Car-MPG.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "from plotly import graph_objs as go\n",
    "init_notebook_mode(True)\n",
    "\n",
    "seed = 5000 # for the results which involve randomness\n",
    "import random\n",
    "random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "column_names = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', \\\n",
    "                'model_year', 'origin', 'car_name']\n",
    "df = pd.read_csv('auto-mpg.csv', delim_whitespace=True, names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement horsepower  weight  acceleration  model_year  \\\n",
       "0  18.0          8         307.0      130.0  3504.0          12.0          70   \n",
       "1  15.0          8         350.0      165.0  3693.0          11.5          70   \n",
       "2  18.0          8         318.0      150.0  3436.0          11.0          70   \n",
       "3  16.0          8         304.0      150.0  3433.0          12.0          70   \n",
       "4  17.0          8         302.0      140.0  3449.0          10.5          70   \n",
       "\n",
       "   origin                   car_name  \n",
       "0       1  chevrolet chevelle malibu  \n",
       "1       1          buick skylark 320  \n",
       "2       1         plymouth satellite  \n",
       "3       1              amc rebel sst  \n",
       "4       1                ford torino  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for missing values in _horsepower_ column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>98.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>ford pinto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>200.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2875.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>ford maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>40.9</td>\n",
       "      <td>4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>?</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>renault lecar deluxe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>23.6</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang cobra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>renault 18i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4</td>\n",
       "      <td>151.0</td>\n",
       "      <td>?</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>amc concord dl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
       "32   25.0          4          98.0          ?  2046.0          19.0   \n",
       "126  21.0          6         200.0          ?  2875.0          17.0   \n",
       "330  40.9          4          85.0          ?  1835.0          17.3   \n",
       "336  23.6          4         140.0          ?  2905.0          14.3   \n",
       "354  34.5          4         100.0          ?  2320.0          15.8   \n",
       "374  23.0          4         151.0          ?  3035.0          20.5   \n",
       "\n",
       "     model_year  origin              car_name  \n",
       "32           71       1            ford pinto  \n",
       "126          74       1         ford maverick  \n",
       "330          80       2  renault lecar deluxe  \n",
       "336          80       1    ford mustang cobra  \n",
       "354          81       2           renault 18i  \n",
       "374          82       1        amc concord dl  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horsepower_missing_idx = df[df.horsepower=='?'].index\n",
    "df.loc[horsepower_missing_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             float64\n",
       "cylinders         int64\n",
       "displacement    float64\n",
       "horsepower       object\n",
       "weight          float64\n",
       "acceleration    float64\n",
       "model_year        int64\n",
       "origin            int64\n",
       "car_name         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values in _horsepower_ will be replaced by the mean value of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[horsepower_missing_idx, 'horsepower'] = float('nan')\n",
    "df.horsepower = df.horsepower.apply(pd.to_numeric)\n",
    "df.loc[horsepower_missing_idx, 'horsepower'] = int( df.horsepower.mean() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display summary of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.51</td>\n",
       "      <td>5.45</td>\n",
       "      <td>193.43</td>\n",
       "      <td>104.46</td>\n",
       "      <td>2970.42</td>\n",
       "      <td>15.57</td>\n",
       "      <td>76.01</td>\n",
       "      <td>1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.82</td>\n",
       "      <td>1.70</td>\n",
       "      <td>104.27</td>\n",
       "      <td>38.20</td>\n",
       "      <td>846.84</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>1613.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>104.25</td>\n",
       "      <td>76.00</td>\n",
       "      <td>2223.75</td>\n",
       "      <td>13.83</td>\n",
       "      <td>73.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>148.50</td>\n",
       "      <td>95.00</td>\n",
       "      <td>2803.50</td>\n",
       "      <td>15.50</td>\n",
       "      <td>76.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>262.00</td>\n",
       "      <td>125.00</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>17.17</td>\n",
       "      <td>79.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.60</td>\n",
       "      <td>8.00</td>\n",
       "      <td>455.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>5140.00</td>\n",
       "      <td>24.80</td>\n",
       "      <td>82.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mpg  cylinders  displacement  horsepower   weight  acceleration  \\\n",
       "count  398.00     398.00        398.00      398.00   398.00        398.00   \n",
       "mean    23.51       5.45        193.43      104.46  2970.42         15.57   \n",
       "std      7.82       1.70        104.27       38.20   846.84          2.76   \n",
       "min      9.00       3.00         68.00       46.00  1613.00          8.00   \n",
       "25%     17.50       4.00        104.25       76.00  2223.75         13.83   \n",
       "50%     23.00       4.00        148.50       95.00  2803.50         15.50   \n",
       "75%     29.00       8.00        262.00      125.00  3608.00         17.17   \n",
       "max     46.60       8.00        455.00      230.00  5140.00         24.80   \n",
       "\n",
       "       model_year  origin  \n",
       "count      398.00  398.00  \n",
       "mean        76.01    1.57  \n",
       "std          3.70    0.80  \n",
       "min         70.00    1.00  \n",
       "25%         73.00    1.00  \n",
       "50%         76.00    1.00  \n",
       "75%         79.00    2.00  \n",
       "max         82.00    3.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('precision', 2)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split dataset into 260 instances for training, 58 instances for cross-validation and the remaining 80 instances for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train, val, test = np.split(df.sample(frac=1), [260, 318])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_list = ['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration']\n",
    "target = ['mpg']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-variate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform bivariate analysis to check for trends across features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (18, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aq2YUAwvXDY0AJVHM75Zt1vwQc2IcNioKU+j0jjM6EdiUGEREREREREQ2QjRn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mDOtXN2Y5SWQRITemRWpESEjjqE7jBRlrmUwexiibIJklGY2Il4FdQG/Ix36UjQiFEJ589N4N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Cwlp7hmm0VhlQXXbLMpt8EthWl3baDUQKHCtB+Tbm/k2c6v6yJuXiePHwG0JTDt/QyujMxF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\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pQog1QIGU8jPAI8CH7TRMoYiGeFQ8UNjDdENRY5VSxLs9FUKbfMTbZyL1MxvyCnPlBMX8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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52e373e5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.tools.plotting.scatter_matrix(df.loc[:, feature_list+target], diagonal=\"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insights:\n",
    "1. Based on the above plots, we can say that **mpg** is a strong function of **displacement, horsepower & weight**. Based on these insights, I'll experiment with models based on different input features\n",
    "2. The above graphs also suggest that relation between mpg & displacement, horsepower & weight could be of **polynomial function**\n",
    "3. The features displacement, horsepower & weight run high risk of being **autocorrelated** to each other, thus violating one of the assumptions of regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OLS with different sets of features¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will test the regression models with different set of features. Initially, I'll add those features with high correlation & check for change in MSE. Later, I'll add features with low correlation & monitor the MSE value. According to my hypothesis, features like acceleration & cylinders with low correlation will increase MSE value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ols_1_features = ['weight']\n",
    "ols_2_features = ols_1_features + ['horsepower']\n",
    "ols_3_features = ols_2_features + ['displacement']\n",
    "ols_4_features = ols_3_features + ['acceleration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create regression object\n",
    "ols_1 = LinearRegression(normalize=True)\n",
    "ols_2 = LinearRegression(normalize=True)\n",
    "ols_3 = LinearRegression(normalize=True)\n",
    "ols_4 = LinearRegression(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit models on train data\n",
    "ols_1.fit(train[ols_1_features], train[target])\n",
    "ols_2.fit(train[ols_2_features], train[target])\n",
    "ols_3.fit(train[ols_3_features], train[target])\n",
    "ols_4.fit(train[ols_4_features], train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE for ols_1 on validation set: 17.06\n",
      "MSE for ols_2 on validation set: 15.77\n",
      "MSE for ols_3 on validation set: 15.97\n",
      "MSE for ols_4 on validation set: 15.96\n"
     ]
    }
   ],
   "source": [
    "print \"MSE for ols_1 on validation set: {:.2f}\".format(mean_squared_error(val[target], \\\n",
    "                                                                          ols_1.predict(val[ols_1_features])))\n",
    "print \"MSE for ols_2 on validation set: {:.2f}\".format(mean_squared_error(val[target], \\\n",
    "                                                                          ols_2.predict(val[ols_2_features])))\n",
    "print \"MSE for ols_3 on validation set: {:.2f}\".format(mean_squared_error(val[target], \\\n",
    "                                                                          ols_3.predict(val[ols_3_features])))\n",
    "print \"MSE for ols_4 on validation set: {:.2f}\".format(mean_squared_error(val[target], \\\n",
    "                                                                          ols_4.predict(val[ols_4_features])))\n",
    "mse_val_ols = mean_squared_error(val[target], ols_3.predict(val[ols_3_features]))\n",
    "mse_test_ols = mean_squared_error(test[target], ols_3.predict(test[ols_3_features]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can infer from the error values, for our linear model, adding acceleration & cylinders will increase the error value due to absence of any correlation with mpg.\n",
    "<br>\n",
    "Thus, for OLS, we get the best possible model with the feature list of **weight, horsepower & displacement**\n",
    "\n",
    "***Next, I'll try to reduce the error term. Since the bivariate plots strongly show quadratic relation between the feature list & mpg, I'll introduce polynomial features and fit using OLS***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polynomial regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create polynomial features\n",
    "poly = PolynomialFeatures(2, include_bias=False)\n",
    "train_poly = poly.fit_transform(train[ols_3_features])\n",
    "val_poly = poly.fit_transform(val[ols_3_features])\n",
    "test_poly = poly.fit_transform(test[ols_3_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['weight',\n",
       " 'horsepower',\n",
       " 'displacement',\n",
       " 'weight^2',\n",
       " 'weight horsepower',\n",
       " 'weight displacement',\n",
       " 'horsepower^2',\n",
       " 'horsepower displacement',\n",
       " 'displacement^2']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print names of the new polynomial features created\n",
    "poly.get_feature_names(ols_3_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE for polynomial regression on validation dataset is: 14.123720\n"
     ]
    }
   ],
   "source": [
    "polyReg_1 = LinearRegression(normalize=True)\n",
    "polyReg_1.fit(train_poly, train[target])\n",
    "mse_val_poly = mean_squared_error(val[target], polyReg_1.predict(val_poly))\n",
    "mse_test_poly = mean_squared_error(test[target], polyReg_1.predict(test_poly))\n",
    "print \"MSE for polynomial regression on validation dataset is: {:f}\".format(mse_val_poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** \n",
    "<br>\n",
    "***Reduction of value 3 in MSE!***\n",
    "Next, I'll try to reduce overfitting by fitting these polynomial features through ridge regression \n",
    "<br>\n",
    "Since we are using polynomial features, it becomes important to check for overfitting. One way to do so is by using Ridge regression which limits the influence of any individual feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge regression for polynomial features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ridge regression tries to prevent overfitting of models by limiting the value of weights using l2 penalty. Optimal value of l2 penalty is arrived at by searching between 1e-12 and 1e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ridge_validation_error(l2_penalty, train_data, val_data, train_target, val_target):\n",
    "    \n",
    "#     X = train_data[feature_list]\n",
    "#     y = train_data[target]\n",
    "    model = Ridge(alpha=l2_penalty, normalize=True)\n",
    "    model.fit(train_data, train_target)\n",
    "    \n",
    "    # Compute the validation error on validation set\n",
    "    \n",
    "#     predictions = model.predict(val_data[feature_list])\n",
    "    error = mean_squared_error(val_target, model.predict(val_data))\n",
    "    \n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On cross-validation, ridge regression with a L2 penalty of 0.00316228 was observed to get the minimum possible \n",
      "MSE of 13.711602\n"
     ]
    }
   ],
   "source": [
    "min_error = float('inf')\n",
    "allErrors = [] # for plotting purposes\n",
    "\n",
    "\n",
    "# iterate over different L2 values for ridge regression\n",
    "for l2_penalty in np.logspace(-10, 5, 13):\n",
    "    error = ridge_validation_error(l2_penalty, train_poly, val_poly, train[target], val[target])\n",
    "    allErrors.append(error)\n",
    "    if error < min_error: \n",
    "        min_error = error\n",
    "        optimal_l2_penalty = l2_penalty\n",
    "mse_val_ridge = min_error\n",
    "print 'On cross-validation, ridge regression with a L2 penalty of {:g} was observed to \\\n",
    "get the minimum possible \\nMSE of {:f}'.format(optimal_l2_penalty, mse_val_ridge)\n",
    "\n",
    "# save test mse\n",
    "model = Ridge(alpha=optimal_l2_penalty, normalize=True)\n",
    "model.fit(train_poly, train[target])\n",
    "mse_test_ridge = mean_squared_error(test[target], model.predict(test_poly))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot prediction accuracy achieved by the parameter combinations tried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (6, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8LPhXVpagvKws43b4rzy8n0hQXl520rpX3w4eP/lIJ21HO088PnydiooyqirK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\njAq/iEjM/H+OsmViL8sblAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52e02a9ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the different l2_penalty values tried in the x axis and the validation error in the y axis.\n",
    "plt.figure()\n",
    "plt.plot(np.logspace(-10, 5, 13), allErrors, '-')\n",
    "plt.title(\"Errors vs L2 Penalty\")\n",
    "plt.xlabel(\"Penalty\")\n",
    "plt.ylabel(\"Errors\")\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Ridge regression didn't lead to any noticeable improvement in loss term. This indicates that our model is not overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def decisionTree_val_error(train_data, val_data, train_target, val_target, max_depth):\n",
    "    dtRegressor = tree.DecisionTreeRegressor(random_state=seed, max_depth = max_depth)\n",
    "    dtRegressor = dtRegressor.fit(train_data, train_target)\n",
    "    error = mean_squared_error(val[target], dtRegressor.predict(val_data))\n",
    "    \n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On cross-validation, decision tree with a depth of 6 was observed to get the minimum possible mean squared error of 15.78\n"
     ]
    }
   ],
   "source": [
    "min_error = float('inf')\n",
    "allErrors = [] # for plotting purposes\n",
    "\n",
    "# iterate over different depth values of decision tree\n",
    "for max_depth in range(2,20):\n",
    "    error = decisionTree_val_error(train[ols_3_features], val[ols_3_features], \\\n",
    "                                   train[target], val[target], max_depth)\n",
    "    allErrors.append(error)\n",
    "    if error < min_error: \n",
    "        min_error = error\n",
    "        optimal_max_depth = max_depth\n",
    "print 'On cross-validation, decision tree with a depth of {} was observed to \\\n",
    "get the minimum possible mean squared error of {:.2f}'.format(optimal_max_depth, min_error)\n",
    "mse_val_dt = min_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store test MSE\n",
    "model = tree.DecisionTreeRegressor(random_state=seed, max_depth = optimal_max_depth)\n",
    "model.fit(train[ols_3_features], train[target])\n",
    "mse_test_dt = mean_squared_error(test[target], model.predict(test[ols_3_features])) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot prediction accuracy achieved by the parameter combinations tried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mSHSn8o3Ak8C7gfcCfzfGfCCBp/YBFwP7Y25bCrzkXH4UWJ1wtC5r6+yfVNnr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\nUsqhCUEppRSgCUEppZTj/wPY4Dola/WtXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52dfcf62d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the different tree depth values tried in the x axis and the validation error in the y axis.\n",
    "plt.figure()\n",
    "plt.plot(range(2,20), allErrors, '-')\n",
    "plt.title(\"Errors vs Tree Depth\")\n",
    "plt.xlabel(\"Tree Depth\")\n",
    "plt.ylabel(\"Errors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rf_val_error(train_data, val_data, train_target, val_target, n_estimators):\n",
    "    \n",
    "    rfRegressor = RandomForestRegressor(random_state=seed, n_estimators = n_estimators)\n",
    "    rfRegressor = rfRegressor.fit(train_data, train_target)\n",
    "    error = mean_squared_error(val[target], rfRegressor.predict(val_data))\n",
    "    \n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On cross-validation, random forest with 10 trees was observed to get the minimum possible mean squared error of 13.68\n"
     ]
    }
   ],
   "source": [
    "min_error = float('inf')\n",
    "allErrors = [] # for plotting purposes\n",
    "\n",
    "# iterate over different number of trees in random forest\n",
    "for n_tree in range(5,50,5):\n",
    "    error = rf_val_error(train[ols_3_features], val[ols_3_features], train[target], val[target], n_tree)\n",
    "    allErrors.append(error)\n",
    "    if error < min_error: \n",
    "        min_error = error\n",
    "        optimal_n_tree = n_tree\n",
    "print 'On cross-validation, random forest with {} trees was observed to \\\n",
    "get the minimum possible mean squared error of {:.2f}'.format(optimal_n_tree, min_error)\n",
    "mse_val_rf = min_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Store test mse\n",
    "final_rf_model = RandomForestRegressor(random_state=seed, n_estimators = optimal_n_tree)\n",
    "final_rf_model.fit(train[ols_3_features], train[target])\n",
    "mse_test_rf = mean_squared_error(test[target], final_rf_model.predict(test[ols_3_features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8kTVs3XWQuZLJ96+c7bfbl48mNiaKK8+axC2XzCAuJopHXlJ+/9wmDjW1Bvzc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\nMcYn/x8DDKrtuwxd/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52dfe7a810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the different number of trees tried in the x axis and the validation error in the y axis.\n",
    "plt.figure()\n",
    "plt.plot(range(5,50,5), allErrors, '-')\n",
    "plt.title(\"Errors vs #Trees\")\n",
    "plt.xlabel(\"#Trees\")\n",
    "plt.ylabel(\"Errors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** All the trees trained in random forests are independent of each other. An ensemble of trees is trained using bootstrapp sampled data. Thus we see an improvement in accuracy over Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gbm_val_error(train_data, val_data, train_target, val_target, n_estimators):\n",
    "    \n",
    "    gbmRegressor = GradientBoostingRegressor(random_state=seed, n_estimators = n_estimators)\n",
    "    gbmRegressor = gbmRegressor.fit(train_data, train_target)\n",
    "    error = mean_squared_error(val[target], gbmRegressor.predict(val_data))\n",
    "    \n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On cross-validation, GBM with 55 sequential trees was observed to get the minimum possible mean squared error \n",
      "of 13.19\n"
     ]
    }
   ],
   "source": [
    "min_error = float('inf')\n",
    "allErrors = [] # for plotting purposes\n",
    "\n",
    "# iterate over different number of trees in random forest\n",
    "for n_estimators in range(5,70,5):\n",
    "    error = gbm_val_error(train[ols_3_features], val[ols_3_features], train[target], val[target], n_estimators)\n",
    "    allErrors.append(error)\n",
    "    if error < min_error: \n",
    "        min_error = error\n",
    "        optimal_n_estimators = n_estimators\n",
    "print 'On cross-validation, GBM with {} sequential trees was observed to \\\n",
    "get the minimum possible mean squared error \\nof {:.2f}'.format(optimal_n_estimators, min_error)\n",
    "mse_val_gbm = min_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Store test mse\n",
    "final_gbm_model = GradientBoostingRegressor(random_state=seed, n_estimators = 45)\n",
    "final_gbm_model = final_gbm_model.fit(train[ols_3_features], train[target])\n",
    "mse_test_gbm = mean_squared_error(test[target], final_gbm_model.predict(test[ols_3_features]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot prediction accuracy achieved by the parameter combinations tried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4sJk9DJwEXB5lDOPdlJoyLjzzAPaaPYnnX1/D5b94RtNuisiIJPLgjdd0vhfb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\nAeD/A+cOYqrSpo7xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f52dfed5e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the different number of boosting stages tried in the x axis and the validation error in the y axis.\n",
    "plt.figure()\n",
    "plt.plot(range(5,70,5), allErrors, '-')\n",
    "plt.title(\"Errors vs #Boosting Stages\")\n",
    "plt.xlabel(\"#Boosting Stages\")\n",
    "plt.ylabel(\"Errors\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analysis:** Consequent tree is trained so as to reduce the error of the previous tree. But in Random Forest, all the trees are independent. Thus GBM increases the accuracy of our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using Random forest & GBM, lower mean squared error was achieved. An ensemble of both these methods can be used as the final solution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating validation & test predictions for ensemble model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate predictions\n",
    "val_pred_ensemble = np.mean([final_rf_model.predict(val[ols_3_features]), \\\n",
    "                            final_gbm_model.predict(val[ols_3_features])], axis=0)\n",
    "test_pred_ensemble = np.mean([final_rf_model.predict(test[ols_3_features]), \\\n",
    "                            final_gbm_model.predict(test[ols_3_features])], axis=0)\n",
    "\n",
    "# Calculate MSE\n",
    "mse_val_ensemble = mean_squared_error(val[target], val_pred_ensemble)\n",
    "mse_test_ensemble = mean_squared_error(test[target], test_pred_ensemble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSE on Validation dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall performance plot on validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "OLS",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          15.9654
         ]
        },
        {
         "name": "Poly",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          14.1237
         ]
        },
        {
         "name": "Ridge",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          13.7116
         ]
        },
        {
         "name": "Decision Tree",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          15.7796
         ]
        },
        {
         "name": "RF",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          13.6769
         ]
        },
        {
         "name": "GBM",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          13.1937
         ]
        },
        {
         "name": "RF_GBM",
         "type": "bar",
         "x": [
          "Validation MSE"
         ],
         "y": [
          13.033
         ]
        }
       ],
       "layout": {
        "title": "Regression Algorithms Performance on Validation set- MSE",
        "xaxis": {
         "title": "Algorithms"
        },
        "yaxis": {
         "title": "Metric Score"
        }
       }
      },
      "text/html": [
       "<div id=\"454b9c6e-e164-4235-96fd-079d7c4146f4\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"454b9c6e-e164-4235-96fd-079d7c4146f4\", [{\"y\": [15.9654], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"OLS\"}, {\"y\": [14.1237], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"Poly\"}, {\"y\": [13.7116], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"Ridge\"}, {\"y\": [15.7796], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"Decision Tree\"}, {\"y\": [13.6769], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"RF\"}, {\"y\": [13.1937], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"GBM\"}, {\"y\": [13.033], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"RF_GBM\"}], {\"yaxis\": {\"title\": \"Metric Score\"}, \"xaxis\": {\"title\": \"Algorithms\"}, \"title\": \"Regression Algorithms Performance on Validation set- MSE\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"454b9c6e-e164-4235-96fd-079d7c4146f4\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"454b9c6e-e164-4235-96fd-079d7c4146f4\", [{\"y\": [15.9654], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"OLS\"}, {\"y\": [14.1237], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"Poly\"}, {\"y\": [13.7116], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"Ridge\"}, {\"y\": [15.7796], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"Decision Tree\"}, {\"y\": [13.6769], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"RF\"}, {\"y\": [13.1937], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"GBM\"}, {\"y\": [13.033], \"x\": [\"Validation MSE\"], \"type\": \"bar\", \"name\": \"RF_GBM\"}], {\"yaxis\": {\"title\": \"Metric Score\"}, \"xaxis\": {\"title\": \"Algorithms\"}, \"title\": \"Regression Algorithms Performance on Validation set- MSE\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mse_val = pd.DataFrame({'Algo': ['OLS', 'Poly', 'Ridge', 'Decision Tree', 'RF', 'GBM', 'RF_GBM'],\\\n",
    "                            'Score':[mse_val_ols, mse_val_poly, mse_val_ridge, mse_val_dt, \\\n",
    "                                     mse_val_rf, mse_val_gbm, mse_val_ensemble], \\\n",
    "                            'Metric':'Validation MSE'})\n",
    "mse_val.Score = mse_val.Score.apply(lambda x: round(x,4))\n",
    "\n",
    "mse_val_data = []\n",
    "for algo in mse_val.Algo.unique():\n",
    "    temp1 = go.Bar(\n",
    "    x = mse_val.Metric.unique().tolist(),\n",
    "    y = mse_val[mse_val.Algo==algo].Score.tolist(),\n",
    "    name= algo\n",
    "    )\n",
    "    mse_val_data.append(temp1)\n",
    "    \n",
    "iplot({\n",
    "        'data':mse_val_data\n",
    "        ,'layout':go.Layout(\n",
    "                title='Regression Algorithms Performance on Validation set- MSE'\n",
    "                ,xaxis={'title': 'Algorithms'}\n",
    "                ,yaxis={'title': 'Metric Score'} \n",
    "        )\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MSE on Test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall performance plot on test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "OLS",
         "type": "bar",
         "x": [
          "Test MSE"
         ],
         "y": [
          21.9214
         ]
        },
        {
         "name": "Poly",
         "type": "bar",
         "x": [
          "Test MSE"
         ],
         "y": [
          20.1567
         ]
        },
        {
         "name": "Ridge",
         "type": "bar",
         "x": [
          "Test MSE"
         ],
         "y": [
          19.5233
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       "<div id=\"618baf24-773d-4015-8e32-1c173ef12b5a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"618baf24-773d-4015-8e32-1c173ef12b5a\", [{\"y\": [21.9214], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"OLS\"}, {\"y\": [20.1567], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"Poly\"}, {\"y\": [19.5233], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"Ridge\"}, {\"y\": [27.8749], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"Decision Tree\"}, {\"y\": [22.4072], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"RF\"}, {\"y\": [18.9376], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"GBM\"}, {\"y\": [20.0732], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"RF_GBM\"}], {\"yaxis\": {\"title\": \"Metric Score\"}, \"xaxis\": {\"title\": \"Algorithms\"}, \"title\": \"Regression Algorithms Performance on Test set- MSE\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
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       "<div id=\"618baf24-773d-4015-8e32-1c173ef12b5a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"618baf24-773d-4015-8e32-1c173ef12b5a\", [{\"y\": [21.9214], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"OLS\"}, {\"y\": [20.1567], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"Poly\"}, {\"y\": [19.5233], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"Ridge\"}, {\"y\": [27.8749], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"Decision Tree\"}, {\"y\": [22.4072], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"RF\"}, {\"y\": [18.9376], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"GBM\"}, {\"y\": [20.0732], \"x\": [\"Test MSE\"], \"type\": \"bar\", \"name\": \"RF_GBM\"}], {\"yaxis\": {\"title\": \"Metric Score\"}, \"xaxis\": {\"title\": \"Algorithms\"}, \"title\": \"Regression Algorithms Performance on Test set- MSE\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
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   "source": [
    "mse_test = pd.DataFrame({'Algo': ['OLS', 'Poly', 'Ridge', 'Decision Tree', 'RF', 'GBM', 'RF_GBM'],\\\n",
    "                            'Score':[mse_test_ols, mse_test_poly, mse_test_ridge, mse_test_dt, \\\n",
    "                                     mse_test_rf, mse_test_gbm, mse_test_ensemble], \\\n",
    "                            'Metric':'Test MSE'})\n",
    "mse_test.Score = mse_test.Score.apply(lambda x: round(x,4))\n",
    "\n",
    "mse_test_data = []\n",
    "for algo in mse_val.Algo.unique():\n",
    "    temp2 = go.Bar(\n",
    "    x = mse_test.Metric.unique().tolist(),\n",
    "    y = mse_test[mse_test.Algo==algo].Score.tolist(),\n",
    "    name= algo\n",
    "    )\n",
    "    mse_test_data.append(temp2)\n",
    "    \n",
    "iplot({\n",
    "        'data':mse_test_data\n",
    "        ,'layout':go.Layout(\n",
    "                title='Regression Algorithms Performance on Test set- MSE'\n",
    "                ,xaxis={'title': 'Algorithms'}\n",
    "                ,yaxis={'title': 'Metric Score'} \n",
    "        )\n",
    "    })"
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