101457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.002244</td>\n",
       "      <td>-0.000342</td>\n",
       "      <td>-0.001889</td>\n",
       "      <td>-0.000711</td>\n",
       "      <td>-0.000093</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>2.966644e-05</td>\n",
       "      <td>-2.579094e-08</td>\n",
       "      <td>6.429422e-09</td>\n",
       "      <td>1.022055e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067169</td>\n",
       "      <td>0.061793</td>\n",
       "      <td>0.068202</td>\n",
       "      <td>0.081389</td>\n",
       "      <td>0.091773</td>\n",
       "      <td>0.136178</td>\n",
       "      <td>0.231390</td>\n",
       "      <td>0.242969</td>\n",
       "      <td>0.676077</td>\n",
       "      <td>1.089535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.002425</td>\n",
       "      <td>-0.001178</td>\n",
       "      <td>-0.001863</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>-0.000141</td>\n",
       "      <td>-0.000036</td>\n",
       "      <td>3.247888e-05</td>\n",
       "      <td>-3.303631e-08</td>\n",
       "      <td>1.673753e-07</td>\n",
       "      <td>6.340325e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067140</td>\n",
       "      <td>0.063233</td>\n",
       "      <td>0.067634</td>\n",
       "      <td>0.081855</td>\n",
       "      <td>0.095408</td>\n",
       "      <td>0.134251</td>\n",
       "      <td>0.232907</td>\n",
       "      <td>0.251594</td>\n",
       "      <td>0.692685</td>\n",
       "      <td>1.110526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.002870</td>\n",
       "      <td>-0.002573</td>\n",
       "      <td>-0.002039</td>\n",
       "      <td>-0.001384</td>\n",
       "      <td>-0.000075</td>\n",
       "      <td>-0.000050</td>\n",
       "      <td>4.953882e-05</td>\n",
       "      <td>-4.773799e-08</td>\n",
       "      <td>1.038036e-07</td>\n",
       "      <td>8.936934e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063880</td>\n",
       "      <td>0.066003</td>\n",
       "      <td>0.072386</td>\n",
       "      <td>0.080517</td>\n",
       "      <td>0.090568</td>\n",
       "      <td>0.141304</td>\n",
       "      <td>0.237755</td>\n",
       "      <td>0.243316</td>\n",
       "      <td>0.679115</td>\n",
       "      <td>1.107811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.003560</td>\n",
       "      <td>-0.003047</td>\n",
       "      <td>-0.002191</td>\n",
       "      <td>-0.001371</td>\n",
       "      <td>-0.000168</td>\n",
       "      <td>-0.000050</td>\n",
       "      <td>-1.315154e-05</td>\n",
       "      <td>-3.681050e-08</td>\n",
       "      <td>-7.120643e-09</td>\n",
       "      <td>9.335270e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062825</td>\n",
       "      <td>0.067288</td>\n",
       "      <td>0.071963</td>\n",
       "      <td>0.078363</td>\n",
       "      <td>0.090179</td>\n",
       "      <td>0.141648</td>\n",
       "      <td>0.241692</td>\n",
       "      <td>0.243423</td>\n",
       "      <td>0.681066</td>\n",
       "      <td>1.133357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.004080</td>\n",
       "      <td>-0.003857</td>\n",
       "      <td>-0.002263</td>\n",
       "      <td>-0.001638</td>\n",
       "      <td>-0.000115</td>\n",
       "      <td>-0.000050</td>\n",
       "      <td>-3.293300e-05</td>\n",
       "      <td>-4.782071e-08</td>\n",
       "      <td>1.713029e-07</td>\n",
       "      <td>9.648081e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060889</td>\n",
       "      <td>0.071001</td>\n",
       "      <td>0.076277</td>\n",
       "      <td>0.075491</td>\n",
       "      <td>0.087569</td>\n",
       "      <td>0.146077</td>\n",
       "      <td>0.246068</td>\n",
       "      <td>0.236435</td>\n",
       "      <td>0.678037</td>\n",
       "      <td>1.148487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.003468</td>\n",
       "      <td>-0.002144</td>\n",
       "      <td>-0.002159</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>-0.000059</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>6.556369e-05</td>\n",
       "      <td>-4.382299e-08</td>\n",
       "      <td>1.466859e-07</td>\n",
       "      <td>8.390359e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061947</td>\n",
       "      <td>0.064683</td>\n",
       "      <td>0.070318</td>\n",
       "      <td>0.081636</td>\n",
       "      <td>0.088828</td>\n",
       "      <td>0.141891</td>\n",
       "      <td>0.237307</td>\n",
       "      <td>0.241313</td>\n",
       "      <td>0.672608</td>\n",
       "      <td>1.109847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.002810</td>\n",
       "      <td>-0.002112</td>\n",
       "      <td>-0.001933</td>\n",
       "      <td>-0.000850</td>\n",
       "      <td>-0.000069</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>3.726784e-05</td>\n",
       "      <td>-4.459314e-08</td>\n",
       "      <td>1.137129e-07</td>\n",
       "      <td>3.448636e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063857</td>\n",
       "      <td>0.064692</td>\n",
       "      <td>0.071785</td>\n",
       "      <td>0.079580</td>\n",
       "      <td>0.090371</td>\n",
       "      <td>0.137989</td>\n",
       "      <td>0.232973</td>\n",
       "      <td>0.238554</td>\n",
       "      <td>0.671882</td>\n",
       "      <td>1.096119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.004187</td>\n",
       "      <td>-0.001835</td>\n",
       "      <td>-0.002173</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>-0.000032</td>\n",
       "      <td>-0.000041</td>\n",
       "      <td>2.065194e-05</td>\n",
       "      <td>-3.178056e-08</td>\n",
       "      <td>7.826814e-08</td>\n",
       "      <td>9.031938e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060272</td>\n",
       "      <td>0.065872</td>\n",
       "      <td>0.072981</td>\n",
       "      <td>0.083639</td>\n",
       "      <td>0.091400</td>\n",
       "      <td>0.141484</td>\n",
       "      <td>0.236578</td>\n",
       "      <td>0.239455</td>\n",
       "      <td>0.682156</td>\n",
       "      <td>1.122371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.002227</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>-0.001710</td>\n",
       "      <td>-0.001434</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>-0.000049</td>\n",
       "      <td>-1.699329e-05</td>\n",
       "      <td>-2.799935e-08</td>\n",
       "      <td>2.291215e-08</td>\n",
       "      <td>6.974396e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067983</td>\n",
       "      <td>0.060768</td>\n",
       "      <td>0.066113</td>\n",
       "      <td>0.083096</td>\n",
       "      <td>0.096538</td>\n",
       "      <td>0.132584</td>\n",
       "      <td>0.224527</td>\n",
       "      <td>0.250582</td>\n",
       "      <td>0.688666</td>\n",
       "      <td>1.076518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.003231</td>\n",
       "      <td>-0.001328</td>\n",
       "      <td>-0.002176</td>\n",
       "      <td>-0.000631</td>\n",
       "      <td>-0.000171</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>-1.316822e-05</td>\n",
       "      <td>-2.388464e-08</td>\n",
       "      <td>-3.832541e-08</td>\n",
       "      <td>4.328829e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063691</td>\n",
       "      <td>0.067340</td>\n",
       "      <td>0.073616</td>\n",
       "      <td>0.075742</td>\n",
       "      <td>0.088056</td>\n",
       "      <td>0.140705</td>\n",
       "      <td>0.239009</td>\n",
       "      <td>0.237952</td>\n",
       "      <td>0.668634</td>\n",
       "      <td>1.118773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.004410</td>\n",
       "      <td>-0.003010</td>\n",
       "      <td>-0.002201</td>\n",
       "      <td>-0.000167</td>\n",
       "      <td>-0.000121</td>\n",
       "      <td>-0.000034</td>\n",
       "      <td>1.093335e-05</td>\n",
       "      <td>-3.028367e-08</td>\n",
       "      <td>4.701351e-08</td>\n",
       "      <td>1.346862e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056849</td>\n",
       "      <td>0.068224</td>\n",
       "      <td>0.072705</td>\n",
       "      <td>0.077387</td>\n",
       "      <td>0.084118</td>\n",
       "      <td>0.143788</td>\n",
       "      <td>0.240897</td>\n",
       "      <td>0.236247</td>\n",
       "      <td>0.660015</td>\n",
       "      <td>1.122888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.003060</td>\n",
       "      <td>-0.000700</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>-0.000363</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-1.479228e-05</td>\n",
       "      <td>-4.338886e-08</td>\n",
       "      <td>7.460415e-08</td>\n",
       "      <td>1.283445e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064902</td>\n",
       "      <td>0.063731</td>\n",
       "      <td>0.069925</td>\n",
       "      <td>0.084244</td>\n",
       "      <td>0.092598</td>\n",
       "      <td>0.141445</td>\n",
       "      <td>0.237405</td>\n",
       "      <td>0.248362</td>\n",
       "      <td>0.689245</td>\n",
       "      <td>1.111721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.003347</td>\n",
       "      <td>-0.001562</td>\n",
       "      <td>-0.001975</td>\n",
       "      <td>-0.001031</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>-0.000047</td>\n",
       "      <td>-1.201425e-05</td>\n",
       "      <td>-6.579619e-08</td>\n",
       "      <td>1.388626e-07</td>\n",
       "      <td>1.412525e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062966</td>\n",
       "      <td>0.063835</td>\n",
       "      <td>0.073288</td>\n",
       "      <td>0.085749</td>\n",
       "      <td>0.091234</td>\n",
       "      <td>0.143378</td>\n",
       "      <td>0.238223</td>\n",
       "      <td>0.240469</td>\n",
       "      <td>0.684960</td>\n",
       "      <td>1.119522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-0.003683</td>\n",
       "      <td>-0.002486</td>\n",
       "      <td>-0.002270</td>\n",
       "      <td>-0.000868</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>-3.074803e-05</td>\n",
       "      <td>-6.108259e-08</td>\n",
       "      <td>-2.047567e-07</td>\n",
       "      <td>2.112359e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059880</td>\n",
       "      <td>0.067323</td>\n",
       "      <td>0.073608</td>\n",
       "      <td>0.079350</td>\n",
       "      <td>0.086188</td>\n",
       "      <td>0.143620</td>\n",
       "      <td>0.240960</td>\n",
       "      <td>0.237645</td>\n",
       "      <td>0.665540</td>\n",
       "      <td>1.121249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.003673</td>\n",
       "      <td>-0.001980</td>\n",
       "      <td>-0.002055</td>\n",
       "      <td>-0.000734</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-3.662157e-05</td>\n",
       "      <td>-4.852194e-08</td>\n",
       "      <td>-6.811819e-08</td>\n",
       "      <td>1.761962e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062701</td>\n",
       "      <td>0.065210</td>\n",
       "      <td>0.072478</td>\n",
       "      <td>0.083276</td>\n",
       "      <td>0.090938</td>\n",
       "      <td>0.143992</td>\n",
       "      <td>0.237625</td>\n",
       "      <td>0.242038</td>\n",
       "      <td>0.683586</td>\n",
       "      <td>1.110480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-0.003626</td>\n",
       "      <td>-0.002623</td>\n",
       "      <td>-0.001886</td>\n",
       "      <td>-0.000698</td>\n",
       "      <td>-0.000031</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>4.847859e-06</td>\n",
       "      <td>-2.359900e-08</td>\n",
       "      <td>-3.163600e-08</td>\n",
       "      <td>7.712480e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061323</td>\n",
       "      <td>0.065298</td>\n",
       "      <td>0.072062</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>0.091163</td>\n",
       "      <td>0.142130</td>\n",
       "      <td>0.237848</td>\n",
       "      <td>0.240846</td>\n",
       "      <td>0.681279</td>\n",
       "      <td>1.114344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.003450</td>\n",
       "      <td>-0.003205</td>\n",
       "      <td>-0.002193</td>\n",
       "      <td>-0.000544</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>9.078348e-06</td>\n",
       "      <td>-4.869565e-08</td>\n",
       "      <td>1.013051e-07</td>\n",
       "      <td>1.033863e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063321</td>\n",
       "      <td>0.067385</td>\n",
       "      <td>0.073637</td>\n",
       "      <td>0.078948</td>\n",
       "      <td>0.088692</td>\n",
       "      <td>0.143470</td>\n",
       "      <td>0.241186</td>\n",
       "      <td>0.236738</td>\n",
       "      <td>0.676802</td>\n",
       "      <td>1.133796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.003515</td>\n",
       "      <td>-0.002077</td>\n",
       "      <td>-0.001969</td>\n",
       "      <td>-0.001903</td>\n",
       "      <td>-0.000147</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>-1.358808e-05</td>\n",
       "      <td>-2.430181e-08</td>\n",
       "      <td>2.189167e-08</td>\n",
       "      <td>-1.307732e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.062650</td>\n",
       "      <td>0.065135</td>\n",
       "      <td>0.071769</td>\n",
       "      <td>0.079062</td>\n",
       "      <td>0.093822</td>\n",
       "      <td>0.138415</td>\n",
       "      <td>0.237416</td>\n",
       "      <td>0.244019</td>\n",
       "      <td>0.689775</td>\n",
       "      <td>1.123746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.003985</td>\n",
       "      <td>-0.002476</td>\n",
       "      <td>-0.002156</td>\n",
       "      <td>-0.000847</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-5.426212e-05</td>\n",
       "      <td>-5.087212e-08</td>\n",
       "      <td>-1.616743e-07</td>\n",
       "      <td>1.590367e-07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061763</td>\n",
       "      <td>0.068856</td>\n",
       "      <td>0.077005</td>\n",
       "      <td>0.081012</td>\n",
       "      <td>0.088891</td>\n",
       "      <td>0.146089</td>\n",
       "      <td>0.242318</td>\n",
       "      <td>0.233330</td>\n",
       "      <td>0.678841</td>\n",
       "      <td>1.131368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         trb         g        gs        pl       x3p       srs   tov_percent  \\\n",
       "0  -0.004407 -0.002424 -0.002191 -0.002154 -0.000008 -0.000057 -3.084216e-05   \n",
       "1  -0.002906 -0.000961 -0.001986 -0.000976  0.000024 -0.000045 -1.196466e-05   \n",
       "2  -0.004140 -0.004694 -0.002236 -0.000498 -0.000130 -0.000042 -2.330874e-05   \n",
       "3  -0.003269 -0.001624 -0.002050 -0.000763 -0.000089 -0.000044 -3.371681e-05   \n",
       "4  -0.003386 -0.002440 -0.002322 -0.000969 -0.000112 -0.000050  1.288092e-05   \n",
       "5  -0.003737 -0.002171 -0.002116 -0.000117 -0.000087 -0.000038 -3.197952e-05   \n",
       "6  -0.002990 -0.000523 -0.002002 -0.001259  0.000041 -0.000052  4.295216e-06   \n",
       "7  -0.003927 -0.002943 -0.002248  0.000324 -0.000048 -0.000030  3.680670e-05   \n",
       "8  -0.004231 -0.003288 -0.002291 -0.000778 -0.000063 -0.000047  1.771102e-05   \n",
       "9  -0.003372 -0.002624 -0.002220 -0.000933 -0.000118 -0.000043  5.088562e-07   \n",
       "10 -0.002843  0.000555 -0.002141 -0.000868 -0.000116 -0.000044 -1.089358e-05   \n",
       "11 -0.003982 -0.002129 -0.002472 -0.001832 -0.000123 -0.000056 -1.845405e-05   \n",
       "12 -0.004014 -0.003073 -0.002103 -0.001373 -0.000080 -0.000050 -8.984191e-06   \n",
       "13 -0.004194 -0.003120 -0.002451 -0.001369 -0.000083 -0.000049  3.005552e-05   \n",
       "14 -0.004068 -0.001790 -0.002217 -0.000514 -0.000078 -0.000039 -2.838480e-05   \n",
       "15 -0.002942 -0.002219 -0.002226 -0.000944 -0.000065 -0.000044  3.015799e-05   \n",
       "16 -0.005283 -0.003204 -0.002553 -0.000571 -0.000075 -0.000043 -3.426144e-05   \n",
       "17 -0.003687 -0.001656 -0.001962 -0.000604  0.000014 -0.000042 -2.307940e-05   \n",
       "18 -0.003961 -0.002601 -0.002261 -0.001087 -0.000011 -0.000046 -4.949141e-05   \n",
       "19 -0.004351 -0.001863 -0.002229 -0.000840 -0.000005 -0.000046  4.892949e-06   \n",
       "20 -0.003003 -0.002582 -0.002152 -0.001470 -0.000184 -0.000049 -3.869052e-05   \n",
       "21 -0.004153 -0.003859 -0.002247 -0.001198 -0.000059 -0.000047  3.824006e-05   \n",
       "22 -0.002045 -0.001324 -0.001950 -0.001075 -0.000152 -0.000047 -3.693688e-05   \n",
       "23 -0.002244 -0.000342 -0.001889 -0.000711 -0.000093 -0.000042  2.966644e-05   \n",
       "24 -0.002425 -0.001178 -0.001863  0.000012 -0.000141 -0.000036  3.247888e-05   \n",
       "25 -0.002870 -0.002573 -0.002039 -0.001384 -0.000075 -0.000050  4.953882e-05   \n",
       "26 -0.003560 -0.003047 -0.002191 -0.001371 -0.000168 -0.000050 -1.315154e-05   \n",
       "27 -0.004080 -0.003857 -0.002263 -0.001638 -0.000115 -0.000050 -3.293300e-05   \n",
       "28 -0.003468 -0.002144 -0.002159  0.000173 -0.000059 -0.000040  6.556369e-05   \n",
       "29 -0.002810 -0.002112 -0.001933 -0.000850 -0.000069 -0.000048  3.726784e-05   \n",
       "30 -0.004187 -0.001835 -0.002173 -0.000343 -0.000032 -0.000041  2.065194e-05   \n",
       "31 -0.002227  0.000735 -0.001710 -0.001434  0.000012 -0.000049 -1.699329e-05   \n",
       "32 -0.003231 -0.001328 -0.002176 -0.000631 -0.000171 -0.000044 -1.316822e-05   \n",
       "33 -0.004410 -0.003010 -0.002201 -0.000167 -0.000121 -0.000034  1.093335e-05   \n",
       "34 -0.003060 -0.000700 -0.001834 -0.000363 -0.000043 -0.000042 -1.479228e-05   \n",
       "35 -0.003347 -0.001562 -0.001975 -0.001031  0.000026 -0.000047 -1.201425e-05   \n",
       "36 -0.003683 -0.002486 -0.002270 -0.000868 -0.000107 -0.000042 -3.074803e-05   \n",
       "37 -0.003673 -0.001980 -0.002055 -0.000734  0.000043 -0.000048 -3.662157e-05   \n",
       "38 -0.003626 -0.002623 -0.001886 -0.000698 -0.000031 -0.000044  4.847859e-06   \n",
       "39 -0.003450 -0.003205 -0.002193 -0.000544 -0.000056 -0.000043  9.078348e-06   \n",
       "40 -0.003515 -0.002077 -0.001969 -0.001903 -0.000147 -0.000052 -1.358808e-05   \n",
       "41 -0.003985 -0.002476 -0.002156 -0.000847 -0.000021 -0.000043 -5.426212e-05   \n",
       "\n",
       "      ts_percent        ft_fga        x3p_ar  ...       drb       tov  \\\n",
       "0  -6.387600e-08  1.479647e-07  1.528023e-07  ...  0.059032  0.066049   \n",
       "1  -4.150120e-08 -9.192775e-08  1.654627e-07  ...  0.066523  0.065251   \n",
       "2  -5.849698e-08 -2.023558e-08  1.386966e-07  ...  0.059300  0.067605   \n",
       "3  -4.595353e-08  6.630952e-09  1.129371e-07  ...  0.064307  0.066415   \n",
       "4  -4.532003e-08  7.421866e-08  1.204109e-07  ...  0.064127  0.069292   \n",
       "5  -2.456329e-08 -1.501305e-07  1.112375e-07  ...  0.060945  0.067591   \n",
       "6  -4.906512e-08  1.024654e-07  1.069692e-07  ...  0.066184  0.063413   \n",
       "7  -2.014373e-08  8.283051e-08  1.075540e-07  ...  0.060340  0.067221   \n",
       "8  -4.827286e-08 -8.780145e-09  2.046685e-07  ...  0.059726  0.069494   \n",
       "9  -5.746523e-08  1.196683e-07  1.161387e-07  ...  0.062992  0.067204   \n",
       "10 -3.667534e-08  7.041264e-08  4.305631e-08  ...  0.064752  0.066901   \n",
       "11 -3.742842e-08  9.781160e-08  6.746401e-08  ...  0.061030  0.068117   \n",
       "12 -5.301368e-08  1.574417e-07  6.400890e-08  ...  0.059729  0.067710   \n",
       "13 -4.743699e-08  1.308537e-07  1.413947e-07  ...  0.059842  0.068414   \n",
       "14 -3.653353e-08  6.880962e-08  4.669906e-08  ...  0.058583  0.064905   \n",
       "15 -3.043198e-08  2.330999e-07  1.957479e-08  ...  0.065478  0.066998   \n",
       "16 -5.518300e-08 -1.943246e-07  2.001463e-07  ...  0.054032  0.067274   \n",
       "17 -2.447523e-08 -1.252657e-07  1.271588e-07  ...  0.062078  0.066781   \n",
       "18 -3.853923e-08 -6.095328e-09  1.123940e-07  ...  0.061237  0.067016   \n",
       "19 -4.625499e-08  8.117369e-08  5.977706e-08  ...  0.057544  0.067065   \n",
       "20 -5.845776e-08 -3.660324e-09  1.009811e-07  ...  0.063399  0.065624   \n",
       "21 -5.656236e-08  2.679822e-07  1.338426e-07  ...  0.059275  0.071306   \n",
       "22 -6.151187e-08 -2.936780e-07  1.623283e-07  ...  0.068430  0.063129   \n",
       "23 -2.579094e-08  6.429422e-09  1.022055e-07  ...  0.067169  0.061793   \n",
       "24 -3.303631e-08  1.673753e-07  6.340325e-08  ...  0.067140  0.063233   \n",
       "25 -4.773799e-08  1.038036e-07  8.936934e-08  ...  0.063880  0.066003   \n",
       "26 -3.681050e-08 -7.120643e-09  9.335270e-08  ...  0.062825  0.067288   \n",
       "27 -4.782071e-08  1.713029e-07  9.648081e-08  ...  0.060889  0.071001   \n",
       "28 -4.382299e-08  1.466859e-07  8.390359e-08  ...  0.061947  0.064683   \n",
       "29 -4.459314e-08  1.137129e-07  3.448636e-08  ...  0.063857  0.064692   \n",
       "30 -3.178056e-08  7.826814e-08  9.031938e-08  ...  0.060272  0.065872   \n",
       "31 -2.799935e-08  2.291215e-08  6.974396e-08  ...  0.067983  0.060768   \n",
       "32 -2.388464e-08 -3.832541e-08  4.328829e-08  ...  0.063691  0.067340   \n",
       "33 -3.028367e-08  4.701351e-08  1.346862e-07  ...  0.056849  0.068224   \n",
       "34 -4.338886e-08  7.460415e-08  1.283445e-07  ...  0.064902  0.063731   \n",
       "35 -6.579619e-08  1.388626e-07  1.412525e-07  ...  0.062966  0.063835   \n",
       "36 -6.108259e-08 -2.047567e-07  2.112359e-07  ...  0.059880  0.067323   \n",
       "37 -4.852194e-08 -6.811819e-08  1.761962e-07  ...  0.062701  0.065210   \n",
       "38 -2.359900e-08 -3.163600e-08  7.712480e-08  ...  0.061323  0.065298   \n",
       "39 -4.869565e-08  1.013051e-07  1.033863e-07  ...  0.063321  0.067385   \n",
       "40 -2.430181e-08  2.189167e-08 -1.307732e-08  ...  0.062650  0.065135   \n",
       "41 -5.087212e-08 -1.616743e-07  1.590367e-07  ...  0.061763  0.068856   \n",
       "\n",
       "          ft       ast        fg      x2pa       fga       fta       pts  \\\n",
       "0   0.072738  0.081890  0.088949  0.142912  0.237568  0.239427  0.674960   \n",
       "1   0.073863  0.079977  0.094673  0.141861  0.236960  0.239861  0.695771   \n",
       "2   0.074979  0.079400  0.088215  0.144105  0.244252  0.238607  0.679539   \n",
       "3   0.070550  0.079850  0.089716  0.141138  0.237994  0.242508  0.676736   \n",
       "4   0.075567  0.073915  0.086814  0.147016  0.247201  0.236640  0.677323   \n",
       "5   0.072909  0.076738  0.089630  0.139610  0.235373  0.239570  0.672708   \n",
       "6   0.070446  0.082497  0.094040  0.137989  0.231800  0.244641  0.690309   \n",
       "7   0.072192  0.081079  0.087191  0.141567  0.238255  0.233637  0.667799   \n",
       "8   0.075523  0.074144  0.083615  0.148449  0.245707  0.233719  0.663117   \n",
       "9   0.073924  0.077188  0.087392  0.142798  0.239641  0.234248  0.664902   \n",
       "10  0.074343  0.077114  0.089580  0.141458  0.239216  0.241190  0.677586   \n",
       "11  0.074722  0.076725  0.088317  0.146601  0.247341  0.239235  0.681313   \n",
       "12  0.073644  0.079040  0.087969  0.143766  0.241733  0.239004  0.673919   \n",
       "13  0.078001  0.075549  0.083290  0.148597  0.246173  0.229568  0.660404   \n",
       "14  0.071860  0.077433  0.088760  0.140567  0.236763  0.238664  0.672664   \n",
       "15  0.072088  0.075222  0.089464  0.142409  0.238462  0.241167  0.675687   \n",
       "16  0.074415  0.080369  0.084062  0.145299  0.241358  0.230065  0.657347   \n",
       "17  0.073656  0.082105  0.089511  0.143534  0.237962  0.240250  0.678694   \n",
       "18  0.074477  0.082087  0.087757  0.144494  0.239600  0.238860  0.674386   \n",
       "19  0.073583  0.075656  0.085235  0.140983  0.233474  0.230611  0.654600   \n",
       "20  0.072961  0.077757  0.089400  0.140096  0.240186  0.241006  0.675398   \n",
       "21  0.076859  0.075235  0.082185  0.150913  0.248380  0.232137  0.661277   \n",
       "22  0.068033  0.078296  0.090039  0.136957  0.234778  0.244665  0.672631   \n",
       "23  0.068202  0.081389  0.091773  0.136178  0.231390  0.242969  0.676077   \n",
       "24  0.067634  0.081855  0.095408  0.134251  0.232907  0.251594  0.692685   \n",
       "25  0.072386  0.080517  0.090568  0.141304  0.237755  0.243316  0.679115   \n",
       "26  0.071963  0.078363  0.090179  0.141648  0.241692  0.243423  0.681066   \n",
       "27  0.076277  0.075491  0.087569  0.146077  0.246068  0.236435  0.678037   \n",
       "28  0.070318  0.081636  0.088828  0.141891  0.237307  0.241313  0.672608   \n",
       "29  0.071785  0.079580  0.090371  0.137989  0.232973  0.238554  0.671882   \n",
       "30  0.072981  0.083639  0.091400  0.141484  0.236578  0.239455  0.682156   \n",
       "31  0.066113  0.083096  0.096538  0.132584  0.224527  0.250582  0.688666   \n",
       "32  0.073616  0.075742  0.088056  0.140705  0.239009  0.237952  0.668634   \n",
       "33  0.072705  0.077387  0.084118  0.143788  0.240897  0.236247  0.660015   \n",
       "34  0.069925  0.084244  0.092598  0.141445  0.237405  0.248362  0.689245   \n",
       "35  0.073288  0.085749  0.091234  0.143378  0.238223  0.240469  0.684960   \n",
       "36  0.073608  0.079350  0.086188  0.143620  0.240960  0.237645  0.665540   \n",
       "37  0.072478  0.083276  0.090938  0.143992  0.237625  0.242038  0.683586   \n",
       "38  0.072062  0.082621  0.091163  0.142130  0.237848  0.240846  0.681279   \n",
       "39  0.073637  0.078948  0.088692  0.143470  0.241186  0.236738  0.676802   \n",
       "40  0.071769  0.079062  0.093822  0.138415  0.237416  0.244019  0.689775   \n",
       "41  0.077005  0.081012  0.088891  0.146089  0.242318  0.233330  0.678841   \n",
       "\n",
       "          mp  \n",
       "0   1.121591  \n",
       "1   1.119493  \n",
       "2   1.139046  \n",
       "3   1.124524  \n",
       "4   1.142930  \n",
       "5   1.118997  \n",
       "6   1.109096  \n",
       "7   1.125712  \n",
       "8   1.134115  \n",
       "9   1.112501  \n",
       "10  1.112018  \n",
       "11  1.152863  \n",
       "12  1.122819  \n",
       "13  1.133607  \n",
       "14  1.115294  \n",
       "15  1.118188  \n",
       "16  1.123285  \n",
       "17  1.120082  \n",
       "18  1.116119  \n",
       "19  1.086482  \n",
       "20  1.125013  \n",
       "21  1.131535  \n",
       "22  1.101457  \n",
       "23  1.089535  \n",
       "24  1.110526  \n",
       "25  1.107811  \n",
       "26  1.133357  \n",
       "27  1.148487  \n",
       "28  1.109847  \n",
       "29  1.096119  \n",
       "30  1.122371  \n",
       "31  1.076518  \n",
       "32  1.118773  \n",
       "33  1.122888  \n",
       "34  1.111721  \n",
       "35  1.119522  \n",
       "36  1.121249  \n",
       "37  1.110480  \n",
       "38  1.114344  \n",
       "39  1.133796  \n",
       "40  1.123746  \n",
       "41  1.131368  \n",
       "\n",
       "[42 rows x 45 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_importances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a3b7a32e-237f-408f-b351-44c26eee1e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "#USING A MORE CLEARER APPROACH TO SHOW FEATURE IMPORTANCES\n",
    "test_num = test_importance['importances_mean'].reshape(-1,1)\n",
    "train_num = train_importance['importances_mean'].reshape(-1,1)\n",
    "\n",
    "test_num = pd.DataFrame(test_num, columns = ['test'])\n",
    "train_num = pd.DataFrame(train_num, columns = ['train'])\n",
    "\n",
    "features = train[predictors].columns\n",
    "features = pd.DataFrame(features, columns = ['features'])\n",
    "\n",
    "test_features = pd.concat([features, test_num], axis=1)\n",
    "train_features = pd.concat([features, train_num], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "59a21a03-61c0-4041-8c79-b8367fbcf188",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features = test_features.sort_values('test', ascending=False).head(20)\n",
    "train_features = train_features.sort_values('train', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2e89ce00-a0be-41af-98c1-162e1278a1ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mp</td>\n",
       "      <td>1.119267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>pts</td>\n",
       "      <td>0.675715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fta</td>\n",
       "      <td>0.239394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fga</td>\n",
       "      <td>0.238911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>x2pa</td>\n",
       "      <td>0.142228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fg</td>\n",
       "      <td>0.089146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ast</td>\n",
       "      <td>0.079339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ft</td>\n",
       "      <td>0.072835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>tov</td>\n",
       "      <td>0.066392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>drb</td>\n",
       "      <td>0.062259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>x2p</td>\n",
       "      <td>0.054804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>orb</td>\n",
       "      <td>0.028872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>pf</td>\n",
       "      <td>0.009321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>n_rtg</td>\n",
       "      <td>0.005548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>l</td>\n",
       "      <td>0.003597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>d_rtg</td>\n",
       "      <td>0.002931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>blk</td>\n",
       "      <td>0.002761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>w</td>\n",
       "      <td>0.002545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>pace</td>\n",
       "      <td>0.001857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>0.001388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   features     train\n",
       "5        mp  1.119267\n",
       "27      pts  0.675715\n",
       "17      fta  0.239394\n",
       "7       fga  0.238911\n",
       "13     x2pa  0.142228\n",
       "6        fg  0.089146\n",
       "22      ast  0.079339\n",
       "16       ft  0.072835\n",
       "25      tov  0.066392\n",
       "20      drb  0.062259\n",
       "12      x2p  0.054804\n",
       "19      orb  0.028872\n",
       "26       pf  0.009321\n",
       "37    n_rtg  0.005548\n",
       "29        l  0.003597\n",
       "36    d_rtg  0.002931\n",
       "24      blk  0.002761\n",
       "28        w  0.002545\n",
       "38     pace  0.001857\n",
       "1       age  0.001388"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a71aa9f9-d8a5-4bdd-9fd1-6947a17bc9b1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mp</td>\n",
       "      <td>1.185364e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>pts</td>\n",
       "      <td>5.626157e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fga</td>\n",
       "      <td>3.637764e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>x2pa</td>\n",
       "      <td>1.631182e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fta</td>\n",
       "      <td>1.515237e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ft</td>\n",
       "      <td>1.189232e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>tov</td>\n",
       "      <td>9.581332e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>orb</td>\n",
       "      <td>2.998006e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>drb</td>\n",
       "      <td>2.426258e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>pf</td>\n",
       "      <td>1.259716e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fg</td>\n",
       "      <td>7.496773e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>x3pa</td>\n",
       "      <td>7.479568e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ast</td>\n",
       "      <td>3.291401e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>experience</td>\n",
       "      <td>3.194075e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>n_rtg</td>\n",
       "      <td>2.969988e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>l</td>\n",
       "      <td>2.568751e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>w</td>\n",
       "      <td>2.089555e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>pace</td>\n",
       "      <td>6.744785e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>d_rtg</td>\n",
       "      <td>6.387076e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>pw</td>\n",
       "      <td>4.051918e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>mov</td>\n",
       "      <td>2.625935e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ft_percent</td>\n",
       "      <td>2.464932e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fg_percent</td>\n",
       "      <td>2.175350e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>pl</td>\n",
       "      <td>2.006215e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>o_rtg</td>\n",
       "      <td>1.625780e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>e_fg_percent_x</td>\n",
       "      <td>1.447563e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>x2p_percent</td>\n",
       "      <td>1.364175e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>tov_percent</td>\n",
       "      <td>1.019069e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>x3p_percent</td>\n",
       "      <td>2.082438e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>f_tr</td>\n",
       "      <td>2.771134e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ft_fga</td>\n",
       "      <td>2.348410e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>x3p_ar</td>\n",
       "      <td>9.706343e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>season</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ts_percent</td>\n",
       "      <td>-9.126714e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>sos</td>\n",
       "      <td>-5.934311e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>srs</td>\n",
       "      <td>-2.253785e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>orb_percent</td>\n",
       "      <td>-4.434740e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>-1.077846e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>x3p</td>\n",
       "      <td>-2.284869e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gs</td>\n",
       "      <td>-5.830627e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>blk</td>\n",
       "      <td>-7.942093e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>x2p</td>\n",
       "      <td>-9.214714e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>trb</td>\n",
       "      <td>-1.045878e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>stl</td>\n",
       "      <td>-1.588756e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>g</td>\n",
       "      <td>-1.753899e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          features          test\n",
       "5               mp  1.185364e+00\n",
       "27             pts  5.626157e-01\n",
       "7              fga  3.637764e-01\n",
       "13            x2pa  1.631182e-01\n",
       "17             fta  1.515237e-01\n",
       "16              ft  1.189232e-01\n",
       "25             tov  9.581332e-02\n",
       "19             orb  2.998006e-02\n",
       "20             drb  2.426258e-02\n",
       "26              pf  1.259716e-02\n",
       "6               fg  7.496773e-03\n",
       "10            x3pa  7.479568e-03\n",
       "22             ast  3.291401e-03\n",
       "2       experience  3.194075e-03\n",
       "37           n_rtg  2.969988e-03\n",
       "29               l  2.568751e-03\n",
       "28               w  2.089555e-03\n",
       "38            pace  6.744785e-04\n",
       "36           d_rtg  6.387076e-04\n",
       "30              pw  4.051918e-04\n",
       "32             mov  2.625935e-04\n",
       "18      ft_percent  2.464932e-04\n",
       "8       fg_percent  2.175350e-04\n",
       "31              pl  2.006215e-04\n",
       "35           o_rtg  1.625780e-04\n",
       "15  e_fg_percent_x  1.447563e-04\n",
       "14     x2p_percent  1.364175e-04\n",
       "42     tov_percent  1.019069e-04\n",
       "11     x3p_percent  2.082438e-05\n",
       "39            f_tr  2.771134e-07\n",
       "44          ft_fga  2.348410e-07\n",
       "40          x3p_ar  9.706343e-08\n",
       "0           season  0.000000e+00\n",
       "41      ts_percent -9.126714e-08\n",
       "33             sos -5.934311e-06\n",
       "34             srs -2.253785e-05\n",
       "43     orb_percent -4.434740e-04\n",
       "1              age -1.077846e-03\n",
       "9              x3p -2.284869e-03\n",
       "4               gs -5.830627e-03\n",
       "24             blk -7.942093e-03\n",
       "12             x2p -9.214714e-03\n",
       "21             trb -1.045878e-02\n",
       "23             stl -1.588756e-02\n",
       "3                g -1.753899e-02"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a9e4d631-7e9e-4bad-ba0d-45a6c2ec605e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the Features on Bar Chat\n",
    "plt.figure(figsize = (10,4))\n",
    "plt.bar(x=train_features['features'], height= train_features['train'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Permutation Feature Importance on (SVR model) Training Set')\n",
    "plt.xlabel('Features')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1d52fc07-6b99-4665-b7a4-2e87e9b7cb3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,4))\n",
    "plt.bar(x=test_features['features'], height= test_features['test'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Permutation Feature Importance on (SVR model) Testing Set')\n",
    "plt.xlabel('Features')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4e77cbd8-c97d-4a6b-8192-69be45d1f66e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVR model is weighing heavily on Minutes Played(mp) and Points(pts) on...\n",
    "#Both during Training and Testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3a6c35f1-9c80-4781-8c40-b862b81930a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "least_test_features = test_features.sort_values('test', ascending=False).tail(20)\n",
    "least_train_features = train_features.sort_values('train', ascending=False).tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ae7545b9-e6ac-4d2a-b3a3-58df32ad7df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the least performing features on training and test set\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, layout='constrained', figsize=(12,5))\n",
    "\n",
    "ax1.bar(x=least_train_features['features'], height=least_train_features['train'])\n",
    "ax1.set_title(\"Least Permutated Features on (SVR model) Training Set\")\n",
    "ax1.set_xlabel(\"Features\")\n",
    "\n",
    "\n",
    "ax2.bar(x=least_test_features['features'], height= least_test_features['test'])\n",
    "ax2.set_title(\"Least Permutated Features on (SVR model) Testing Set\")\n",
    "ax2.set_xlabel(\"Features\")\n",
    "\n",
    "#slant the xticks on both axes\n",
    "for i, ax in enumerate([ax1, ax2]):\n",
    "    ax.set_xticks(ax.get_xticks()) # setting the ticks first before..\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45)  #x_ticklabel modifications\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d227e52-e142-4509-b3f8-fa4cf645f18e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0bfc8078-fccd-4e02-baa1-cbb50107050e",
   "metadata": {},
   "source": [
    "PERMUTATION FEATURE IMPORTANCE ON KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47d9ea71-4f5f-412b-9d30-6db9cea2d5f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Was repeating processes above.\n",
    "#Unifying the processes through a function\n",
    "\n",
    "\n",
    "def find_permutation(model, predictors, target, column_name):\n",
    "    train_impt = permutation_importance(\n",
    "    model, predictors, target, random_state = 0, n_repeats = 42, n_jobs = 2\n",
    "    )\n",
    "    \n",
    "    train_num = train_impt['importances_mean'].reshape(-1,1)\n",
    "    train_num = pd.DataFrame(train_num, columns = column_name)\n",
    "\n",
    "    features = predictors.columns\n",
    "    features = pd.DataFrame(features, columns = ['features'])\n",
    "\n",
    "    train_features = pd.concat([features, train_num], axis =1)\n",
    "    top_train_features = train_features.sort_values(column_name, ascending=False).head(20)\n",
    "    least_train_features = train_features.sort_values(column_name, ascending=False).tail(20)\n",
    "\n",
    "    return top_train_features, least_train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b55f4a6d-5580-4161-ac10-ac34cb5ee27b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features, least_train_features = find_permutation(\n",
    "    knn, train[predictors], train['share'], ['train']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72c09841-3e66-4193-a64c-71fc7e544fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8e924d2-c609-4e1f-a836-8086d4a1f6e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "least_train_feaures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a0a4536-1f74-405c-8cc4-140c02689173",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a3fad873-4fb4-44af-83d2-93495fae0088",
   "metadata": {},
   "source": [
    "IMPROVING THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8e838dba-3e4c-4621-b84d-db62c2fc5464",
   "metadata": {},
   "outputs": [],
   "source": [
    "averaged  = df[[\"season\", 'x3p','fg', 'ast', 'pts', 'w', 'mp']].groupby(\n",
    "    'season', group_keys = False).apply(\n",
    "    lambda x: x/x.mean(), include_groups =False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "652dc9fc-a631-4bf1-91e3-f72df2199ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x3p_r</th>\n",
       "      <th>fg_r</th>\n",
       "      <th>ast_r</th>\n",
       "      <th>pts_r</th>\n",
       "      <th>w_r</th>\n",
       "      <th>mp_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.020505</td>\n",
       "      <td>0.568957</td>\n",
       "      <td>0.573970</td>\n",
       "      <td>0.575596</td>\n",
       "      <td>1.016884</td>\n",
       "      <td>0.668141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.078500</td>\n",
       "      <td>0.897041</td>\n",
       "      <td>0.553224</td>\n",
       "      <td>0.831954</td>\n",
       "      <td>1.016884</td>\n",
       "      <td>1.216487</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.884010</td>\n",
       "      <td>0.905347</td>\n",
       "      <td>1.825640</td>\n",
       "      <td>0.964163</td>\n",
       "      <td>1.016884</td>\n",
       "      <td>1.088136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.631524</td>\n",
       "      <td>3.363905</td>\n",
       "      <td>1.383060</td>\n",
       "      <td>3.447125</td>\n",
       "      <td>1.016884</td>\n",
       "      <td>2.059329</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020765</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>0.019348</td>\n",
       "      <td>1.016884</td>\n",
       "      <td>0.020679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>1.382259</td>\n",
       "      <td>1.286944</td>\n",
       "      <td>2.816166</td>\n",
       "      <td>1.212846</td>\n",
       "      <td>0.857742</td>\n",
       "      <td>1.533096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15610</th>\n",
       "      <td>0.017721</td>\n",
       "      <td>0.098996</td>\n",
       "      <td>0.129578</td>\n",
       "      <td>0.106791</td>\n",
       "      <td>0.857742</td>\n",
       "      <td>0.122105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15611</th>\n",
       "      <td>0.141770</td>\n",
       "      <td>0.338670</td>\n",
       "      <td>0.293711</td>\n",
       "      <td>0.320374</td>\n",
       "      <td>0.857742</td>\n",
       "      <td>0.434151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15612</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010421</td>\n",
       "      <td>0.025916</td>\n",
       "      <td>0.011442</td>\n",
       "      <td>0.857742</td>\n",
       "      <td>0.025325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15613</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088575</td>\n",
       "      <td>0.051831</td>\n",
       "      <td>0.072466</td>\n",
       "      <td>0.857742</td>\n",
       "      <td>0.113965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15614 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          x3p_r      fg_r     ast_r     pts_r       w_r      mp_r\n",
       "0      1.020505  0.568957  0.573970  0.575596  1.016884  0.668141\n",
       "1      0.078500  0.897041  0.553224  0.831954  1.016884  1.216487\n",
       "2      1.884010  0.905347  1.825640  0.964163  1.016884  1.088136\n",
       "3      4.631524  3.363905  1.383060  3.447125  1.016884  2.059329\n",
       "4      0.000000  0.020765  0.013831  0.019348  1.016884  0.020679\n",
       "...         ...       ...       ...       ...       ...       ...\n",
       "15609  1.382259  1.286944  2.816166  1.212846  0.857742  1.533096\n",
       "15610  0.017721  0.098996  0.129578  0.106791  0.857742  0.122105\n",
       "15611  0.141770  0.338670  0.293711  0.320374  0.857742  0.434151\n",
       "15612  0.000000  0.010421  0.025916  0.011442  0.857742  0.025325\n",
       "15613  0.000000  0.088575  0.051831  0.072466  0.857742  0.113965\n",
       "\n",
       "[15614 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averaged.columns = ['x3p_r', 'fg_r', 'ast_r', 'pts_r', 'w_r', 'mp_r']\n",
    "averaged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d307cb49-9f91-4335-91a9-05b5167067bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add the new feature to the dataframe\n",
    "df[['x3p_r', 'fg_r', 'ast_r', 'pts_r', 'w_r', 'mp_r']] = averaged [['x3p_r', 'fg_r', 'ast_r', 'pts_r', 'w_r', 'mp_r']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66f42c66-d593-4806-a9c2-4b9eb1a3a48e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'player', 'pos', 'age', 'experience', 'lg_x', 'tm', 'g', 'gs',\n",
       "       'mp', 'fg', 'fga', 'fg_percent', 'x3p', 'x3pa', 'x3p_percent', 'x2p',\n",
       "       'x2pa', 'x2p_percent', 'e_fg_percent_x', 'ft', 'fta', 'ft_percent',\n",
       "       'orb', 'drb', 'trb', 'ast', 'stl', 'blk', 'tov', 'pf', 'pts', 'pts_won',\n",
       "       'pts_max', 'share', 'team', 'w', 'l', 'pw', 'pl', 'mov', 'sos', 'srs',\n",
       "       'o_rtg', 'd_rtg', 'n_rtg', 'pace', 'f_tr', 'x3p_ar', 'ts_percent',\n",
       "       'tov_percent', 'orb_percent', 'ft_fga', 'x3p_r', 'fg_r', 'ast_r',\n",
       "       'pts_r', 'w_r', 'mp_r'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0d5a88a4-3977-44a0-b580-e9224be88eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add the new features to predictors\n",
    "predictors += ['x3p_r', 'fg_r', 'ast_r', 'pts_r', 'w_r', 'mp_r']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "469d4b87-e141-4a48-83f2-58948808fc4d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pc\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\svm\\_base.py:1237: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "mean_aps, aps, predictions  = backtest(df, pipe_1, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d4514864-8abc-4cef-bfab-a31bd98eaf78",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_aps2, aps2, predictions2  = backtest(df, pipe_2, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c7f4cc0a-cf37-424f-9ed8-9a35d1df3fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8632183908045977, 0.8836366538952746)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_aps, mean_aps2\n",
    "#the new predictors did not have much effect on the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9dbf98-d793-47f4-88ba-ccb768265f11",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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