attachment_Linear_regression (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img height=\"60\" width=\"120\" src=\"https://shwetkm.github.io/upxlogo.png\"></img>\n",
    "# UpX Academy - Machine Learning Track\n",
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agenda\n",
    "\n",
    "1. Introducing the bikeshare dataset\n",
    "    - Reading in the data\n",
    "    - Visualizing the data\n",
    "2. Linear regression basics\n",
    "    - Form of linear regression\n",
    "    - Building a linear regression model\n",
    "    - Using the model for prediction\n",
    "    - Does the scale of the features matter?\n",
    "3. Working with multiple features\n",
    "    - Visualizing the data (part 2)\n",
    "    - Adding more features to the model\n",
    "4. Choosing between models\n",
    "    - Feature selection\n",
    "    - Evaluation metrics for regression problems\n",
    "    - Comparing models with train/test split and RMSE\n",
    "    - Comparing testing RMSE with null RMSE\n",
    "5. Creating features\n",
    "    - Handling categorical features\n",
    "    - Feature engineering\n",
    "6. Advantages/Disadvantages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in the data\n",
    "\n",
    "We'll be working with a dataset from Capital Bikeshare that was used in a Kaggle competition ([data dictionary](https://www.kaggle.com/c/bike-sharing-demand/data))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the data and set the datetime as the index\n",
    "import pandas as pd\n",
    "url = 'https://raw.githubusercontent.com/upxacademy/ML_with_Python/master/Datasets/bikeshare.csv?token=AYxzdiGnjM610dBT7PuwUnUNOmm3bGcvks5ZFDyLwA%3D%3D'\n",
    "bikes = pd.read_csv(url, index_col='datetime', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  \n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00        81        0.0       3          13     16  \n",
       "2011-01-01 01:00:00        80        0.0       8          32     40  \n",
       "2011-01-01 02:00:00        80        0.0       5          27     32  \n",
       "2011-01-01 03:00:00        75        0.0       3          10     13  \n",
       "2011-01-01 04:00:00        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.00000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "      <td>10886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.506614</td>\n",
       "      <td>0.028569</td>\n",
       "      <td>0.680875</td>\n",
       "      <td>1.418427</td>\n",
       "      <td>20.23086</td>\n",
       "      <td>23.655084</td>\n",
       "      <td>61.886460</td>\n",
       "      <td>12.799395</td>\n",
       "      <td>36.021955</td>\n",
       "      <td>155.552177</td>\n",
       "      <td>191.574132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.116174</td>\n",
       "      <td>0.166599</td>\n",
       "      <td>0.466159</td>\n",
       "      <td>0.633839</td>\n",
       "      <td>7.79159</td>\n",
       "      <td>8.474601</td>\n",
       "      <td>19.245033</td>\n",
       "      <td>8.164537</td>\n",
       "      <td>49.960477</td>\n",
       "      <td>151.039033</td>\n",
       "      <td>181.144454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.94000</td>\n",
       "      <td>16.665000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>7.001500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.50000</td>\n",
       "      <td>24.240000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>12.998000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.24000</td>\n",
       "      <td>31.060000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>16.997900</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>284.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>41.00000</td>\n",
       "      <td>45.455000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>56.996900</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             season       holiday    workingday       weather         temp  \\\n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.00000   \n",
       "mean       2.506614      0.028569      0.680875      1.418427     20.23086   \n",
       "std        1.116174      0.166599      0.466159      0.633839      7.79159   \n",
       "min        1.000000      0.000000      0.000000      1.000000      0.82000   \n",
       "25%        2.000000      0.000000      0.000000      1.000000     13.94000   \n",
       "50%        3.000000      0.000000      1.000000      1.000000     20.50000   \n",
       "75%        4.000000      0.000000      1.000000      2.000000     26.24000   \n",
       "max        4.000000      1.000000      1.000000      4.000000     41.00000   \n",
       "\n",
       "              atemp      humidity     windspeed        casual    registered  \\\n",
       "count  10886.000000  10886.000000  10886.000000  10886.000000  10886.000000   \n",
       "mean      23.655084     61.886460     12.799395     36.021955    155.552177   \n",
       "std        8.474601     19.245033      8.164537     49.960477    151.039033   \n",
       "min        0.760000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       16.665000     47.000000      7.001500      4.000000     36.000000   \n",
       "50%       24.240000     62.000000     12.998000     17.000000    118.000000   \n",
       "75%       31.060000     77.000000     16.997900     49.000000    222.000000   \n",
       "max       45.455000    100.000000     56.996900    367.000000    886.000000   \n",
       "\n",
       "              count  \n",
       "count  10886.000000  \n",
       "mean     191.574132  \n",
       "std      181.144454  \n",
       "min        1.000000  \n",
       "25%       42.000000  \n",
       "50%      145.000000  \n",
       "75%      284.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Questions:**\n",
    "\n",
    "- What does each observation represent?\n",
    "- What is the response variable (as defined by Kaggle)?\n",
    "- How many features are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# \"count\" is a method, so it's best to name that column something else\n",
    "bikes.rename(columns={'count':'total'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "      <td>2686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "      <td>2733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "      <td>2734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       holiday workingday weather  temp atemp humidity windspeed casual  \\\n",
       "         count      count   count count count    count     count  count   \n",
       "season                                                                    \n",
       "1         2686       2686    2686  2686  2686     2686      2686   2686   \n",
       "2         2733       2733    2733  2733  2733     2733      2733   2733   \n",
       "3         2733       2733    2733  2733  2733     2733      2733   2733   \n",
       "4         2734       2734    2734  2734  2734     2734      2734   2734   \n",
       "\n",
       "       registered total  \n",
       "            count count  \n",
       "season                   \n",
       "1            2686  2686  \n",
       "2            2733  2733  \n",
       "3            2733  2733  \n",
       "4            2734  2734  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Placeholder for any queries\n",
    "seasons = bikes.groupby('season').agg(['count'])\n",
    "seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2307436d518>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fRylTwWla5X1NIb9V2WRoZNe+/uZS8N/FcoXkcGuLoVBaBuCLn3uJH3+8EBTz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uAAAg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\nIiJD5t8B8WXuUw8R2+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x230720874a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas scatter plot\n",
    "bikes.plot(kind='scatter', x='temp', y='total', alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23072087390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B4p9Tykvo1C36mdGSPwb45kfnnI/T58/HHl//zimP7raV18O3n/Y56s+I44SZ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\n/wEgwLfH4d5hbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2307487dcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn scatter plot with regression line\n",
    "sns.lmplot(x='temp', y='total', data=bikes, aspect=1.5, scatter_kws={'alpha':0.2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Form of linear regression\n",
    "\n",
    "$y = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... + \\beta_nx_n$\n",
    "\n",
    "- $y$ is the response\n",
    "- $\\beta_0$ is the intercept\n",
    "- $\\beta_1$ is the coefficient for $x_1$ (the first feature)\n",
    "- $\\beta_n$ is the coefficient for $x_n$ (the nth feature)\n",
    "\n",
    "The $\\beta$ values are called the **model coefficients**:\n",
    "\n",
    "- These values are estimated (or \"learned\") during the model fitting process using the **least squares criterion**.\n",
    "- Specifically, we are going to find the line (mathematically) which minimizes the **sum of squared residuals** (or \"sum of squared errors\").\n",
    "- And once we've learned these coefficients, we can use the model to predict the response.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/upxacademy/ML_with_Python/master/images/estimating_coefficients.png?token=AH0Y7JmsKZPG913jPp4rVGpOba5gxbonks5ZFEq7wA%3D%3D\"></img>\n",
    "\n",
    "In the diagram above:\n",
    "\n",
    "- The black dots are the **observed values** of x and y.\n",
    "- The blue line is our **least squares line**.\n",
    "- The red lines are the **residuals**, which are the vertical distances between the observed values and the least squares line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a linear regression model\n",
    "\n",
    "Refer : http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create X and y\n",
    "feature_cols = ['temp']\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import, instantiate, fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.04621295962\n",
      "[ 9.17054048]\n"
     ]
    }
   ],
   "source": [
    "# print the coefficients\n",
    "print (linreg.intercept_)\n",
    "print (linreg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the **intercept** ($\\beta_0$):\n",
    "\n",
    "- It is the value of $y$ when $x$=0.\n",
    "- Thus, it is the estimated number of rentals when the temperature is 0 degrees Celsius.\n",
    "- **Note:** It does not always make sense to interpret the intercept. (Why?)\n",
    "\n",
    "Interpreting the **\"temp\" coefficient** ($\\beta_1$):\n",
    "\n",
    "- It is the change in $y$ divided by change in $x$, or the \"slope\".\n",
    "- Thus, a temperature increase of 1 degree Celsius is **associated with** a rental increase of 9.17 bikes.\n",
    "- This is not a statement of causation.\n",
    "- $\\beta_1$ would be **negative** if an increase in temperature was associated with a **decrease** in rentals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model for prediction\n",
    "\n",
    "How many bike rentals would we predict if the temperature was 25 degrees Celsius?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 235.309725])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manually calculate the prediction\n",
    "linreg.intercept_ + linreg.coef_*25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 235.309725])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the predict method\n",
    "linreg.predict(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does the scale of the features matter?\n",
    "\n",
    "Let's say that temperature was measured in Fahrenheit, rather than Celsius. How would that affect the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a new column for Fahrenheit temperature\n",
    "bikes['temp_F'] = bikes.temp * 1.8 + 32\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Seaborn scatter plot with regression line\n",
    "sns.lmplot(x='temp_F', y='total', data=bikes, aspect=1.5, scatter_kws={'alpha':0.2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create X and y\n",
    "feature_cols = ['temp_F']\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total\n",
    "\n",
    "# instantiate and fit\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)\n",
    "\n",
    "# print the coefficients\n",
    "print (linreg.intercept_)\n",
    "print (linreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert 25 degrees Celsius to Fahrenheit\n",
    "25 * 1.8 + 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# predict rentals for 77 degrees Fahrenheit\n",
    "linreg.predict(77)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** The scale of the features is **irrelevant** for linear regression models. When changing the scale, we simply change our **interpretation** of the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove the temp_F column\n",
    "bikes.drop('temp_F', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data (part 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# explore more features\n",
    "feature_cols = ['temp', 'season', 'weather', 'humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# multiple scatter plots in Seaborn\n",
    "sns.pairplot(bikes, x_vars=feature_cols, y_vars='total', kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# multiple scatter plots in Pandas\n",
    "fig, axs = plt.subplots(1, len(feature_cols), sharey=True)\n",
    "for index, feature in enumerate(feature_cols):\n",
    "    bikes.plot(kind='scatter', x=feature, y='total', ax=axs[index], figsize=(16, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are you seeing anything that you did not expect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# box plot of rentals, grouped by season\n",
    "bikes.boxplot(column='total', by='season')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notably:\n",
    "\n",
    "- A line can't capture a non-linear relationship.\n",
    "- There are more rentals in winter than in spring (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# line plot of rentals\n",
    "bikes.total.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this tell us?\n",
    "\n",
    "There are more rentals in the winter than the spring, but only because the system is experiencing **overall growth** and the winter months happen to come after the spring months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding more features to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a list of features\n",
    "feature_cols = ['temp', 'season', 'weather', 'humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create X and y\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total\n",
    "\n",
    "# instantiate and fit\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)\n",
    "\n",
    "# print the coefficients\n",
    "print (linreg.intercept_)\n",
    "print (linreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pair the feature names with the coefficients\n",
    "list(zip(feature_cols, linreg.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the coefficients:\n",
    "\n",
    "- Holding all other features fixed, a 1 unit increase in **temperature** is associated with a **rental increase of 7.86 bikes**.\n",
    "- Holding all other features fixed, a 1 unit increase in **season** is associated with a **rental increase of 22.5 bikes**.\n",
    "- Holding all other features fixed, a 1 unit increase in **weather** is associated with a **rental increase of 6.67 bikes**.\n",
    "- Holding all other features fixed, a 1 unit increase in **humidity** is associated with a **rental decrease of 3.12 bikes**.\n",
    "\n",
    "Does anything look incorrect?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection\n",
    "\n",
    "How do we choose which features to include in the model? We're going to use **train/test split** (and eventually **cross-validation**).\n",
    "\n",
    "Why not use of **p-values** or **R-squared** for feature selection?\n",
    "\n",
    "- Linear models rely upon **a lot of assumptions** (such as the features being independent), and if those assumptions are violated, p-values and R-squared are less reliable. Train/test split relies on fewer assumptions.\n",
    "- Features that are unrelated to the response can still have **significant p-values**.\n",
    "- Adding features to your model that are unrelated to the response will always **increase the R-squared value**, and adjusted R-squared does not sufficiently account for this.\n",
    "- p-values and R-squared are **proxies** for our goal of generalization, whereas train/test split and cross-validation attempt to **directly estimate** how well the model will generalize to out-of-sample data.\n",
    "\n",
    "More generally:\n",
    "\n",
    "- There are different methodologies that can be used for solving any given data science problem, and this course follows a **machine learning methodology**.\n",
    "- This course focuses on **general purpose approaches** that can be applied to any model, rather than model-specific approaches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics for regression problems\n",
    "\n",
    "Evaluation metrics for classification problems, such as **accuracy**, are not useful for regression problems. We need evaluation metrics designed for comparing **continuous values**.\n",
    "\n",
    "Here are three common evaluation metrics for regression problems:\n",
    "\n",
    "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$\n",
    "\n",
    "**Mean Squared Error** (MSE) is the mean of the squared errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$\n",
    "\n",
    "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n",
    "\n",
    "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# example true and predicted response values\n",
    "true = [10, 7, 5, 5]\n",
    "pred = [8, 6, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculate these metrics by hand!\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "print ('MAE:', metrics.mean_absolute_error(true, pred))\n",
    "print ('MSE:', metrics.mean_squared_error(true, pred))\n",
    "print ('RMSE:', np.sqrt(metrics.mean_squared_error(true, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing these metrics:\n",
    "\n",
    "- **MAE** is the easiest to understand, because it's the average error.\n",
    "- **MSE** is more popular than MAE, because MSE \"punishes\" larger errors, which tends to be useful in the real world.\n",
    "- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \"y\" units.\n",
    "\n",
    "All of these are **loss functions**, because we want to minimize them.\n",
    "\n",
    "Here's an additional example, to demonstrate how MSE/RMSE punish larger errors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# same true values as above\n",
    "true = [10, 7, 5, 5]\n",
    "\n",
    "# new set of predicted values\n",
    "pred = [10, 7, 5, 13]\n",
    "\n",
    "# MAE is the same as before\n",
    "print ('MAE:', metrics.mean_absolute_error(true, pred))\n",
    "\n",
    "# MSE and RMSE are larger than before\n",
    "print ('MSE:', metrics.mean_squared_error(true, pred))\n",
    "print ('RMSE:', np.sqrt(metrics.mean_squared_error(true, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing models with train/test split and RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# define a function that accepts a list of features and returns testing RMSE\n",
    "def train_test_rmse(feature_cols):\n",
    "    X = bikes[feature_cols]\n",
    "    y = bikes.total\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n",
    "    linreg = LinearRegression()\n",
    "    linreg.fit(X_train, y_train)\n",
    "    y_pred = linreg.predict(X_test)\n",
    "    return np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare different sets of features\n",
    "print (train_test_rmse(['temp', 'season', 'weather', 'humidity']))\n",
    "print (train_test_rmse(['temp', 'season', 'weather']))\n",
    "print (train_test_rmse(['temp', 'season', 'humidity']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# using these as features is not allowed!\n",
    "print (train_test_rmse(['casual', 'registered']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing testing RMSE with null RMSE\n",
    "\n",
    "Null RMSE is the RMSE that could be achieved by **always predicting the mean response value**. It is a benchmark against which you may want to measure your regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split X and y into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n",
    "\n",
    "# create a NumPy array with the same shape as y_test\n",
    "y_null = np.zeros_like(y_test, dtype=float)\n",
    "\n",
    "# fill the array with the mean value of y_test\n",
    "y_null.fill(y_test.mean())\n",
    "y_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compute null RMSE\n",
    "np.sqrt(metrics.mean_squared_error(y_test, y_null))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling categorical features\n",
    "\n",
    "scikit-learn expects all features to be numeric. So how do we include a categorical feature in our model?\n",
    "\n",
    "- **Ordered categories:** transform them to sensible numeric values (example: small=1, medium=2, large=3)\n",
    "- **Unordered categories:** use dummy encoding (0/1)\n",
    "\n",
    "What are the categorical features in our dataset?\n",
    "\n",
    "- **Ordered categories:** weather (already encoded with sensible numeric values)\n",
    "- **Unordered categories:** season (needs dummy encoding), holiday (already dummy encoded), workingday (already dummy encoded)\n",
    "\n",
    "For season, we can't simply leave the encoding as 1 = spring, 2 = summer, 3 = fall, and 4 = winter, because that would imply an **ordered relationship**. Instead, we create **multiple dummy variables:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create dummy variables\n",
    "season_dummies = pd.get_dummies(bikes.season, prefix='season')\n",
    "\n",
    "# print 5 random rows\n",
    "season_dummies.sample(n=5, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we actually only need **three dummy variables (not four)**, and thus we'll drop the first dummy variable.\n",
    "\n",
    "Why? Because three dummies captures all of the \"information\" about the season feature, and implicitly defines spring (season 1) as the **baseline level:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop the first column\n",
    "season_dummies.drop(season_dummies.columns[0], axis=1, inplace=True)\n",
    "\n",
    "# print 5 random rows\n",
    "season_dummies.sample(n=5, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, if you have a categorical feature with **k possible values**, you create **k-1 dummy variables**.\n",
    "\n",
    "If that's confusing, think about why we only need one dummy variable for holiday, not two dummy variables (holiday_yes and holiday_no)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# concatenate the original DataFrame and the dummy DataFrame (axis=0 means rows, axis=1 means columns)\n",
    "bikes = pd.concat([bikes, season_dummies], axis=1)\n",
    "\n",
    "# print 5 random rows\n",
    "bikes.sample(n=5, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('temp', 11.186405863575784),\n",
       " ('season_2', -3.3905430997179598),\n",
       " ('season_3', -41.736860713170678),\n",
       " ('season_4', 64.415961468242145),\n",
       " ('humidity', -2.819481636259646)]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# include dummy variables for season in the model\n",
    "feature_cols = ['temp', 'season_2', 'season_3', 'season_4', 'humidity']\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)\n",
    "list(zip(feature_cols, linreg.coef_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we interpret the season coefficients? They are **measured against the baseline (spring)**:\n",
    "\n",
    "- Holding all other features fixed, **summer** is associated with a **rental decrease of 3.39 bikes** compared to the spring.\n",
    "- Holding all other features fixed, **fall** is associated with a **rental decrease of 41.7 bikes** compared to the spring.\n",
    "- Holding all other features fixed, **winter** is associated with a **rental increase of 64.4 bikes** compared to the spring.\n",
    "\n",
    "Would it matter if we changed which season was defined as the baseline?\n",
    "\n",
    "- No, it would simply change our **interpretation** of the coefficients.\n",
    "\n",
    "**Important:** Dummy encoding is relevant for all machine learning models, not just linear regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare original season variable with dummy variables\n",
    "print (train_test_rmse(['temp', 'season', 'humidity']))\n",
    "print (train_test_rmse(['temp', 'season_2', 'season_3', 'season_4', 'humidity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "\n",
    "See if you can create the following features:\n",
    "\n",
    "- **hour:** as a single numeric feature (0 through 23)\n",
    "- **hour:** as a categorical feature (use 23 dummy variables)\n",
    "- **daytime:** as a single categorical feature (daytime=1 from 7am to 8pm, and daytime=0 otherwise)\n",
    "\n",
    "Then, try using each of the three features (on its own) with `train_test_rmse` to see which one performs the best!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hour as a numeric feature\n",
    "bikes['hour'] = bikes.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hour as a categorical feature\n",
    "hour_dummies = pd.get_dummies(bikes.hour, prefix='hour')\n",
    "hour_dummies.drop(hour_dummies.columns[0], axis=1, inplace=True)\n",
    "bikes = pd.concat([bikes, hour_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# daytime as a categorical feature\n",
    "bikes['daytime'] = ((bikes.hour > 6) & (bikes.hour < 21)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print (train_test_rmse(['hour']))\n",
    "print (train_test_rmse(bikes.columns[bikes.columns.str.startswith('hour_')]))\n",
    "print (train_test_rmse(['daytime']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advantages and Disdvantages\n",
    "\n",
    "Advantages of linear regression:\n",
    "\n",
    "- Simple to explain\n",
    "- Highly interpretable\n",
    "- Model training and prediction are fast\n",
    "- No tuning is required (excluding regularization)\n",
    "- Features don't need scaling\n",
    "- Can perform well with a small number of observations\n",
    "- Well-understood\n",
    "\n",
    "Disadvantages of linear regression:\n",
    "\n",
    "- Presumes a linear relationship between the features and the response\n",
    "- Performance is (generally) not competitive with the best supervised learning methods due to high bias\n",
    "- Can't automatically learn feature interactions"
   ]
  }
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