PC_SF_ML.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# ! pip install gradio"
      ],
      "metadata": {
        "id": "NTZT9cXOxRgr"
      },
      "execution_count": 474,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Data Hnadling:Effective preprocessing, handling missing values, and appropriate scaling.\n",
        "# 3. Visualization: Clear and insightful visual representation using Matplotlib for feature engineering. 5-6 input params rest can be predefined\n",
        "# 4. Model Implementation: Correct application of algorithms with feature engineering.\n",
        "# 2. Insights and Interpretation: Thoughtful analysis and meaningful insights based on predictions and clustering.\n",
        "# 5. Use gradio to create an interface"
      ],
      "metadata": {
        "id": "evU7nw_rxcOT"
      },
      "execution_count": 475,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "_kpaquC1rVc7"
      },
      "execution_count": 476,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/train.csv\")\n",
        "train = pd.read_csv(\"/content/train.csv\")\n",
        "test = pd.read_csv(\"/content/test.csv\")"
      ],
      "metadata": {
        "id": "byqBnkD7uyRR"
      },
      "execution_count": 477,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSrUIqH5wu4o",
        "outputId": "400039d1-3cde-4a68-8658-7e8d4b69e22f"
      },
      "execution_count": 478,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 0\n",
              "MSSubClass         0\n",
              "MSZoning           0\n",
              "LotFrontage      259\n",
              "LotArea            0\n",
              "                ... \n",
              "MoSold             0\n",
              "YrSold             0\n",
              "SaleType           0\n",
              "SaleCondition      0\n",
              "SalePrice          0\n",
              "Length: 81, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 478
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djrrhUeA-wyN",
        "outputId": "6d8c12d4-d145-4c46-d7be-cc7ef700460f"
      },
      "execution_count": 479,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 81)"
            ]
          },
          "metadata": {},
          "execution_count": 479
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isnull(), yticklabels=False,cmap='Reds')\n",
        "'''shows that ['Alley', 'PoolQC', 'FireplaceQu'] have too many null values so I'm dropping them.\n",
        " Since all the cols are not shown here, I'll drop all the cols with >20% null values'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "r6O62SNL9EyH",
        "outputId": "eeaa1c99-b129-4917-9de0-7138bfa8c55f"
      },
      "execution_count": 480,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"shows that ['Alley', 'PoolQC', 'FireplaceQu'] have too many null values so I'm dropping them.\\n Since all the cols are not shown here, I'll drop all the cols with >20% null values\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 480
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop_columns = df.columns[df.isnull().sum() >= 292]\n",
        "drop_columns = ['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']\n",
        "print(drop_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO7S4fNB_scG",
        "outputId": "3b4e3f2b-8590-4762-9768-b9e52bf25332"
      },
      "execution_count": 481,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(drop_columns,axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "YLPVLy17A5a0"
      },
      "execution_count": 482,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "gY9AaZ87BW1z",
        "outputId": "ad0e0191-0cf1-4037-c206-b8e6457b03e0"
      },
      "execution_count": 483,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
              "0   1          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
              "1   2          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
              "2   3          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
              "3   4          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
              "4   5          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
              "\n",
              "  Utilities LotConfig  ... EnclosedPorch 3SsnPorch ScreenPorch PoolArea  \\\n",
              "0    AllPub    Inside  ...             0         0           0        0   \n",
              "1    AllPub       FR2  ...             0         0           0        0   \n",
              "2    AllPub    Inside  ...             0         0           0        0   \n",
              "3    AllPub    Corner  ...           272         0           0        0   \n",
              "4    AllPub       FR2  ...             0         0           0        0   \n",
              "\n",
              "  MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
              "0       0      2    2008        WD         Normal     208500  \n",
              "1       0      5    2007        WD         Normal     181500  \n",
              "2       0      9    2008        WD         Normal     223500  \n",
              "3       0      2    2006        WD        Abnorml     140000  \n",
              "4       0     12    2008        WD         Normal     250000  \n",
              "\n",
              "[5 rows x 76 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dacfb90-16e5-4ef3-bcee-f2df5311edcd\" class=\"colab-df-container\">\n",
              "    <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>Id</th>\n",
              "      <th>MSSubClass</th>\n",
              "      <th>MSZoning</th>\n",
              "      <th>LotFrontage</th>\n",
              "      <th>LotArea</th>\n",
              "      <th>Street</th>\n",
              "      <th>LotShape</th>\n",
              "      <th>LandContour</th>\n",
              "      <th>Utilities</th>\n",
              "      <th>LotConfig</th>\n",
              "      <th>...</th>\n",
              "      <th>EnclosedPorch</th>\n",
              "      <th>3SsnPorch</th>\n",
              "      <th>ScreenPorch</th>\n",
              "      <th>PoolArea</th>\n",
              "      <th>MiscVal</th>\n",
              "      <th>MoSold</th>\n",
              "      <th>YrSold</th>\n",
              "      <th>SaleType</th>\n",
              "      <th>SaleCondition</th>\n",
              "      <th>SalePrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>65.0</td>\n",
              "      <td>8450</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>RL</td>\n",
              "      <td>80.0</td>\n",
              "      <td>9600</td>\n",
              "      <td>Pave</td>\n",
              "      <td>Reg</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2007</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>68.0</td>\n",
              "      <td>11250</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>223500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>70</td>\n",
              "      <td>RL</td>\n",
              "      <td>60.0</td>\n",
              "      <td>9550</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Corner</td>\n",
              "      <td>...</td>\n",
              "      <td>272</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2006</td>\n",
              "      <td>WD</td>\n",
              "      <td>Abnorml</td>\n",
              "      <td>140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>60</td>\n",
              "      <td>RL</td>\n",
              "      <td>84.0</td>\n",
              "      <td>14260</td>\n",
              "      <td>Pave</td>\n",
              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>FR2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>2008</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 76 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dacfb90-16e5-4ef3-bcee-f2df5311edcd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3dacfb90-16e5-4ef3-bcee-f2df5311edcd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3dacfb90-16e5-4ef3-bcee-f2df5311edcd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-440d28c6-7b14-4fd9-baf3-d02f11cd8768\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-440d28c6-7b14-4fd9-baf3-d02f11cd8768')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    }\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-440d28c6-7b14-4fd9-baf3-d02f11cd8768 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 483
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Filling missing values***"
      ],
      "metadata": {
        "id": "kIq-U12QIUnY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Filling missing values\n",
        "fill_cols = df.columns[df.isnull().sum() >= 1]\n",
        "for col in fill_cols:\n",
        "    print(f\"'{col}' data type: {df[col].dtype}\")\n",
        "#I am going to fill mean values for float dtypes and mode for object dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Oa1PoXiB557",
        "outputId": "dc7336b1-9c8a-4f69-90a4-b03a9aae6300"
      },
      "execution_count": 484,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'LotFrontage' data type: float64\n",
            "'MasVnrType' data type: object\n",
            "'MasVnrArea' data type: float64\n",
            "'BsmtQual' data type: object\n",
            "'BsmtCond' data type: object\n",
            "'BsmtExposure' data type: object\n",
            "'BsmtFinType1' data type: object\n",
            "'BsmtFinType2' data type: object\n",
            "'Electrical' data type: object\n",
            "'GarageType' data type: object\n",
            "'GarageYrBlt' data type: float64\n",
            "'GarageFinish' data type: object\n",
            "'GarageQual' data type: object\n",
            "'GarageCond' data type: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fill_with_mean = []\n",
        "fill_with_mode = []\n",
        "for col in fill_cols:\n",
        "  if df[col].dtype == 'float64':\n",
        "    fill_with_mean.append(col)\n",
        "  else:\n",
        "    fill_with_mode.append(col)"
      ],
      "metadata": {
        "id": "OkKgSOdGExk_"
      },
      "execution_count": 485,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(fill_with_mode)\n",
        "print(fill_with_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyZ7RxxgFg4B",
        "outputId": "46a9bbdc-438c-45f7-f759-851b57b257a2"
      },
      "execution_count": 486,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']\n",
            "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in fill_cols:\n",
        "  if df[col].dtype == 'float64':\n",
        "    df[col] = df[col].fillna(df[col].mean())\n",
        "  else:\n",
        "    df[col] = df[col].fillna(df[col].mode()[0])"
      ],
      "metadata": {
        "id": "1STKAnrxFyTb"
      },
      "execution_count": 487,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['Id'],axis=1,inplace=True)#need to input the col coz u already specified from which df.. remember form next time"
      ],
      "metadata": {
        "id": "9daxtV9mIiw1"
      },
      "execution_count": 488,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# def data_preprep(data):\n",
        "  #dropped drop_columns = ['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature']\n",
        "  #fill_with_mode = ['MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']\n",
        "  #fill_with_mean = ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']\n",
        "  #drop id col"
      ],
      "metadata": {
        "id": "iYiiPVe_-c5m"
      },
      "execution_count": 489,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Feature Selection/Modification***"
      ],
      "metadata": {
        "id": "Y_IY4WhgJUwM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsyP6attJUZJ",
        "outputId": "2592b914-eff2-44e3-aa38-dac4940b2e1f"
      },
      "execution_count": 490,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
              "       'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope',\n",
              "       'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle',\n",
              "       'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle',\n",
              "       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea',\n",
              "       'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond',\n",
              "       'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2',\n",
              "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC',\n",
              "       'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
              "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
              "       'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd',\n",
              "       'Functional', 'Fireplaces', 'GarageType', 'GarageYrBlt', 'GarageFinish',\n",
              "       'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond', 'PavedDrive',\n",
              "       'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',\n",
              "       'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
              "       'SaleCondition', 'SalePrice'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 490
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping features whose value may be approximated using other features\n",
        "drop_cols = ['Condition1', 'Condition2','HouseStyle','RoofStyle',\n",
        "       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea','BsmtQual', 'BsmtCond',\n",
        "       'BsmtExposure','BsmtFinSF1','BsmtFinSF2', 'Heating','MoSold', 'SaleType','SaleCondition','Electrical','Street','GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageCars','Foundation','Functional']\n",
        "df_red = df.drop(drop_cols, axis=1)"
      ],
      "metadata": {
        "id": "Ui2g3pWuMaU1"
      },
      "execution_count": 491,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#For LabelEncoding:\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "def custom_encoder(df_red):\n",
        "  category_mapping = {\"Ex\": 5, \"Gd\": 4, \"TA\": 3, \"Fa\": 2, \"Po\": 1, 'NA':0,'Y':2,'N':0,'P':1,'Gtl':1,'Mod':2,'Sev':3}\n",
        "\n",
        "  # Create a custom LabelEncoder\n",
        "  class CustomLabelEncoder(LabelEncoder):\n",
        "      def fit(self, X, y=None):\n",
        "          super().fit(X)\n",
        "          self.classes_ = category_mapping\n",
        "          return self\n",
        "\n",
        "  # Use the custom encoder\n",
        "  encoder = CustomLabelEncoder()\n",
        "  df_red['KitchenQual_encoded'] = encoder.fit_transform(df_red['KitchenQual'])\n",
        "  df_red['ExterQual_encoded'] = encoder.fit_transform(df_red['ExterQual'])\n",
        "  df_red['Extercond_encoded'] = encoder.fit_transform(df_red['ExterCond'])\n",
        "  df_red['GarageQual_encoded'] = encoder.fit_transform(df_red['GarageQual'])\n",
        "  df_red['Garagecond_encoded'] = encoder.fit_transform(df_red['GarageCond'])\n",
        "  df_red['PavedDrive_encoded'] = encoder.fit_transform(df_red['PavedDrive'])\n",
        "  df_red['LandSlope'] = encoder.fit_transform(df_red['LandSlope'])\n",
        "  df_red['HeatingQC'] = encoder.fit_transform(df_red['HeatingQC'])\n",
        "  df_red['CentralAir'] = encoder.fit_transform(df_red['CentralAir'])\n",
        "custom_encoder(df_red)"
      ],
      "metadata": {
        "id": "KJIGAcWXYWKL"
      },
      "execution_count": 492,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_trans(df_red):\n",
        "  df_red['house_cond'] = df_red['1stFlrSF']+df_red['2ndFlrSF']-(0.5)*df_red['LowQualFinSF']+df_red['GrLivArea'] + (50)*df_red['BsmtFullBath']+ (50)*df_red['BsmtHalfBath']+ (50)*df_red['FullBath']+ (50)*df_red['HalfBath']\n",
        "  + (500)*df_red['KitchenAbvGr']*df_red['KitchenQual_encoded'] + 100*df_red['TotRmsAbvGrd']\n",
        "  + 100*df_red['Fireplaces'] + df_red['WoodDeckSF']+ df_red['OpenPorchSF']+ df_red['EnclosedPorch']+ df_red['3SsnPorch'] +df_red['ScreenPorch']+ df_red['PoolArea'] + df_red['MiscVal'] + df_red['LotArea'] + 10*df_red['LotFrontage'] + 100*(df_red['OverallQual']+df_red['OverallCond']+df_red['ExterQual_encoded']+df_red['Extercond_encoded']+df_red['CentralAir']+df_red['HeatingQC']) + df_red['TotalBsmtSF'] + df_red['GarageArea']\n",
        "  df_red['Garage_cond'] = df_red['GarageQual_encoded'] + df_red['Garagecond_encoded'] + df_red['PavedDrive_encoded'] - df_red['LandSlope']\n",
        "  df_red['Age'] = df_red['YrSold'] - (df_red['YearRemodAdd'] + df_red['YearBuilt'])*0.5\n",
        "  df_red.drop(['LotFrontage', 'LotArea','1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea',\n",
        "        'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr',\n",
        "        'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd','Fireplaces','WoodDeckSF',\n",
        "        'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea',\n",
        "        'MiscVal', 'KitchenQual_encoded','OverallQual','OverallCond','ExterQual_encoded',\n",
        "  'Extercond_encoded','BsmtFinType1', 'BsmtFinType2', 'BsmtUnfSF', 'TotalBsmtSF','ExterQual', 'ExterCond', 'GarageQual',\n",
        "  'GarageCond', 'GarageQual_encoded', 'Garagecond_encoded','GarageArea','PavedDrive','PavedDrive_encoded','LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'BldgType','CentralAir', 'HeatingQC', 'YearBuilt', 'YearRemodAdd','YrSold'], axis=1,inplace=True)\n",
        "feature_trans(df_red)"
      ],
      "metadata": {
        "id": "Ds1OjGZgVKnx"
      },
      "execution_count": 493,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols_light = []\n",
        "cat_cols_heavy = []\n",
        "for col in df_red.columns:\n",
        "  if df_red[col].dtype == 'object':\n",
        "    print(f\"'{col}' unique values: {df_red[col].nunique()}\")\n",
        "    if df_red[col].nunique() >= 6:\n",
        "      cat_cols_heavy.append(col)\n",
        "    else:\n",
        "      cat_cols_light.append(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbTFdY7ekJzq",
        "outputId": "f4c3d0ae-e7e4-4403-af03-d794e0cb6a1b"
      },
      "execution_count": 494,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'MSZoning' unique values: 5\n",
            "'Neighborhood' unique values: 25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(cat_cols_heavy)\n",
        "print(cat_cols_light)#Was useful when I had too many features.."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNAZ0Xt6kVb0",
        "outputId": "3f384420-b942-4a85-ff7c-20805aeee3d8"
      },
      "execution_count": 495,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Neighborhood']\n",
            "['MSZoning']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data = df_red, y = 'SalePrice', hue='Neighborhood')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "sMCWuIpx6vJw",
        "outputId": "c52067c0-c724-4030-d451-08fedced70bf"
      },
      "execution_count": 496,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='SalePrice'>"
            ]
          },
          "metadata": {},
          "execution_count": 496
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data = df_red, y = 'SalePrice', hue='MSZoning')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "Yrh-nDme9vL_",
        "outputId": "75255cc5-2adc-4d7a-d5f7-182b1a0d6db1"
      },
      "execution_count": 497,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='SalePrice'>"
            ]
          },
          "metadata": {},
          "execution_count": 497
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_values = {}\n",
        "for col in df_red[['Neighborhood','MSZoning']]:\n",
        "    mean_values[col] = df_red.groupby(col)['SalePrice'].mean().to_dict()\n",
        "def feature_encoder(df_red):\n",
        "  df_red[\"NHB_encoded\"] = df_red.groupby(\"Neighborhood\")[\"SalePrice\"].transform(\"mean\")\n",
        "  df_red[\"MSZ_encoded\"] = df_red.groupby(\"MSZoning\")[\"SalePrice\"].transform(\"mean\")\n",
        "  df_red.drop(['Neighborhood','MSZoning'], axis=1,inplace=True)\n",
        "\n"
      ],
      "metadata": {
        "id": "Nbl7kMb9-M0V"
      },
      "execution_count": 498,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(mean_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8j2UvWayPYxw",
        "outputId": "b5d581f7-2af5-4611-c372-8a34beb57e16"
      },
      "execution_count": 499,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Neighborhood': {'Blmngtn': 194870.88235294117, 'Blueste': 137500.0, 'BrDale': 104493.75, 'BrkSide': 124834.05172413793, 'ClearCr': 212565.42857142858, 'CollgCr': 197965.77333333335, 'Crawfor': 210624.72549019608, 'Edwards': 128219.7, 'Gilbert': 192854.50632911394, 'IDOTRR': 100123.78378378379, 'MeadowV': 98576.4705882353, 'Mitchel': 156270.1224489796, 'NAmes': 145847.08, 'NPkVill': 142694.44444444444, 'NWAmes': 189050.0684931507, 'NoRidge': 335295.31707317074, 'NridgHt': 316270.6233766234, 'OldTown': 128225.30088495575, 'SWISU': 142591.36, 'Sawyer': 136793.13513513515, 'SawyerW': 186555.7966101695, 'Somerst': 225379.83720930232, 'StoneBr': 310499.0, 'Timber': 242247.44736842104, 'Veenker': 238772.72727272726}, 'MSZoning': {'C (all)': 74528.0, 'FV': 214014.06153846154, 'RH': 131558.375, 'RL': 191004.99478714162, 'RM': 126316.83027522935}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_encoder(df_red)"
      ],
      "metadata": {
        "id": "tmDgB0I_-lQC"
      },
      "execution_count": 500,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "buRKViV2AA4D"
      },
      "execution_count": 501,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Preprocessing Train and Test Data***"
      ],
      "metadata": {
        "id": "dkuY4D6BRuKb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = df_red.drop('SalePrice', axis=1)\n",
        "Y_train = df_red['SalePrice']\n",
        "X_train[['MSSubClass', 'house_cond', 'Garage_cond', 'Age','NHB_encoded', 'MSZ_encoded']] = scaler.fit_transform(df_red[['MSSubClass','house_cond', 'Garage_cond', 'Age','NHB_encoded', 'MSZ_encoded']])"
      ],
      "metadata": {
        "id": "uWX1RWk1Ayjz"
      },
      "execution_count": 502,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = test.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature','Id'], axis=1)"
      ],
      "metadata": {
        "id": "BiMIY73XIvFc"
      },
      "execution_count": 503,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fill_cols = X_test.columns[X_test.isnull().sum() >= 1]\n",
        "for col in fill_cols:\n",
        "  if X_test[col].dtype == 'float64':\n",
        "    X_test[col] = X_test[col].fillna(X_test[col].mean())\n",
        "  else:\n",
        "    X_test[col] = X_test[col].fillna(X_test[col].mode()[0])\n",
        "drop_cols = ['Condition1', 'Condition2','HouseStyle','RoofStyle',\n",
        "       'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea','BsmtQual', 'BsmtCond',\n",
        "       'BsmtExposure','BsmtFinSF1','BsmtFinSF2', 'Heating','MoSold', 'SaleType','SaleCondition','Electrical','Street','GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageCars','Foundation','Functional']\n",
        "X_test = X_test.drop(drop_cols, axis=1)\n",
        "custom_encoder(X_test)\n",
        "feature_trans(X_test)\n",
        "for col in X_test[['Neighborhood','MSZoning']]:\n",
        "    X_test[col] = X_test[col].apply(lambda x: mean_values[col].get(x, x))  # Use get() to handle missing values\n"
      ],
      "metadata": {
        "id": "ZRoyNqgeE8QF"
      },
      "execution_count": 504,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[['NHB_encoded','MSZ_encoded']] = X_test[['Neighborhood','MSZoning']]"
      ],
      "metadata": {
        "id": "krwFehc0T6w_"
      },
      "execution_count": 505,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[['MSSubClass', 'house_cond', 'Garage_cond', 'Age','NHB_encoded', 'MSZ_encoded']] = scaler.fit_transform(X_test[['MSSubClass','house_cond', 'Garage_cond', 'Age','NHB_encoded', 'MSZ_encoded']])"
      ],
      "metadata": {
        "id": "9tE71rRvRN5e"
      },
      "execution_count": 506,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.drop(['Neighborhood','MSZoning'], axis=1,inplace=True)\n",
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "g1mZgURaTt-s",
        "outputId": "a266efde-26f3-47f8-edfa-93143fa752f2"
      },
      "execution_count": 507,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MSSubClass  house_cond  Garage_cond       Age  NHB_encoded  MSZ_encoded\n",
              "0   -0.874711   -1.245852     0.386027  0.801821    -0.558681    -1.751357\n",
              "1   -0.874711   -0.330834     0.386027  0.930177    -0.558681     0.405112\n",
              "2    0.061351    0.318469     0.386027 -0.759842     0.233917     0.405112\n",
              "3    0.061351    0.268523     0.386027 -0.781235     0.233917     0.405112\n",
              "4    1.465443   -0.428729     0.386027 -0.524523     2.217536     0.405112"
            ],
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              "      <th>Garage_cond</th>\n",
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              "      <th>NHB_encoded</th>\n",
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              "      <td>-1.245852</td>\n",
              "      <td>0.386027</td>\n",
              "      <td>0.801821</td>\n",
              "      <td>-0.558681</td>\n",
              "      <td>-1.751357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.874711</td>\n",
              "      <td>-0.330834</td>\n",
              "      <td>0.386027</td>\n",
              "      <td>0.930177</td>\n",
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              "      <td>-0.759842</td>\n",
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              "      <td>-0.781235</td>\n",
              "      <td>0.233917</td>\n",
              "      <td>0.405112</td>\n",
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              "      <th>4</th>\n",
              "      <td>1.465443</td>\n",
              "      <td>-0.428729</td>\n",
              "      <td>0.386027</td>\n",
              "      <td>-0.524523</td>\n",
              "      <td>2.217536</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "        document.querySelector('#df-7861857c-1f2f-43a7-b0d9-9322d03c6aea button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7861857c-1f2f-43a7-b0d9-9322d03c6aea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-151b4bbb-7d36-493b-9b99-e04fffef3681 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 507
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Model***"
      ],
      "metadata": {
        "id": "XT9ebWsuRpKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = KNeighborsRegressor(5)\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "B4muzfPyRsft",
        "outputId": "fb630e99-d6a8-42ed-cd43-861a2d6d93d8"
      },
      "execution_count": 508,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 508
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Predictions***"
      ],
      "metadata": {
        "id": "MWN8S8eJSkm0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "pred = pd.DataFrame(y_pred)\n",
        "sub_df = pd.read_csv('/content/sample_submission.csv')\n",
        "datasets=pd.concat([sub_df['Id'], pred], axis=1)\n",
        "datasets.columns = ['Id', 'SalePrice']\n",
        "datasets.to_csv(\"Sample_submission.csv\", index=False)"
      ],
      "metadata": {
        "id": "jwYR8wjSSomO"
      },
      "execution_count": 509,
      "outputs": []
    }
  ]
}