LifeExpectancy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### DataSet Link: https://www.kaggle.com/augustus0498/life-expectancy-who"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About the dataset - Regression\n",
    "* The Global Health Observatory (GHO)data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. Analyzing this data will help to predict life expectancy based on health factors for 193 countries.\n",
    "## Input variables explained:\n",
    "### Life Expectancy (WHO) <br>\n",
    "1 - Country: 193 Countries (Categorical)<br>\n",
    "2 - Year: from 2000 to 2015 years (Numerical)<br>\n",
    "3 - Status: Country is Developed or Developing (Categorical)<br>\n",
    "4 - AdultMortality: Adult Mortality Rates of both sexes (probability of dying between 15 and 60 years per 1000 population (Numerical)<br>\n",
    "5 - infantdeaths: Number of Infant Deaths per 1000 population (Numerical)<br>\n",
    "6 - Alcohol: Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol) (Numerical)<br>\n",
    "7 - percentageexpenditure: Expenditure on health as a percentage of Gross Domestic Product per capita(%) (Numerical)<br>\n",
    "8 - Hepatitis: BHepatitis B (HepB) immunization coverage among 1-year-olds (%) (Numerical)<br>\n",
    "9 - Measles: Measles - number of reported cases per 1000 population (Numerical)<br>\n",
    "10 - BMI: Average Body Mass Index of entire population (Numerical)<br>\n",
    "11 - Under-fivedeaths: Number of under-five deaths per 1000 population (Numerical)<br>\n",
    "12 - Polio: Polio (Pol3) immunization coverage among 1-year-olds (%) (Numerical)<br>\n",
    "13 - Totalexpenditure: General government expenditure on health as a percentage of total government expenditure (%) (Numerical)<br>\n",
    "14 - Diphtheria: Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%) (Numerical)<br>\n",
    "15 - HIV/AIDS: Deaths per 1000 live births HIV/AIDS - 0-4 years (Numerical)<br>\n",
    "16 - GDP: Gross Domestic Product per capita in USD (Numerical)<br>\n",
    "17 - Population: Population of the country (Numerical)<br>\n",
    "18 - thinness1-19years: Prevalence of thinness among children and adolescents for Age 10 to 19 (% ) (Numerical)<br>\n",
    "19 - thinness5-9years: Prevalence of thinness among children for Age 5 to 9(%) (Numerical)<br>\n",
    "20 - Incomecompositionofresources: Human Development Index in terms of income composition of resources - index ranging from 0 to 1 (Numerical)<br>\n",
    "21 - Schooling: Number of years of Schooling in years (Numerical)<br>\n",
    "## Output variables explained ( Target Variable):\n",
    "22 - Lifeexpectancy: Life Expectancy in age (Numerical)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approach\n",
    "In order to optimize life expectency with the help of the dataset, we will have to take the following steps:\n",
    "* Import data from dataset and perform initial high-level analysis: look at the number of rows, look at the missing values, look at dataset columns and their values respective to target value\n",
    "* Clean the data: remove irrelevant columns, deal with missing and incorrect values, turn categorical columns into dummy variables.\n",
    "* Here, Year is consider as categorical variable though values is numrical as Year is absolute value\n",
    "* Use machine learning techniques to predict the life expectancy and to find out factors, which affect the life expectancy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import data and required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      "Country                         2938 non-null object\n",
      "Year                            2938 non-null int64\n",
      "Status                          2938 non-null object\n",
      "Lifeexpectancy                  2928 non-null float64\n",
      "AdultMortality                  2928 non-null float64\n",
      "infantdeaths                    2938 non-null int64\n",
      "Alcohol                         2744 non-null float64\n",
      "percentageexpenditure           2938 non-null float64\n",
      "HepatitisB                      2385 non-null float64\n",
      "Measles                         2938 non-null int64\n",
      "BMI                             2904 non-null float64\n",
      "under-fivedeaths                2938 non-null int64\n",
      "Polio                           2919 non-null float64\n",
      "Totalexpenditure                2712 non-null float64\n",
      "Diphtheria                      2919 non-null float64\n",
      "HIV/AIDS                        2938 non-null float64\n",
      "GDP                             2490 non-null float64\n",
      "Population                      2286 non-null float64\n",
      "thinness1-19years               2904 non-null float64\n",
      "thinness5-9years                2904 non-null float64\n",
      "Incomecompositionofresources    2771 non-null float64\n",
      "Schooling                       2775 non-null float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 505.0+ KB\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Lifeexpectancy</th>\n",
       "      <th>AdultMortality</th>\n",
       "      <th>infantdeaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentageexpenditure</th>\n",
       "      <th>HepatitisB</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Totalexpenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness1-19years</th>\n",
       "      <th>thinness5-9years</th>\n",
       "      <th>Incomecompositionofresources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 22 columns</p>\n",
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      "text/plain": [
       "       Country  Year      Status  Lifeexpectancy  AdultMortality  \\\n",
       "0  Afghanistan  2015  Developing            65.0           263.0   \n",
       "1  Afghanistan  2014  Developing            59.9           271.0   \n",
       "2  Afghanistan  2013  Developing            59.9           268.0   \n",
       "3  Afghanistan  2012  Developing            59.5           272.0   \n",
       "4  Afghanistan  2011  Developing            59.2           275.0   \n",
       "\n",
       "   infantdeaths  Alcohol  percentageexpenditure  HepatitisB  Measles  ...  \\\n",
       "0            62     0.01              71.279624        65.0     1154  ...   \n",
       "1            64     0.01              73.523582        62.0      492  ...   \n",
       "2            66     0.01              73.219243        64.0      430  ...   \n",
       "3            69     0.01              78.184215        67.0     2787  ...   \n",
       "4            71     0.01               7.097109        68.0     3013  ...   \n",
       "\n",
       "   Polio  Totalexpenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n",
       "0    6.0              8.16        65.0       0.1  584.259210  33736494.0   \n",
       "1   58.0              8.18        62.0       0.1  612.696514    327582.0   \n",
       "2   62.0              8.13        64.0       0.1  631.744976  31731688.0   \n",
       "3   67.0              8.52        67.0       0.1  669.959000   3696958.0   \n",
       "4   68.0              7.87        68.0       0.1   63.537231   2978599.0   \n",
       "\n",
       "   thinness1-19years  thinness5-9years  Incomecompositionofresources  \\\n",
       "0               17.2              17.3                         0.479   \n",
       "1               17.5              17.5                         0.476   \n",
       "2               17.7              17.7                         0.470   \n",
       "3               17.9              18.0                         0.463   \n",
       "4               18.2              18.2                         0.454   \n",
       "\n",
       "   Schooling  \n",
       "0       10.1  \n",
       "1       10.0  \n",
       "2        9.9  \n",
       "3        9.8  \n",
       "4        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Wed Mar 25 00:41:04 2020\n",
    "\n",
    "@author: kdpat\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from  sklearn.preprocessing  import PolynomialFeatures\n",
    "from  sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.svm import SVR, LinearSVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    " \n",
    "data = pd.read_csv('C:/Users/kdpat/Downloads/life-expectancy-who/led.csv')\n",
    "A=data.head(1000)\n",
    "data.info()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. DATA PREPROCESSING\n",
    "### Data Variation and Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Lifeexpectancy</th>\n",
       "      <th>AdultMortality</th>\n",
       "      <th>infantdeaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentageexpenditure</th>\n",
       "      <th>HepatitisB</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-fivedeaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Totalexpenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness1-19years</th>\n",
       "      <th>thinness5-9years</th>\n",
       "      <th>Incomecompositionofresources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2744.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2385.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2712.00000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>2.286000e+03</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2771.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.224932</td>\n",
       "      <td>164.796448</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.321247</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.324084</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>4.839704</td>\n",
       "      <td>4.870317</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.523867</td>\n",
       "      <td>124.292079</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>4.052413</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>20.044034</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.428046</td>\n",
       "      <td>2.49832</td>\n",
       "      <td>23.716912</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>14270.169342</td>\n",
       "      <td>6.101210e+07</td>\n",
       "      <td>4.420195</td>\n",
       "      <td>4.508882</td>\n",
       "      <td>0.210904</td>\n",
       "      <td>3.358920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.100000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.26000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>463.935626</td>\n",
       "      <td>1.957932e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.493000</td>\n",
       "      <td>10.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.75500</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.702500</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.200000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.49250</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5910.806335</td>\n",
       "      <td>7.420359e+06</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>17.60000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Lifeexpectancy  AdultMortality  infantdeaths      Alcohol  \\\n",
       "count  2938.000000     2928.000000     2928.000000   2938.000000  2744.000000   \n",
       "mean   2007.518720       69.224932      164.796448     30.303948     4.602861   \n",
       "std       4.613841        9.523867      124.292079    117.926501     4.052413   \n",
       "min    2000.000000       36.300000        1.000000      0.000000     0.010000   \n",
       "25%    2004.000000       63.100000       74.000000      0.000000     0.877500   \n",
       "50%    2008.000000       72.100000      144.000000      3.000000     3.755000   \n",
       "75%    2012.000000       75.700000      228.000000     22.000000     7.702500   \n",
       "max    2015.000000       89.000000      723.000000   1800.000000    17.870000   \n",
       "\n",
       "       percentageexpenditure   HepatitisB        Measles          BMI  \\\n",
       "count            2938.000000  2385.000000    2938.000000  2904.000000   \n",
       "mean              738.251295    80.940461    2419.592240    38.321247   \n",
       "std              1987.914858    25.070016   11467.272489    20.044034   \n",
       "min                 0.000000     1.000000       0.000000     1.000000   \n",
       "25%                 4.685343    77.000000       0.000000    19.300000   \n",
       "50%                64.912906    92.000000      17.000000    43.500000   \n",
       "75%               441.534144    97.000000     360.250000    56.200000   \n",
       "max             19479.911610    99.000000  212183.000000    87.300000   \n",
       "\n",
       "       under-fivedeaths        Polio  Totalexpenditure   Diphtheria  \\\n",
       "count       2938.000000  2919.000000        2712.00000  2919.000000   \n",
       "mean          42.035739    82.550188           5.93819    82.324084   \n",
       "std          160.445548    23.428046           2.49832    23.716912   \n",
       "min            0.000000     3.000000           0.37000     2.000000   \n",
       "25%            0.000000    78.000000           4.26000    78.000000   \n",
       "50%            4.000000    93.000000           5.75500    93.000000   \n",
       "75%           28.000000    97.000000           7.49250    97.000000   \n",
       "max         2500.000000    99.000000          17.60000    99.000000   \n",
       "\n",
       "          HIV/AIDS            GDP    Population  thinness1-19years  \\\n",
       "count  2938.000000    2490.000000  2.286000e+03        2904.000000   \n",
       "mean      1.742103    7483.158469  1.275338e+07           4.839704   \n",
       "std       5.077785   14270.169342  6.101210e+07           4.420195   \n",
       "min       0.100000       1.681350  3.400000e+01           0.100000   \n",
       "25%       0.100000     463.935626  1.957932e+05           1.600000   \n",
       "50%       0.100000    1766.947595  1.386542e+06           3.300000   \n",
       "75%       0.800000    5910.806335  7.420359e+06           7.200000   \n",
       "max      50.600000  119172.741800  1.293859e+09          27.700000   \n",
       "\n",
       "       thinness5-9years  Incomecompositionofresources    Schooling  \n",
       "count       2904.000000                   2771.000000  2775.000000  \n",
       "mean           4.870317                      0.627551    11.992793  \n",
       "std            4.508882                      0.210904     3.358920  \n",
       "min            0.100000                      0.000000     0.000000  \n",
       "25%            1.500000                      0.493000    10.100000  \n",
       "50%            3.300000                      0.677000    12.300000  \n",
       "75%            7.200000                      0.779000    14.300000  \n",
       "max           28.600000                      0.948000    20.700000  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938</td>\n",
       "      <td>2938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>193</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>Developing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>16</td>\n",
       "      <td>2426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Country      Status\n",
       "count           2938        2938\n",
       "unique           193           2\n",
       "top     Turkmenistan  Developing\n",
       "freq              16        2426"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Counts</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haiti</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kazakhstan</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mauritania</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gambia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DemocraticPeople'sRepublicofKorea</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TheformerYugoslavrepublicofMacedonia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AntiguaandBarbuda</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kiribati</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nepal</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rwanda</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madagascar</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenya</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montenegro</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UnitedArabEmirates</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Somalia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swaziland</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maldives</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eritrea</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElSalvador</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SouthAfrica</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BosniaandHerzegovina</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran(IslamicRepublicof)</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mongolia</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ecuador</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seychelles</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RussianFederation</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guatemala</th>\n",
       "      <td>16</td>\n",
       "      <td>0.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MarshallIslands</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palau</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SanMarino</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nauru</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monaco</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaintKittsandNevis</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CookIslands</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niue</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuvalu</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominica</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Counts Percent\n",
       "Turkmenistan                              16    0.5%\n",
       "Azerbaijan                                16    0.5%\n",
       "Burundi                                   16    0.5%\n",
       "Kuwait                                    16    0.5%\n",
       "Ireland                                   16    0.5%\n",
       "Poland                                    16    0.5%\n",
       "Haiti                                     16    0.5%\n",
       "Romania                                   16    0.5%\n",
       "Kazakhstan                                16    0.5%\n",
       "Bahrain                                   16    0.5%\n",
       "Mauritania                                16    0.5%\n",
       "Gambia                                    16    0.5%\n",
       "Lebanon                                   16    0.5%\n",
       "DemocraticPeople'sRepublicofKorea         16    0.5%\n",
       "Timor-Leste                               16    0.5%\n",
       "TheformerYugoslavrepublicofMacedonia      16    0.5%\n",
       "AntiguaandBarbuda                         16    0.5%\n",
       "Kiribati                                  16    0.5%\n",
       "Nepal                                     16    0.5%\n",
       "Rwanda                                    16    0.5%\n",
       "Madagascar                                16    0.5%\n",
       "Kenya                                     16    0.5%\n",
       "Montenegro                                16    0.5%\n",
       "Yemen                                     16    0.5%\n",
       "UnitedArabEmirates                        16    0.5%\n",
       "Somalia                                   16    0.5%\n",
       "Croatia                                   16    0.5%\n",
       "Turkey                                    16    0.5%\n",
       "Czechia                                   16    0.5%\n",
       "Barbados                                  16    0.5%\n",
       "...                                      ...     ...\n",
       "Swaziland                                 16    0.5%\n",
       "Israel                                    16    0.5%\n",
       "Maldives                                  16    0.5%\n",
       "Eritrea                                   16    0.5%\n",
       "Bulgaria                                  16    0.5%\n",
       "ElSalvador                                16    0.5%\n",
       "Argentina                                 16    0.5%\n",
       "Mexico                                    16    0.5%\n",
       "SouthAfrica                               16    0.5%\n",
       "BosniaandHerzegovina                      16    0.5%\n",
       "Denmark                                   16    0.5%\n",
       "Indonesia                                 16    0.5%\n",
       "Iran(IslamicRepublicof)                   16    0.5%\n",
       "Uzbekistan                                16    0.5%\n",
       "Bhutan                                    16    0.5%\n",
       "Mongolia                                  16    0.5%\n",
       "Ecuador                                   16    0.5%\n",
       "Seychelles                                16    0.5%\n",
       "RussianFederation                         16    0.5%\n",
       "Guatemala                                 16    0.5%\n",
       "MarshallIslands                            1    0.0%\n",
       "Palau                                      1    0.0%\n",
       "SanMarino                                  1    0.0%\n",
       "Nauru                                      1    0.0%\n",
       "Monaco                                     1    0.0%\n",
       "SaintKittsandNevis                         1    0.0%\n",
       "CookIslands                                1    0.0%\n",
       "Niue                                       1    0.0%\n",
       "Tuvalu                                     1    0.0%\n",
       "Dominica                                   1    0.0%\n",
       "\n",
       "[193 rows x 2 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S=data.Country\n",
    "Counts=S.value_counts()\n",
    "Percent=S.value_counts(normalize=True).mul(100).round(1).astype(str)+'%'\n",
    "pd.DataFrame({'Counts':Counts,'Percent':Percent})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Counts</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Developing</th>\n",
       "      <td>2426</td>\n",
       "      <td>82.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Developed</th>\n",
       "      <td>512</td>\n",
       "      <td>17.4%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Counts Percent\n",
       "Developing    2426   82.6%\n",
       "Developed      512   17.4%"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S=data.Status\n",
    "Counts=S.value_counts()\n",
    "Percent=S.value_counts(normalize=True).mul(100).round(1).astype(str)+'%'\n",
    "pd.DataFrame({'Counts':Counts,'Percent':Percent})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,15))\n",
    "ax = fig.gca()\n",
    "data.hist(ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                           0\n",
       "Year                              0\n",
       "Status                            0\n",
       "Lifeexpectancy                   10\n",
       "AdultMortality                   10\n",
       "infantdeaths                      0\n",
       "Alcohol                         194\n",
       "percentageexpenditure             0\n",
       "HepatitisB                      553\n",
       "Measles                           0\n",
       "BMI                              34\n",
       "under-fivedeaths                  0\n",
       "Polio                            19\n",
       "Totalexpenditure                226\n",
       "Diphtheria                       19\n",
       "HIV/AIDS                          0\n",
       "GDP                             448\n",
       "Population                      652\n",
       "thinness1-19years                34\n",
       "thinness5-9years                 34\n",
       "Incomecompositionofresources    167\n",
       "Schooling                       163\n",
       "dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing values \n",
    "* with mean values in numerical columns group by Country\n",
    "* if values are still blank then, with mean values in numerical columns group by status\n",
    "* Remove the records which does not have target values. These are the same countries which has only one entry in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2928 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      "Year                            2928 non-null int64\n",
      "Lifeexpectancy                  2928 non-null float64\n",
      "AdultMortality                  2928 non-null float64\n",
      "infantdeaths                    2928 non-null int64\n",
      "Alcohol                         2928 non-null float64\n",
      "percentageexpenditure           2928 non-null float64\n",
      "HepatitisB                      2928 non-null float64\n",
      "Measles                         2928 non-null int64\n",
      "BMI                             2928 non-null float64\n",
      "under-fivedeaths                2928 non-null int64\n",
      "Polio                           2928 non-null float64\n",
      "Totalexpenditure                2928 non-null float64\n",
      "Diphtheria                      2928 non-null float64\n",
      "HIV/AIDS                        2928 non-null float64\n",
      "GDP                             2928 non-null float64\n",
      "Population                      2928 non-null float64\n",
      "thinness1-19years               2928 non-null float64\n",
      "thinness5-9years                2928 non-null float64\n",
      "Incomecompositionofresources    2928 non-null float64\n",
      "Schooling                       2928 non-null float64\n",
      "Country                         2928 non-null object\n",
      "Status                          2928 non-null object\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 526.1+ KB\n"
     ]
    }
   ],
   "source": [
    "##### Mean values for missing values group by Country\n",
    "data_grouped1=data.groupby(['Country'])\n",
    "data_imputed = data_grouped1.transform(lambda grp: grp.fillna(grp.mean()))\n",
    "\n",
    "#### Target Column: remove record without values\n",
    "data_imputed = data_imputed.dropna(axis=0, subset=['Lifeexpectancy'])\n",
    "data_imputed[['Country']]=data[['Country']]\n",
    "data_imputed[['Status']]=data[['Status']]\n",
    "\n",
    "\n",
    "##### Mean values for missing valuesgroup by status\n",
    "data_grouped2=data_imputed.groupby(['Status'])\n",
    "data_imputed = data_grouped2.transform(lambda grp: grp.fillna(grp.mean()))\n",
    "data_imputed[['Country']]=data[['Country']]\n",
    "data_imputed[['Status']]=data[['Status']]\n",
    "\n",
    "data_imputed.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating dummies for categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Binary Columns\n",
    "data_imputed['Status'] = data_imputed['Status'].map({'Developing':1, 'Developed':0}).astype(int)\n",
    "data_imputed.describe(include=['O'])\n",
    "\n",
    "#### Vector Column\n",
    "Country=pd.get_dummies(data_imputed['Country'],columns='Country',prefix='Country')\n",
    "Year=pd.get_dummies(data_imputed['Year'],columns='Year',prefix='Year')\n",
    "data_imputed=pd.concat([data_imputed, Country], axis=1)\n",
    "data_imputed=pd.concat([data_imputed, Year], axis=1)\n",
    "\n",
    "#### Drop original vector column\n",
    "data_imputed.drop(['Country'],axis=1,inplace= True)\n",
    "data_imputed.drop(['Year'],axis=1,inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Regression\n",
    "### Split the data into train, validation and test\n",
    "* As we can see from the graphs of the input varibles, it is clear that they not have normally distributed data, hence we are using MinMaxScaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### x and Y\n",
    "x=data_imputed.copy()\n",
    "x.drop(['Lifeexpectancy'],axis=1,inplace= True)\n",
    "y=data_imputed['Lifeexpectancy']\n",
    "#y=pd.DataFrame(y)\n",
    "\n",
    "\n",
    "#split train-test data and scalling \n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=0)\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(x_train,y_train,random_state=0)\n",
    "mn = MinMaxScaler()\n",
    "x_train = pd.DataFrame(mn.fit_transform(x_train))\n",
    "x_test = pd.DataFrame(mn.transform(x_test))\n",
    "x_tr = pd.DataFrame(mn.fit_transform(x_tr))\n",
    "x_val = pd.DataFrame(mn.transform(x_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-1 Linear Regression\n",
    "#### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross Validation Score: 0.955076 (+/- 0.01)\n"
     ]
    }
   ],
   "source": [
    "#linear regression with cross validation\n",
    "cvsl={'Model':[],'Mean_Score':[],'Std':[]}\n",
    "Cross_validation_Score = pd.DataFrame(cvsl)\n",
    "lreg = LinearRegression()\n",
    "cslreg=cross_val_score(lreg,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'Linear Regression','Mean_Score':cslreg.mean(),'Std':cslreg.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross Validation Score: %0.6f (+/- %0.2f)\" % (cslreg.mean(), cslreg.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-2 Ridge\n",
    "#### Naive Grid search & Graphical representation of alpha paramter and train-test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9610\n",
      "Best parameters: {'alpha': 0.01}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$\\\\alpha$')"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_range = [0.01, 0.1, 1, 10, 100]\n",
    "train_score_list = []\n",
    "test_score_list = []\n",
    "\n",
    "best_score=0\n",
    "for alpha in x_range: \n",
    "    ridge = Ridge(alpha)\n",
    "    ridge.fit(x_tr,y_tr)\n",
    "    train_score_list.append(ridge.score(x_tr,y_tr))\n",
    "    score=ridge.score(x_val, y_val)\n",
    "    test_score_list.append((score))\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        best_parameters = {'alpha': alpha}\n",
    "\n",
    "print(\"Best score: {:.4f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))\n",
    "\n",
    "%matplotlib inline\n",
    "plt.plot(x_range, train_score_list, c = 'g', label = 'Train Score')\n",
    "plt.plot(x_range, test_score_list, c = 'b', label = 'Validation Score')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc = 3)\n",
    "plt.xlabel(r'$\\alpha$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross Validation Score: 0.954983 (+/- 0.02)\n"
     ]
    }
   ],
   "source": [
    "#Ridge with cross validation\n",
    "alpha=0.01\n",
    "ridge = Ridge(alpha)\n",
    "csridge=cross_val_score(ridge,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'Ridge','Mean_Score':csridge.mean(),'Std':csridge.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross Validation Score: %0.6f (+/- %0.2f)\" % (csridge.mean(), csridge.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-3 Lasso\n",
    "#### Naive Grid search & Graphical representation of alpha paramter and train-test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8918\n",
      "Best parameters: {'alpha': 0.01}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$\\\\alpha$')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_range = [0.01, 0.1, 1, 10, 100]\n",
    "train_score_list = []\n",
    "test_score_list = []\n",
    "\n",
    "best_score=0\n",
    "for alpha in x_range: \n",
    "    lasso = Lasso(alpha)\n",
    "    lasso.fit(x_tr,y_tr)\n",
    "    train_score_list.append(lasso.score(x_tr,y_tr))\n",
    "    score=lasso.score(x_val, y_val)\n",
    "    test_score_list.append((score))\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        best_parameters = {'alpha': alpha}\n",
    "\n",
    "print(\"Best score: {:.4f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))\n",
    "\n",
    "%matplotlib inline\n",
    "plt.plot(x_range, train_score_list, c = 'g', label = 'Train Score')\n",
    "plt.plot(x_range, test_score_list, c = 'b', label = 'Validation Score')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc = 3)\n",
    "plt.xlabel(r'$\\alpha$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross Validation score: 0.894714 (+/- 0.02)\n"
     ]
    }
   ],
   "source": [
    "# Lasso with cross validation\n",
    "alpha=0.01\n",
    "lasso = Lasso(alpha)\n",
    "cslasso=cross_val_score(lasso,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'Lasso','Mean_Score':cslasso.mean(),'Std':cslasso.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross Validation score: %0.6f (+/- %0.2f)\" % (cslasso.mean(), cslasso.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-4 Polynomial Regression\n",
    "#### Naive Grid search & Graphical representation of alpha paramter and train-test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9600\n",
      "Best parameters: {'Polynomial degree': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Polynomial degree')"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_score_list = []\n",
    "test_score_list = []\n",
    "x_range = [1,2]\n",
    "\n",
    "best_score=0\n",
    "for n in range(1,3):\n",
    "    poly = PolynomialFeatures(n)\n",
    "    X_train_poly = poly.fit_transform(x_tr)\n",
    "    X_test_poly = poly.transform(x_val)\n",
    "    lreg.fit(X_train_poly, y_tr)\n",
    "    train_score_list.append(lreg.score(X_train_poly, y_tr))\n",
    "    score=lreg.score(X_test_poly, y_val)\n",
    "    test_score_list.append((score))\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        best_parameters = {'Polynomial degree': n}\n",
    "\n",
    "print(\"Best score: {:.4f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))\n",
    "\n",
    "%matplotlib inline\n",
    "plt.plot(x_range, train_score_list, c = 'g', label = 'Train Score')\n",
    "plt.plot(x_range, test_score_list, c = 'b', label = 'Validation Score')\n",
    "plt.legend(loc = 3)\n",
    "plt.xlabel('Polynomial degree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross Validation score: 0.954351 (+/- 0.01)\n"
     ]
    }
   ],
   "source": [
    "# polynomial regression with cross validation\n",
    "lreg = LinearRegression()\n",
    "poly = PolynomialFeatures(1)\n",
    "X_train_poly = poly.fit_transform(x_train)\n",
    "cslregpoly=cross_val_score(lreg,X_train_poly,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'Polynomial Regression','Mean_Score':cslregpoly.mean(),'Std':cslregpoly.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross Validation score: %0.6f (+/- %0.2f)\" % (cslregpoly.mean(), cslregpoly.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-5 SVM Simple\n",
    "#### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross validation score: 0.885082 (+/- 0.07)\n"
     ]
    }
   ],
   "source": [
    "#SVM simple with cross validation\n",
    "lregSVR = LinearSVR()\n",
    "cslregSVR=cross_val_score(lregSVR,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'SVM Simple','Mean_Score':cslregSVR.mean(),'Std':cslregSVR.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross validation score: %0.6f (+/- %0.2f)\" % (cslregSVR.mean(), cslregSVR.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-6 SVM Kernel=Linear\n",
    "#### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross validation score: 0.915591 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "#SVM Linear with cross validation\n",
    "lregSVRL = SVR(kernel='linear', C=1)\n",
    "cslregSVRL=cross_val_score(lregSVRL,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'SVM Kernel=Linear','Mean_Score':cslregSVRL.mean(),'Std':cslregSVRL.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross validation score: %0.6f (+/- %0.2f)\" % (cslregSVRL.mean(), cslregSVRL.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-7 SVM Kernel=rbf\n",
    "#### Naive Grid search & Graphical representation of alpha paramter and train-test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9592\n",
      "Best parameters: {'gamma': 0.1, 'C': 100}\n"
     ]
    }
   ],
   "source": [
    "Gamma = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "C = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "train_score_list = []\n",
    "test_score_list = []\n",
    "\n",
    "best_score=0\n",
    "for gamma in Gamma: \n",
    "    for c in C:\n",
    "        lregSVRR = SVR(kernel='rbf', gamma=gamma, C=c)\n",
    "        lregSVRR.fit(x_tr,y_tr)\n",
    "        train_score_list.append(lregSVRR.score(x_tr,y_tr))\n",
    "        score=lregSVRR.score(x_val, y_val)\n",
    "        test_score_list.append((score))\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'gamma': gamma, 'C': c}\n",
    "\n",
    "print(\"Best score: {:.4f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1bba470ae10>"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the mean cross-validation scores\n",
    "%matplotlib notebook\n",
    "import mglearn\n",
    "scores = np.array(test_score_list).reshape(6, 6)\n",
    "mglearn.tools.heatmap(scores, xlabel='C', xticklabels=C, ylabel='Gamma', yticklabels=Gamma, cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross validation score: 0.957147 (+/- 0.02)\n"
     ]
    }
   ],
   "source": [
    "#SVM rbf with cross validation\n",
    "lregSVRR = SVR(kernel='rbf', gamma=0.1, C=100)\n",
    "cslregSVRR=cross_val_score(lregSVRR,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'SVM Kernel=rbf','Mean_Score':cslregSVRR.mean(),'Std':cslregSVRR.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross validation score: %0.6f (+/- %0.2f)\" % (cslregSVRR.mean(), cslregSVRR.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-8 SVM Kernel=poly\n",
    "#### Naive Grid search & Graphical representation of alpha paramter and train-test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8282\n",
      "Best parameters: {'Degree': 2, 'C': 1000}\n"
     ]
    }
   ],
   "source": [
    "Degree = [2,3,4]\n",
    "C = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "train_score_list = []\n",
    "test_score_list = []\n",
    "\n",
    "best_score=0\n",
    "for degree in Degree: \n",
    "    for c in C:\n",
    "        lregSVRP = SVR(kernel='poly', degree=degree, C=c, gamma='auto')\n",
    "        lregSVRP.fit(x_tr,y_tr)\n",
    "        train_score_list.append(lregSVRP.score(x_tr,y_tr))\n",
    "        score=lregSVRP.score(x_val, y_val)\n",
    "        test_score_list.append((score))\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'Degree': degree, 'C': c}\n",
    "\n",
    "print(\"Best score: {:.4f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"431.5151265742489\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1bba0dc0278>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the mean cross-validation scores\n",
    "%matplotlib notebook\n",
    "import mglearn\n",
    "scores = np.array(test_score_list).reshape(3, 7)\n",
    "mglearn.tools.heatmap(scores, xlabel='C', xticklabels=C, ylabel='Degree', yticklabels=Degree, cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross validation score: 0.822728 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "#SVM Poly with cross validation\n",
    "lregSVRP = SVR(kernel='poly', degree=2, C=1000, gamma='auto')\n",
    "lregSVRP.fit(x_train,y_train)\n",
    "cslregSVRP=cross_val_score(lregSVRP,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'SVM Kernel=Poly','Mean_Score':cslregSVRP.mean(),'Std':cslregSVRP.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross validation score: %0.6f (+/- %0.2f)\" % (cslregSVRP.mean(), cslregSVRP.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model-9 KNeighborsRegressor\n",
    "#### Naive Grid search & Graphical representation of alpha paramter and test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8909\n",
      "Best parameters: {'K': 4}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'K')"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_score_list = []\n",
    "test_score_list = []\n",
    "K=[1,2,3,4,5,6,7,8,9]\n",
    "best_score=0\n",
    "for k in range(1,10):\n",
    "    knn_reg = KNeighborsRegressor(k)\n",
    "    knn_reg.fit(x_tr, y_tr)\n",
    "    train_score_list.append(knn_reg.score(x_tr, y_tr))\n",
    "    score=knn_reg.score(x_val, y_val)\n",
    "    test_score_list.append((score))\n",
    "    if score > best_score:\n",
    "        best_score = score\n",
    "        best_parameters = {'K': k}\n",
    "\n",
    "print(\"Best score: {:.4f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.plot(K, train_score_list, c = 'g', label = 'Train Score')\n",
    "plt.plot(K, test_score_list, c = 'b', label = 'Validation Score')\n",
    "plt.legend(loc = 3)\n",
    "plt.xlabel('K')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg Cross validation score: 0.888586 (+/- 0.03)\n"
     ]
    }
   ],
   "source": [
    "#KNN with cross validation\n",
    "knn_reg = KNeighborsRegressor(4)\n",
    "csknn_reg=cross_val_score(knn_reg,x_train,y_train,cv=5)\n",
    "Cross_validation_Score = Cross_validation_Score.append({'Model':'KNeighborsRegressor','Mean_Score':csknn_reg.mean(),'Std':csknn_reg.std() * 2}, ignore_index=True)\n",
    "print(\"Avg Cross validation score: %0.6f (+/- %0.2f)\" % (csknn_reg.mean(), csknn_reg.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comperision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Model  Mean_Score       Std\n",
      "0       Linear Regression    0.955076  0.013500\n",
      "1                   Ridge    0.954983  0.015913\n",
      "2                   Lasso    0.894714  0.023043\n",
      "4   Polynomial Regression    0.954351  0.012865\n",
      "6              SVM Simple    0.885082  0.068014\n",
      "7       SVM Kernel=Linear    0.915591  0.034254\n",
      "8          SVM Kernel=rbf    0.957147  0.016217\n",
      "9         SVM Kernel=Poly    0.822728  0.029408\n",
      "10    KNeighborsRegressor    0.888586  0.026405\n"
     ]
    }
   ],
   "source": [
    "#Cross_validation_Score_list.drop_duplicates\n",
    "#print(Cross_validation_Score_list.drop_duplicates(keep='last'))\n",
    "Cross_validation_Score = Cross_validation_Score.drop_duplicates()\n",
    "print(Cross_validation_Score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As per above table, SVR with kernel=rbf has the highest avg cross validation score. Hence, SMV with Kernel=rbf is the best Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model : SVM with kernel=rbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model Train Score:0.9958136607235222\n",
      "Final Model Test Score:0.9578471299100133\n"
     ]
    }
   ],
   "source": [
    "#### Final Model SVR with RBF Gamma=0.1 and C=100\n",
    "lregSVRR = SVR(kernel='rbf', gamma=0.1, C=100)\n",
    "lregSVRR.fit(x_train,y_train)\n",
    "print(\"Final Model Train Score:{0}\".format(lregSVRR.score(x_train,y_train)))\n",
    "print(\"Final Model Test Score:{0}\".format(lregSVRR.score(x_test,y_test)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}