Life Predicting Expectancy GH.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "client_1ea73d1c868b41d9849a0c8bbc170611 = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='QyqBIsA7UMEWMmSuEua6Hc_P1MEJgu00SScWTcmwgLRQ',\n",
    "    ibm_auth_endpoint=\"https://iam.cloud.ibm.com/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3.eu-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_1ea73d1c868b41d9849a0c8bbc170611.get_object(Bucket='lifezproject-donotdelete-pr-hljh2qcjn7tcjs',Key='Life Expectancy Data.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "data = pd.read_csv(body)\n",
    "data.head()\n"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 2,
     "data": {
      "text/plain": "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n0  Afghanistan  2015  Developing              65.0            263.0   \n1  Afghanistan  2014  Developing              59.9            271.0   \n2  Afghanistan  2013  Developing              59.9            268.0   \n3  Afghanistan  2012  Developing              59.5            272.0   \n4  Afghanistan  2011  Developing              59.2            275.0   \n\n   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   ...  \\\n0             62     0.01               71.279624         65.0      1154  ...   \n1             64     0.01               73.523582         62.0       492  ...   \n2             66     0.01               73.219243         64.0       430  ...   \n3             69     0.01               78.184215         67.0      2787  ...   \n4             71     0.01                7.097109         68.0      3013  ...   \n\n   Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n0    6.0               8.16         65.0        0.1  584.259210  33736494.0   \n1   58.0               8.18         62.0        0.1  612.696514    327582.0   \n2   62.0               8.13         64.0        0.1  631.744976  31731688.0   \n3   67.0               8.52         67.0        0.1  669.959000   3696958.0   \n4   68.0               7.87         68.0        0.1   63.537231   2978599.0   \n\n    thinness  1-19 years   thinness 5-9 years  \\\n0                   17.2                 17.3   \n1                   17.5                 17.5   \n2                   17.7                 17.7   \n3                   17.9                 18.0   \n4                   18.2                 18.2   \n\n   Income composition of resources  Schooling  \n0                            0.479       10.1  \n1                            0.476       10.0  \n2                            0.470        9.9  \n3                            0.463        9.8  \n4                            0.454        9.5  \n\n[5 rows x 22 columns]",
      "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>Year</th>\n      <th>Status</th>\n      <th>Life expectancy</th>\n      <th>Adult Mortality</th>\n      <th>infant deaths</th>\n      <th>Alcohol</th>\n      <th>percentage expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>...</th>\n      <th>Polio</th>\n      <th>Total expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>thinness  1-19 years</th>\n      <th>thinness 5-9 years</th>\n      <th>Income composition of resources</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    </tr>\n    <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    </tr>\n    <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</table>\n<p>5 rows × 22 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import statsmodels.api as sm"
   ],
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data.info()"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2938 entries, 0 to 2937\nData columns (total 22 columns):\nCountry                            2938 non-null object\nYear                               2938 non-null int64\nStatus                             2938 non-null object\nLife expectancy                    2928 non-null float64\nAdult Mortality                    2928 non-null float64\ninfant deaths                      2938 non-null int64\nAlcohol                            2744 non-null float64\npercentage expenditure             2938 non-null float64\nHepatitis B                        2385 non-null float64\nMeasles                            2938 non-null int64\n BMI                               2904 non-null float64\nunder-five deaths                  2938 non-null int64\nPolio                              2919 non-null float64\nTotal expenditure                  2712 non-null float64\nDiphtheria                         2919 non-null float64\n HIV/AIDS                          2938 non-null float64\nGDP                                2490 non-null float64\nPopulation                         2286 non-null float64\n thinness  1-19 years              2904 non-null float64\n thinness 5-9 years                2904 non-null float64\nIncome composition of resources    2771 non-null float64\nSchooling                          2775 non-null float64\ndtypes: float64(16), int64(4), object(2)\nmemory usage: 505.0+ KB\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data.columns.values"
   ],
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 5,
     "data": {
      "text/plain": "array(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n       'infant deaths', 'Alcohol', 'percentage expenditure',\n       'Hepatitis B', 'Measles ', ' BMI ', 'under-five deaths ', 'Polio',\n       'Total expenditure', 'Diphtheria ', ' HIV/AIDS', 'GDP',\n       'Population', ' thinness  1-19 years', ' thinness 5-9 years',\n       'Income composition of resources', 'Schooling'], dtype=object)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "column_names = ['Country', 'Year', 'Status', 'Life Expectancy', 'Adult Mortality',\n",
    "       'Infant Deaths', 'Alcohol', 'Percentage Expenditure',\n",
    "       'Hepatitis B', 'Measles', 'BMI', 'Under-Five Deaths ', 'Polio',\n",
    "       'Total Expenditure', 'Diphtheria', 'HIV/AIDS', 'GDP',\n",
    "       'Population', 'Thinness 1-19 years', 'Thinness 5-9 years',\n",
    "       'Income Composition of Resources', 'Schooling']"
   ],
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data.columns = column_names\n",
    "data.info()"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2938 entries, 0 to 2937\nData columns (total 22 columns):\nCountry                            2938 non-null object\nYear                               2938 non-null int64\nStatus                             2938 non-null object\nLife Expectancy                    2928 non-null float64\nAdult Mortality                    2928 non-null float64\nInfant Deaths                      2938 non-null int64\nAlcohol                            2744 non-null float64\nPercentage Expenditure             2938 non-null float64\nHepatitis B                        2385 non-null float64\nMeasles                            2938 non-null int64\nBMI                                2904 non-null float64\nUnder-Five Deaths                  2938 non-null int64\nPolio                              2919 non-null float64\nTotal Expenditure                  2712 non-null float64\nDiphtheria                         2919 non-null float64\nHIV/AIDS                           2938 non-null float64\nGDP                                2490 non-null float64\nPopulation                         2286 non-null float64\nThinness 1-19 years                2904 non-null float64\nThinness 5-9 years                 2904 non-null float64\nIncome Composition of Resources    2771 non-null float64\nSchooling                          2775 non-null float64\ndtypes: float64(16), int64(4), object(2)\nmemory usage: 505.0+ KB\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data.corr()"
   ],
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "                                     Year  Life Expectancy  Adult Mortality  \\\nYear                             1.000000         0.170033        -0.079052   \nLife Expectancy                  0.170033         1.000000        -0.696359   \nAdult Mortality                 -0.079052        -0.696359         1.000000   \nInfant Deaths                   -0.037415        -0.196557         0.078756   \nAlcohol                         -0.052990         0.404877        -0.195848   \nPercentage Expenditure           0.031400         0.381864        -0.242860   \nHepatitis B                      0.104333         0.256762        -0.162476   \nMeasles                         -0.082493        -0.157586         0.031176   \nBMI                              0.108974         0.567694        -0.387017   \nUnder-Five Deaths               -0.042937        -0.222529         0.094146   \nPolio                            0.094158         0.465556        -0.274823   \nTotal Expenditure                0.090740         0.218086        -0.115281   \nDiphtheria                       0.134337         0.479495        -0.275131   \nHIV/AIDS                        -0.139741        -0.556556         0.523821   \nGDP                              0.101620         0.461455        -0.296049   \nPopulation                       0.016969        -0.021538        -0.013647   \nThinness 1-19 years             -0.047876        -0.477183         0.302904   \nThinness 5-9 years              -0.050929        -0.471584         0.308457   \nIncome Composition of Resources  0.243468         0.724776        -0.457626   \nSchooling                        0.209400         0.751975        -0.454612   \n\n                                 Infant Deaths   Alcohol  \\\nYear                                 -0.037415 -0.052990   \nLife Expectancy                      -0.196557  0.404877   \nAdult Mortality                       0.078756 -0.195848   \nInfant Deaths                         1.000000 -0.115638   \nAlcohol                              -0.115638  1.000000   \nPercentage Expenditure               -0.085612  0.341285   \nHepatitis B                          -0.223566  0.087549   \nMeasles                               0.501128 -0.051827   \nBMI                                  -0.227279  0.330408   \nUnder-Five Deaths                     0.996629 -0.112370   \nPolio                                -0.170689  0.221734   \nTotal Expenditure                    -0.128616  0.296942   \nDiphtheria                           -0.175171  0.222020   \nHIV/AIDS                              0.025231 -0.048845   \nGDP                                  -0.108427  0.354712   \nPopulation                            0.556801 -0.035252   \nThinness 1-19 years                   0.465711 -0.428795   \nThinness 5-9 years                    0.471350 -0.417414   \nIncome Composition of Resources      -0.145139  0.450040   \nSchooling                            -0.193720  0.547378   \n\n                                 Percentage Expenditure  Hepatitis B  \\\nYear                                           0.031400     0.104333   \nLife Expectancy                                0.381864     0.256762   \nAdult Mortality                               -0.242860    -0.162476   \nInfant Deaths                                 -0.085612    -0.223566   \nAlcohol                                        0.341285     0.087549   \nPercentage Expenditure                         1.000000     0.016274   \nHepatitis B                                    0.016274     1.000000   \nMeasles                                       -0.056596    -0.120529   \nBMI                                            0.228700     0.150380   \nUnder-Five Deaths                             -0.087852    -0.233126   \nPolio                                          0.147259     0.486171   \nTotal Expenditure                              0.174420     0.058280   \nDiphtheria                                     0.143624     0.611495   \nHIV/AIDS                                      -0.097857    -0.112675   \nGDP                                            0.899373     0.083903   \nPopulation                                    -0.025662    -0.123321   \nThinness 1-19 years                           -0.251369    -0.120429   \nThinness 5-9 years                            -0.252905    -0.124960   \nIncome Composition of Resources                0.381952     0.199549   \nSchooling                                      0.389687     0.231117   \n\n                                  Measles       BMI  Under-Five Deaths   \\\nYear                            -0.082493  0.108974           -0.042937   \nLife Expectancy                 -0.157586  0.567694           -0.222529   \nAdult Mortality                  0.031176 -0.387017            0.094146   \nInfant Deaths                    0.501128 -0.227279            0.996629   \nAlcohol                         -0.051827  0.330408           -0.112370   \nPercentage Expenditure          -0.056596  0.228700           -0.087852   \nHepatitis B                     -0.120529  0.150380           -0.233126   \nMeasles                          1.000000 -0.175977            0.507809   \nBMI                             -0.175977  1.000000           -0.237669   \nUnder-Five Deaths                0.507809 -0.237669            1.000000   \nPolio                           -0.136166  0.284569           -0.188720   \nTotal Expenditure               -0.106241  0.242503           -0.130148   \nDiphtheria                      -0.141882  0.283147           -0.195668   \nHIV/AIDS                         0.030899 -0.243717            0.038062   \nGDP                             -0.076466  0.301557           -0.112081   \nPopulation                       0.265966 -0.072301            0.544423   \nThinness 1-19 years              0.224808 -0.532025            0.467789   \nThinness 5-9 years               0.221072 -0.538911            0.472263   \nIncome Composition of Resources -0.129568  0.508774           -0.163305   \nSchooling                       -0.137225  0.546961           -0.209373   \n\n                                    Polio  Total Expenditure  Diphtheria  \\\nYear                             0.094158           0.090740    0.134337   \nLife Expectancy                  0.465556           0.218086    0.479495   \nAdult Mortality                 -0.274823          -0.115281   -0.275131   \nInfant Deaths                   -0.170689          -0.128616   -0.175171   \nAlcohol                          0.221734           0.296942    0.222020   \nPercentage Expenditure           0.147259           0.174420    0.143624   \nHepatitis B                      0.486171           0.058280    0.611495   \nMeasles                         -0.136166          -0.106241   -0.141882   \nBMI                              0.284569           0.242503    0.283147   \nUnder-Five Deaths               -0.188720          -0.130148   -0.195668   \nPolio                            1.000000           0.137330    0.673553   \nTotal Expenditure                0.137330           1.000000    0.152754   \nDiphtheria                       0.673553           0.152754    1.000000   \nHIV/AIDS                        -0.159560          -0.001389   -0.164860   \nGDP                              0.211976           0.138364    0.200666   \nPopulation                      -0.038540          -0.079662   -0.028444   \nThinness 1-19 years             -0.221823          -0.277101   -0.229518   \nThinness 5-9 years              -0.222592          -0.283774   -0.222743   \nIncome Composition of Resources  0.381078           0.166682    0.401456   \nSchooling                        0.417866           0.246384    0.425332   \n\n                                 HIV/AIDS       GDP  Population  \\\nYear                            -0.139741  0.101620    0.016969   \nLife Expectancy                 -0.556556  0.461455   -0.021538   \nAdult Mortality                  0.523821 -0.296049   -0.013647   \nInfant Deaths                    0.025231 -0.108427    0.556801   \nAlcohol                         -0.048845  0.354712   -0.035252   \nPercentage Expenditure          -0.097857  0.899373   -0.025662   \nHepatitis B                     -0.112675  0.083903   -0.123321   \nMeasles                          0.030899 -0.076466    0.265966   \nBMI                             -0.243717  0.301557   -0.072301   \nUnder-Five Deaths                0.038062 -0.112081    0.544423   \nPolio                           -0.159560  0.211976   -0.038540   \nTotal Expenditure               -0.001389  0.138364   -0.079662   \nDiphtheria                      -0.164860  0.200666   -0.028444   \nHIV/AIDS                         1.000000 -0.136491   -0.027854   \nGDP                             -0.136491  1.000000   -0.028270   \nPopulation                      -0.027854 -0.028270    1.000000   \nThinness 1-19 years              0.204064 -0.285697    0.253944   \nThinness 5-9 years               0.207283 -0.290539    0.251403   \nIncome Composition of Resources -0.249519  0.460341   -0.008735   \nSchooling                       -0.220429  0.448273   -0.031668   \n\n                                 Thinness 1-19 years  Thinness 5-9 years  \\\nYear                                       -0.047876           -0.050929   \nLife Expectancy                            -0.477183           -0.471584   \nAdult Mortality                             0.302904            0.308457   \nInfant Deaths                               0.465711            0.471350   \nAlcohol                                    -0.428795           -0.417414   \nPercentage Expenditure                     -0.251369           -0.252905   \nHepatitis B                                -0.120429           -0.124960   \nMeasles                                     0.224808            0.221072   \nBMI                                        -0.532025           -0.538911   \nUnder-Five Deaths                           0.467789            0.472263   \nPolio                                      -0.221823           -0.222592   \nTotal Expenditure                          -0.277101           -0.283774   \nDiphtheria                                 -0.229518           -0.222743   \nHIV/AIDS                                    0.204064            0.207283   \nGDP                                        -0.285697           -0.290539   \nPopulation                                  0.253944            0.251403   \nThinness 1-19 years                         1.000000            0.939102   \nThinness 5-9 years                          0.939102            1.000000   \nIncome Composition of Resources            -0.422429           -0.411053   \nSchooling                                  -0.471652           -0.460632   \n\n                                 Income Composition of Resources  Schooling  \nYear                                                    0.243468   0.209400  \nLife Expectancy                                         0.724776   0.751975  \nAdult Mortality                                        -0.457626  -0.454612  \nInfant Deaths                                          -0.145139  -0.193720  \nAlcohol                                                 0.450040   0.547378  \nPercentage Expenditure                                  0.381952   0.389687  \nHepatitis B                                             0.199549   0.231117  \nMeasles                                                -0.129568  -0.137225  \nBMI                                                     0.508774   0.546961  \nUnder-Five Deaths                                      -0.163305  -0.209373  \nPolio                                                   0.381078   0.417866  \nTotal Expenditure                                       0.166682   0.246384  \nDiphtheria                                              0.401456   0.425332  \nHIV/AIDS                                               -0.249519  -0.220429  \nGDP                                                     0.460341   0.448273  \nPopulation                                             -0.008735  -0.031668  \nThinness 1-19 years                                    -0.422429  -0.471652  \nThinness 5-9 years                                     -0.411053  -0.460632  \nIncome Composition of Resources                         1.000000   0.800092  \nSchooling                                               0.800092   1.000000  ",
      "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>Year</th>\n      <th>Life Expectancy</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>BMI</th>\n      <th>Under-Five Deaths</th>\n      <th>Polio</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</th>\n      <th>Schooling</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Year</th>\n      <td>1.000000</td>\n      <td>0.170033</td>\n      <td>-0.079052</td>\n      <td>-0.037415</td>\n      <td>-0.052990</td>\n      <td>0.031400</td>\n      <td>0.104333</td>\n      <td>-0.082493</td>\n      <td>0.108974</td>\n      <td>-0.042937</td>\n      <td>0.094158</td>\n      <td>0.090740</td>\n      <td>0.134337</td>\n      <td>-0.139741</td>\n      <td>0.101620</td>\n      <td>0.016969</td>\n      <td>-0.047876</td>\n      <td>-0.050929</td>\n      <td>0.243468</td>\n      <td>0.209400</td>\n    </tr>\n    <tr>\n      <th>Life Expectancy</th>\n      <td>0.170033</td>\n      <td>1.000000</td>\n      <td>-0.696359</td>\n      <td>-0.196557</td>\n      <td>0.404877</td>\n      <td>0.381864</td>\n      <td>0.256762</td>\n      <td>-0.157586</td>\n      <td>0.567694</td>\n      <td>-0.222529</td>\n      <td>0.465556</td>\n      <td>0.218086</td>\n      <td>0.479495</td>\n      <td>-0.556556</td>\n      <td>0.461455</td>\n      <td>-0.021538</td>\n      <td>-0.477183</td>\n      <td>-0.471584</td>\n      <td>0.724776</td>\n      <td>0.751975</td>\n    </tr>\n    <tr>\n      <th>Adult Mortality</th>\n      <td>-0.079052</td>\n      <td>-0.696359</td>\n      <td>1.000000</td>\n      <td>0.078756</td>\n      <td>-0.195848</td>\n      <td>-0.242860</td>\n      <td>-0.162476</td>\n      <td>0.031176</td>\n      <td>-0.387017</td>\n      <td>0.094146</td>\n      <td>-0.274823</td>\n      <td>-0.115281</td>\n      <td>-0.275131</td>\n      <td>0.523821</td>\n      <td>-0.296049</td>\n      <td>-0.013647</td>\n      <td>0.302904</td>\n      <td>0.308457</td>\n      <td>-0.457626</td>\n      <td>-0.454612</td>\n    </tr>\n    <tr>\n      <th>Infant Deaths</th>\n      <td>-0.037415</td>\n      <td>-0.196557</td>\n      <td>0.078756</td>\n      <td>1.000000</td>\n      <td>-0.115638</td>\n      <td>-0.085612</td>\n      <td>-0.223566</td>\n      <td>0.501128</td>\n      <td>-0.227279</td>\n      <td>0.996629</td>\n      <td>-0.170689</td>\n      <td>-0.128616</td>\n      <td>-0.175171</td>\n      <td>0.025231</td>\n      <td>-0.108427</td>\n      <td>0.556801</td>\n      <td>0.465711</td>\n      <td>0.471350</td>\n      <td>-0.145139</td>\n      <td>-0.193720</td>\n    </tr>\n    <tr>\n      <th>Alcohol</th>\n      <td>-0.052990</td>\n      <td>0.404877</td>\n      <td>-0.195848</td>\n      <td>-0.115638</td>\n      <td>1.000000</td>\n      <td>0.341285</td>\n      <td>0.087549</td>\n      <td>-0.051827</td>\n      <td>0.330408</td>\n      <td>-0.112370</td>\n      <td>0.221734</td>\n      <td>0.296942</td>\n      <td>0.222020</td>\n      <td>-0.048845</td>\n      <td>0.354712</td>\n      <td>-0.035252</td>\n      <td>-0.428795</td>\n      <td>-0.417414</td>\n      <td>0.450040</td>\n      <td>0.547378</td>\n    </tr>\n    <tr>\n      <th>Percentage Expenditure</th>\n      <td>0.031400</td>\n      <td>0.381864</td>\n      <td>-0.242860</td>\n      <td>-0.085612</td>\n      <td>0.341285</td>\n      <td>1.000000</td>\n      <td>0.016274</td>\n      <td>-0.056596</td>\n      <td>0.228700</td>\n      <td>-0.087852</td>\n      <td>0.147259</td>\n      <td>0.174420</td>\n      <td>0.143624</td>\n      <td>-0.097857</td>\n      <td>0.899373</td>\n      <td>-0.025662</td>\n      <td>-0.251369</td>\n      <td>-0.252905</td>\n      <td>0.381952</td>\n      <td>0.389687</td>\n    </tr>\n    <tr>\n      <th>Hepatitis B</th>\n      <td>0.104333</td>\n      <td>0.256762</td>\n      <td>-0.162476</td>\n      <td>-0.223566</td>\n      <td>0.087549</td>\n      <td>0.016274</td>\n      <td>1.000000</td>\n      <td>-0.120529</td>\n      <td>0.150380</td>\n      <td>-0.233126</td>\n      <td>0.486171</td>\n      <td>0.058280</td>\n      <td>0.611495</td>\n      <td>-0.112675</td>\n      <td>0.083903</td>\n      <td>-0.123321</td>\n      <td>-0.120429</td>\n      <td>-0.124960</td>\n      <td>0.199549</td>\n      <td>0.231117</td>\n    </tr>\n    <tr>\n      <th>Measles</th>\n      <td>-0.082493</td>\n      <td>-0.157586</td>\n      <td>0.031176</td>\n      <td>0.501128</td>\n      <td>-0.051827</td>\n      <td>-0.056596</td>\n      <td>-0.120529</td>\n      <td>1.000000</td>\n      <td>-0.175977</td>\n      <td>0.507809</td>\n      <td>-0.136166</td>\n      <td>-0.106241</td>\n      <td>-0.141882</td>\n      <td>0.030899</td>\n      <td>-0.076466</td>\n      <td>0.265966</td>\n      <td>0.224808</td>\n      <td>0.221072</td>\n      <td>-0.129568</td>\n      <td>-0.137225</td>\n    </tr>\n    <tr>\n      <th>BMI</th>\n      <td>0.108974</td>\n      <td>0.567694</td>\n      <td>-0.387017</td>\n      <td>-0.227279</td>\n      <td>0.330408</td>\n      <td>0.228700</td>\n      <td>0.150380</td>\n      <td>-0.175977</td>\n      <td>1.000000</td>\n      <td>-0.237669</td>\n      <td>0.284569</td>\n      <td>0.242503</td>\n      <td>0.283147</td>\n      <td>-0.243717</td>\n      <td>0.301557</td>\n      <td>-0.072301</td>\n      <td>-0.532025</td>\n      <td>-0.538911</td>\n      <td>0.508774</td>\n      <td>0.546961</td>\n    </tr>\n    <tr>\n      <th>Under-Five Deaths</th>\n      <td>-0.042937</td>\n      <td>-0.222529</td>\n      <td>0.094146</td>\n      <td>0.996629</td>\n      <td>-0.112370</td>\n      <td>-0.087852</td>\n      <td>-0.233126</td>\n      <td>0.507809</td>\n      <td>-0.237669</td>\n      <td>1.000000</td>\n      <td>-0.188720</td>\n      <td>-0.130148</td>\n      <td>-0.195668</td>\n      <td>0.038062</td>\n      <td>-0.112081</td>\n      <td>0.544423</td>\n      <td>0.467789</td>\n      <td>0.472263</td>\n      <td>-0.163305</td>\n      <td>-0.209373</td>\n    </tr>\n    <tr>\n      <th>Polio</th>\n      <td>0.094158</td>\n      <td>0.465556</td>\n      <td>-0.274823</td>\n      <td>-0.170689</td>\n      <td>0.221734</td>\n      <td>0.147259</td>\n      <td>0.486171</td>\n      <td>-0.136166</td>\n      <td>0.284569</td>\n      <td>-0.188720</td>\n      <td>1.000000</td>\n      <td>0.137330</td>\n      <td>0.673553</td>\n      <td>-0.159560</td>\n      <td>0.211976</td>\n      <td>-0.038540</td>\n      <td>-0.221823</td>\n      <td>-0.222592</td>\n      <td>0.381078</td>\n      <td>0.417866</td>\n    </tr>\n    <tr>\n      <th>Total Expenditure</th>\n      <td>0.090740</td>\n      <td>0.218086</td>\n      <td>-0.115281</td>\n      <td>-0.128616</td>\n      <td>0.296942</td>\n      <td>0.174420</td>\n      <td>0.058280</td>\n      <td>-0.106241</td>\n      <td>0.242503</td>\n      <td>-0.130148</td>\n      <td>0.137330</td>\n      <td>1.000000</td>\n      <td>0.152754</td>\n      <td>-0.001389</td>\n      <td>0.138364</td>\n      <td>-0.079662</td>\n      <td>-0.277101</td>\n      <td>-0.283774</td>\n      <td>0.166682</td>\n      <td>0.246384</td>\n    </tr>\n    <tr>\n      <th>Diphtheria</th>\n      <td>0.134337</td>\n      <td>0.479495</td>\n      <td>-0.275131</td>\n      <td>-0.175171</td>\n      <td>0.222020</td>\n      <td>0.143624</td>\n      <td>0.611495</td>\n      <td>-0.141882</td>\n      <td>0.283147</td>\n      <td>-0.195668</td>\n      <td>0.673553</td>\n      <td>0.152754</td>\n      <td>1.000000</td>\n      <td>-0.164860</td>\n      <td>0.200666</td>\n      <td>-0.028444</td>\n      <td>-0.229518</td>\n      <td>-0.222743</td>\n      <td>0.401456</td>\n      <td>0.425332</td>\n    </tr>\n    <tr>\n      <th>HIV/AIDS</th>\n      <td>-0.139741</td>\n      <td>-0.556556</td>\n      <td>0.523821</td>\n      <td>0.025231</td>\n      <td>-0.048845</td>\n      <td>-0.097857</td>\n      <td>-0.112675</td>\n      <td>0.030899</td>\n      <td>-0.243717</td>\n      <td>0.038062</td>\n      <td>-0.159560</td>\n      <td>-0.001389</td>\n      <td>-0.164860</td>\n      <td>1.000000</td>\n      <td>-0.136491</td>\n      <td>-0.027854</td>\n      <td>0.204064</td>\n      <td>0.207283</td>\n      <td>-0.249519</td>\n      <td>-0.220429</td>\n    </tr>\n    <tr>\n      <th>GDP</th>\n      <td>0.101620</td>\n      <td>0.461455</td>\n      <td>-0.296049</td>\n      <td>-0.108427</td>\n      <td>0.354712</td>\n      <td>0.899373</td>\n      <td>0.083903</td>\n      <td>-0.076466</td>\n      <td>0.301557</td>\n      <td>-0.112081</td>\n      <td>0.211976</td>\n      <td>0.138364</td>\n      <td>0.200666</td>\n      <td>-0.136491</td>\n      <td>1.000000</td>\n      <td>-0.028270</td>\n      <td>-0.285697</td>\n      <td>-0.290539</td>\n      <td>0.460341</td>\n      <td>0.448273</td>\n    </tr>\n    <tr>\n      <th>Population</th>\n      <td>0.016969</td>\n      <td>-0.021538</td>\n      <td>-0.013647</td>\n      <td>0.556801</td>\n      <td>-0.035252</td>\n      <td>-0.025662</td>\n      <td>-0.123321</td>\n      <td>0.265966</td>\n      <td>-0.072301</td>\n      <td>0.544423</td>\n      <td>-0.038540</td>\n      <td>-0.079662</td>\n      <td>-0.028444</td>\n      <td>-0.027854</td>\n      <td>-0.028270</td>\n      <td>1.000000</td>\n      <td>0.253944</td>\n      <td>0.251403</td>\n      <td>-0.008735</td>\n      <td>-0.031668</td>\n    </tr>\n    <tr>\n      <th>Thinness 1-19 years</th>\n      <td>-0.047876</td>\n      <td>-0.477183</td>\n      <td>0.302904</td>\n      <td>0.465711</td>\n      <td>-0.428795</td>\n      <td>-0.251369</td>\n      <td>-0.120429</td>\n      <td>0.224808</td>\n      <td>-0.532025</td>\n      <td>0.467789</td>\n      <td>-0.221823</td>\n      <td>-0.277101</td>\n      <td>-0.229518</td>\n      <td>0.204064</td>\n      <td>-0.285697</td>\n      <td>0.253944</td>\n      <td>1.000000</td>\n      <td>0.939102</td>\n      <td>-0.422429</td>\n      <td>-0.471652</td>\n    </tr>\n    <tr>\n      <th>Thinness 5-9 years</th>\n      <td>-0.050929</td>\n      <td>-0.471584</td>\n      <td>0.308457</td>\n      <td>0.471350</td>\n      <td>-0.417414</td>\n      <td>-0.252905</td>\n      <td>-0.124960</td>\n      <td>0.221072</td>\n      <td>-0.538911</td>\n      <td>0.472263</td>\n      <td>-0.222592</td>\n      <td>-0.283774</td>\n      <td>-0.222743</td>\n      <td>0.207283</td>\n      <td>-0.290539</td>\n      <td>0.251403</td>\n      <td>0.939102</td>\n      <td>1.000000</td>\n      <td>-0.411053</td>\n      <td>-0.460632</td>\n    </tr>\n    <tr>\n      <th>Income Composition of Resources</th>\n      <td>0.243468</td>\n      <td>0.724776</td>\n      <td>-0.457626</td>\n      <td>-0.145139</td>\n      <td>0.450040</td>\n      <td>0.381952</td>\n      <td>0.199549</td>\n      <td>-0.129568</td>\n      <td>0.508774</td>\n      <td>-0.163305</td>\n      <td>0.381078</td>\n      <td>0.166682</td>\n      <td>0.401456</td>\n      <td>-0.249519</td>\n      <td>0.460341</td>\n      <td>-0.008735</td>\n      <td>-0.422429</td>\n      <td>-0.411053</td>\n      <td>1.000000</td>\n      <td>0.800092</td>\n    </tr>\n    <tr>\n      <th>Schooling</th>\n      <td>0.209400</td>\n      <td>0.751975</td>\n      <td>-0.454612</td>\n      <td>-0.193720</td>\n      <td>0.547378</td>\n      <td>0.389687</td>\n      <td>0.231117</td>\n      <td>-0.137225</td>\n      <td>0.546961</td>\n      <td>-0.209373</td>\n      <td>0.417866</td>\n      <td>0.246384</td>\n      <td>0.425332</td>\n      <td>-0.220429</td>\n      <td>0.448273</td>\n      <td>-0.031668</td>\n      <td>-0.471652</td>\n      <td>-0.460632</td>\n      <td>0.800092</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1 = data.copy()"
   ],
   "execution_count": 9,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1 = data1.dropna(axis = 0, subset = ['Life Expectancy'])\n",
    "data1.info()"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2928 entries, 0 to 2937\nData columns (total 22 columns):\nCountry                            2928 non-null object\nYear                               2928 non-null int64\nStatus                             2928 non-null object\nLife Expectancy                    2928 non-null float64\nAdult Mortality                    2928 non-null float64\nInfant Deaths                      2928 non-null int64\nAlcohol                            2735 non-null float64\nPercentage Expenditure             2928 non-null float64\nHepatitis B                        2375 non-null float64\nMeasles                            2928 non-null int64\nBMI                                2896 non-null float64\nUnder-Five Deaths                  2928 non-null int64\nPolio                              2909 non-null float64\nTotal Expenditure                  2702 non-null float64\nDiphtheria                         2909 non-null float64\nHIV/AIDS                           2928 non-null float64\nGDP                                2485 non-null float64\nPopulation                         2284 non-null float64\nThinness 1-19 years                2896 non-null float64\nThinness 5-9 years                 2896 non-null float64\nIncome Composition of Resources    2768 non-null float64\nSchooling                          2768 non-null float64\ndtypes: float64(16), int64(4), object(2)\nmemory usage: 526.1+ KB\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1.mean()"
   ],
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "Year                               2.007500e+03\nLife Expectancy                    6.922493e+01\nAdult Mortality                    1.647964e+02\nInfant Deaths                      3.040745e+01\nAlcohol                            4.614856e+00\nPercentage Expenditure             7.403212e+02\nHepatitis B                        8.096084e+01\nMeasles                            2.427856e+03\nBMI                                3.823539e+01\nUnder-Five Deaths                  4.217930e+01\nPolio                              8.254830e+01\nTotal Expenditure                  5.930163e+00\nDiphtheria                         8.232142e+01\nHIV/AIDS                           1.747712e+00\nGDP                                7.494211e+03\nPopulation                         1.276454e+07\nThinness 1-19 years                4.850622e+00\nThinness 5-9 years                 4.881423e+00\nIncome Composition of Resources    6.274187e-01\nSchooling                          1.199964e+01\ndtype: float64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1 = data1.fillna(data1.mean())\n",
    "data1.info()"
   ],
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2928 entries, 0 to 2937\nData columns (total 22 columns):\nCountry                            2928 non-null object\nYear                               2928 non-null int64\nStatus                             2928 non-null object\nLife Expectancy                    2928 non-null float64\nAdult Mortality                    2928 non-null float64\nInfant Deaths                      2928 non-null int64\nAlcohol                            2928 non-null float64\nPercentage Expenditure             2928 non-null float64\nHepatitis B                        2928 non-null float64\nMeasles                            2928 non-null int64\nBMI                                2928 non-null float64\nUnder-Five Deaths                  2928 non-null int64\nPolio                              2928 non-null float64\nTotal Expenditure                  2928 non-null float64\nDiphtheria                         2928 non-null float64\nHIV/AIDS                           2928 non-null float64\nGDP                                2928 non-null float64\nPopulation                         2928 non-null float64\nThinness 1-19 years                2928 non-null float64\nThinness 5-9 years                 2928 non-null float64\nIncome Composition of Resources    2928 non-null float64\nSchooling                          2928 non-null float64\ndtypes: float64(16), int64(4), object(2)\nmemory usage: 526.1+ KB\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1.head()"
   ],
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "       Country  Year      Status  Life Expectancy  Adult Mortality  \\\n0  Afghanistan  2015  Developing             65.0            263.0   \n1  Afghanistan  2014  Developing             59.9            271.0   \n2  Afghanistan  2013  Developing             59.9            268.0   \n3  Afghanistan  2012  Developing             59.5            272.0   \n4  Afghanistan  2011  Developing             59.2            275.0   \n\n   Infant Deaths  Alcohol  Percentage Expenditure  Hepatitis B  Measles  ...  \\\n0             62     0.01               71.279624         65.0     1154  ...   \n1             64     0.01               73.523582         62.0      492  ...   \n2             66     0.01               73.219243         64.0      430  ...   \n3             69     0.01               78.184215         67.0     2787  ...   \n4             71     0.01                7.097109         68.0     3013  ...   \n\n   Polio  Total Expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n0    6.0               8.16        65.0       0.1  584.259210  33736494.0   \n1   58.0               8.18        62.0       0.1  612.696514    327582.0   \n2   62.0               8.13        64.0       0.1  631.744976  31731688.0   \n3   67.0               8.52        67.0       0.1  669.959000   3696958.0   \n4   68.0               7.87        68.0       0.1   63.537231   2978599.0   \n\n   Thinness 1-19 years  Thinness 5-9 years  Income Composition of Resources  \\\n0                 17.2                17.3                            0.479   \n1                 17.5                17.5                            0.476   \n2                 17.7                17.7                            0.470   \n3                 17.9                18.0                            0.463   \n4                 18.2                18.2                            0.454   \n\n   Schooling  \n0       10.1  \n1       10.0  \n2        9.9  \n3        9.8  \n4        9.5  \n\n[5 rows x 22 columns]",
      "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>Year</th>\n      <th>Status</th>\n      <th>Life Expectancy</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>...</th>\n      <th>Polio</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</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    </tr>\n    <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    </tr>\n    <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</table>\n<p>5 rows × 22 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1 = data1.drop('Country', axis = 1)\n",
    "data1.head()"
   ],
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "   Year      Status  Life Expectancy  Adult Mortality  Infant Deaths  Alcohol  \\\n0  2015  Developing             65.0            263.0             62     0.01   \n1  2014  Developing             59.9            271.0             64     0.01   \n2  2013  Developing             59.9            268.0             66     0.01   \n3  2012  Developing             59.5            272.0             69     0.01   \n4  2011  Developing             59.2            275.0             71     0.01   \n\n   Percentage Expenditure  Hepatitis B  Measles   BMI  ...  Polio  \\\n0               71.279624         65.0     1154  19.1  ...    6.0   \n1               73.523582         62.0      492  18.6  ...   58.0   \n2               73.219243         64.0      430  18.1  ...   62.0   \n3               78.184215         67.0     2787  17.6  ...   67.0   \n4                7.097109         68.0     3013  17.2  ...   68.0   \n\n   Total Expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n0               8.16        65.0       0.1  584.259210  33736494.0   \n1               8.18        62.0       0.1  612.696514    327582.0   \n2               8.13        64.0       0.1  631.744976  31731688.0   \n3               8.52        67.0       0.1  669.959000   3696958.0   \n4               7.87        68.0       0.1   63.537231   2978599.0   \n\n   Thinness 1-19 years  Thinness 5-9 years  Income Composition of Resources  \\\n0                 17.2                17.3                            0.479   \n1                 17.5                17.5                            0.476   \n2                 17.7                17.7                            0.470   \n3                 17.9                18.0                            0.463   \n4                 18.2                18.2                            0.454   \n\n   Schooling  \n0       10.1  \n1       10.0  \n2        9.9  \n3        9.8  \n4        9.5  \n\n[5 rows x 21 columns]",
      "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>Year</th>\n      <th>Status</th>\n      <th>Life Expectancy</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>BMI</th>\n      <th>...</th>\n      <th>Polio</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</th>\n      <th>Schooling</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\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>19.1</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    </tr>\n    <tr>\n      <th>1</th>\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>18.6</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    </tr>\n    <tr>\n      <th>2</th>\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>18.1</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>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>17.6</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>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>17.2</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</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1['Status'] = data1['Status'].map({'Developing' : 0, 'Developed' : 1})"
   ],
   "execution_count": 15,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1.head(5)"
   ],
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "   Year  Status  Life Expectancy  Adult Mortality  Infant Deaths  Alcohol  \\\n0  2015       0             65.0            263.0             62     0.01   \n1  2014       0             59.9            271.0             64     0.01   \n2  2013       0             59.9            268.0             66     0.01   \n3  2012       0             59.5            272.0             69     0.01   \n4  2011       0             59.2            275.0             71     0.01   \n\n   Percentage Expenditure  Hepatitis B  Measles   BMI  ...  Polio  \\\n0               71.279624         65.0     1154  19.1  ...    6.0   \n1               73.523582         62.0      492  18.6  ...   58.0   \n2               73.219243         64.0      430  18.1  ...   62.0   \n3               78.184215         67.0     2787  17.6  ...   67.0   \n4                7.097109         68.0     3013  17.2  ...   68.0   \n\n   Total Expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n0               8.16        65.0       0.1  584.259210  33736494.0   \n1               8.18        62.0       0.1  612.696514    327582.0   \n2               8.13        64.0       0.1  631.744976  31731688.0   \n3               8.52        67.0       0.1  669.959000   3696958.0   \n4               7.87        68.0       0.1   63.537231   2978599.0   \n\n   Thinness 1-19 years  Thinness 5-9 years  Income Composition of Resources  \\\n0                 17.2                17.3                            0.479   \n1                 17.5                17.5                            0.476   \n2                 17.7                17.7                            0.470   \n3                 17.9                18.0                            0.463   \n4                 18.2                18.2                            0.454   \n\n   Schooling  \n0       10.1  \n1       10.0  \n2        9.9  \n3        9.8  \n4        9.5  \n\n[5 rows x 21 columns]",
      "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>Year</th>\n      <th>Status</th>\n      <th>Life Expectancy</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>BMI</th>\n      <th>...</th>\n      <th>Polio</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</th>\n      <th>Schooling</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2015</td>\n      <td>0</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>19.1</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014</td>\n      <td>0</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>18.6</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>0</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>18.1</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>2012</td>\n      <td>0</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>17.6</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>2011</td>\n      <td>0</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>17.2</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</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "column_2 = ['Year', 'Status', 'Adult Mortality',\n",
    "       'Infant Deaths', 'Alcohol', 'Percentage Expenditure',\n",
    "       'Hepatitis B', 'Measles', 'BMI', 'Under-Five Deaths ', 'Polio',\n",
    "       'Total Expenditure', 'Diphtheria', 'HIV/AIDS', 'GDP', 'Population',\n",
    "       'Thinness 1-19 years', 'Thinness 5-9 years',\n",
    "       'Income Composition of Resources', 'Schooling', 'Life Expectancy']"
   ],
   "execution_count": 17,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data1 = data1[column_2]\n",
    "data1.head()"
   ],
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "   Year  Status  Adult Mortality  Infant Deaths  Alcohol  \\\n0  2015       0            263.0             62     0.01   \n1  2014       0            271.0             64     0.01   \n2  2013       0            268.0             66     0.01   \n3  2012       0            272.0             69     0.01   \n4  2011       0            275.0             71     0.01   \n\n   Percentage Expenditure  Hepatitis B  Measles   BMI  Under-Five Deaths   \\\n0               71.279624         65.0     1154  19.1                  83   \n1               73.523582         62.0      492  18.6                  86   \n2               73.219243         64.0      430  18.1                  89   \n3               78.184215         67.0     2787  17.6                  93   \n4                7.097109         68.0     3013  17.2                  97   \n\n   ...  Total Expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n0  ...               8.16        65.0       0.1  584.259210  33736494.0   \n1  ...               8.18        62.0       0.1  612.696514    327582.0   \n2  ...               8.13        64.0       0.1  631.744976  31731688.0   \n3  ...               8.52        67.0       0.1  669.959000   3696958.0   \n4  ...               7.87        68.0       0.1   63.537231   2978599.0   \n\n   Thinness 1-19 years  Thinness 5-9 years  Income Composition of Resources  \\\n0                 17.2                17.3                            0.479   \n1                 17.5                17.5                            0.476   \n2                 17.7                17.7                            0.470   \n3                 17.9                18.0                            0.463   \n4                 18.2                18.2                            0.454   \n\n   Schooling  Life Expectancy  \n0       10.1             65.0  \n1       10.0             59.9  \n2        9.9             59.9  \n3        9.8             59.5  \n4        9.5             59.2  \n\n[5 rows x 21 columns]",
      "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>Year</th>\n      <th>Status</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>BMI</th>\n      <th>Under-Five Deaths</th>\n      <th>...</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</th>\n      <th>Schooling</th>\n      <th>Life Expectancy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2015</td>\n      <td>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>19.1</td>\n      <td>83</td>\n      <td>...</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      <td>65.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014</td>\n      <td>0</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>18.6</td>\n      <td>86</td>\n      <td>...</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      <td>59.9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>0</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>18.1</td>\n      <td>89</td>\n      <td>...</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      <td>59.9</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012</td>\n      <td>0</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>17.6</td>\n      <td>93</td>\n      <td>...</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      <td>59.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>0</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>17.2</td>\n      <td>97</td>\n      <td>...</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      <td>59.2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data_preprocessed = data1.copy()"
   ],
   "execution_count": 19,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data_preprocessed.head()"
   ],
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 20,
     "data": {
      "text/plain": "   Year  Status  Adult Mortality  Infant Deaths  Alcohol  \\\n0  2015       0            263.0             62     0.01   \n1  2014       0            271.0             64     0.01   \n2  2013       0            268.0             66     0.01   \n3  2012       0            272.0             69     0.01   \n4  2011       0            275.0             71     0.01   \n\n   Percentage Expenditure  Hepatitis B  Measles   BMI  Under-Five Deaths   \\\n0               71.279624         65.0     1154  19.1                  83   \n1               73.523582         62.0      492  18.6                  86   \n2               73.219243         64.0      430  18.1                  89   \n3               78.184215         67.0     2787  17.6                  93   \n4                7.097109         68.0     3013  17.2                  97   \n\n   ...  Total Expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n0  ...               8.16        65.0       0.1  584.259210  33736494.0   \n1  ...               8.18        62.0       0.1  612.696514    327582.0   \n2  ...               8.13        64.0       0.1  631.744976  31731688.0   \n3  ...               8.52        67.0       0.1  669.959000   3696958.0   \n4  ...               7.87        68.0       0.1   63.537231   2978599.0   \n\n   Thinness 1-19 years  Thinness 5-9 years  Income Composition of Resources  \\\n0                 17.2                17.3                            0.479   \n1                 17.5                17.5                            0.476   \n2                 17.7                17.7                            0.470   \n3                 17.9                18.0                            0.463   \n4                 18.2                18.2                            0.454   \n\n   Schooling  Life Expectancy  \n0       10.1             65.0  \n1       10.0             59.9  \n2        9.9             59.9  \n3        9.8             59.5  \n4        9.5             59.2  \n\n[5 rows x 21 columns]",
      "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>Year</th>\n      <th>Status</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>BMI</th>\n      <th>Under-Five Deaths</th>\n      <th>...</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</th>\n      <th>Schooling</th>\n      <th>Life Expectancy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2015</td>\n      <td>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>19.1</td>\n      <td>83</td>\n      <td>...</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      <td>65.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014</td>\n      <td>0</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>18.6</td>\n      <td>86</td>\n      <td>...</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      <td>59.9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>0</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>18.1</td>\n      <td>89</td>\n      <td>...</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      <td>59.9</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012</td>\n      <td>0</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>17.6</td>\n      <td>93</td>\n      <td>...</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      <td>59.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>0</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>17.2</td>\n      <td>97</td>\n      <td>...</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      <td>59.2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data_preprocessed.info()"
   ],
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 2928 entries, 0 to 2937\nData columns (total 21 columns):\nYear                               2928 non-null int64\nStatus                             2928 non-null int64\nAdult Mortality                    2928 non-null float64\nInfant Deaths                      2928 non-null int64\nAlcohol                            2928 non-null float64\nPercentage Expenditure             2928 non-null float64\nHepatitis B                        2928 non-null float64\nMeasles                            2928 non-null int64\nBMI                                2928 non-null float64\nUnder-Five Deaths                  2928 non-null int64\nPolio                              2928 non-null float64\nTotal Expenditure                  2928 non-null float64\nDiphtheria                         2928 non-null float64\nHIV/AIDS                           2928 non-null float64\nGDP                                2928 non-null float64\nPopulation                         2928 non-null float64\nThinness 1-19 years                2928 non-null float64\nThinness 5-9 years                 2928 non-null float64\nIncome Composition of Resources    2928 non-null float64\nSchooling                          2928 non-null float64\nLife Expectancy                    2928 non-null float64\ndtypes: float64(16), int64(5)\nmemory usage: 503.2 KB\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data_with_targets = data_preprocessed.copy()"
   ],
   "execution_count": 22,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "targets = data_with_targets.iloc[:,-1]\n",
    "targets.head()"
   ],
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "0    65.0\n1    59.9\n2    59.9\n3    59.5\n4    59.2\nName: Life Expectancy, dtype: float64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "targets.shape"
   ],
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
      "text/plain": "(2928,)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "inputs = data_with_targets.iloc[:,:-1]\n",
    "inputs.head()"
   ],
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 25,
     "data": {
      "text/plain": "   Year  Status  Adult Mortality  Infant Deaths  Alcohol  \\\n0  2015       0            263.0             62     0.01   \n1  2014       0            271.0             64     0.01   \n2  2013       0            268.0             66     0.01   \n3  2012       0            272.0             69     0.01   \n4  2011       0            275.0             71     0.01   \n\n   Percentage Expenditure  Hepatitis B  Measles   BMI  Under-Five Deaths   \\\n0               71.279624         65.0     1154  19.1                  83   \n1               73.523582         62.0      492  18.6                  86   \n2               73.219243         64.0      430  18.1                  89   \n3               78.184215         67.0     2787  17.6                  93   \n4                7.097109         68.0     3013  17.2                  97   \n\n   Polio  Total Expenditure  Diphtheria  HIV/AIDS         GDP  Population  \\\n0    6.0               8.16        65.0       0.1  584.259210  33736494.0   \n1   58.0               8.18        62.0       0.1  612.696514    327582.0   \n2   62.0               8.13        64.0       0.1  631.744976  31731688.0   \n3   67.0               8.52        67.0       0.1  669.959000   3696958.0   \n4   68.0               7.87        68.0       0.1   63.537231   2978599.0   \n\n   Thinness 1-19 years  Thinness 5-9 years  Income Composition of Resources  \\\n0                 17.2                17.3                            0.479   \n1                 17.5                17.5                            0.476   \n2                 17.7                17.7                            0.470   \n3                 17.9                18.0                            0.463   \n4                 18.2                18.2                            0.454   \n\n   Schooling  \n0       10.1  \n1       10.0  \n2        9.9  \n3        9.8  \n4        9.5  ",
      "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>Year</th>\n      <th>Status</th>\n      <th>Adult Mortality</th>\n      <th>Infant Deaths</th>\n      <th>Alcohol</th>\n      <th>Percentage Expenditure</th>\n      <th>Hepatitis B</th>\n      <th>Measles</th>\n      <th>BMI</th>\n      <th>Under-Five Deaths</th>\n      <th>Polio</th>\n      <th>Total Expenditure</th>\n      <th>Diphtheria</th>\n      <th>HIV/AIDS</th>\n      <th>GDP</th>\n      <th>Population</th>\n      <th>Thinness 1-19 years</th>\n      <th>Thinness 5-9 years</th>\n      <th>Income Composition of Resources</th>\n      <th>Schooling</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2015</td>\n      <td>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>19.1</td>\n      <td>83</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014</td>\n      <td>0</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>18.6</td>\n      <td>86</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>0</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>18.1</td>\n      <td>89</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>2012</td>\n      <td>0</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>17.6</td>\n      <td>93</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>2011</td>\n      <td>0</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>17.2</td>\n      <td>97</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</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "inputs.shape"
   ],
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 26,
     "data": {
      "text/plain": "(2928, 20)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import train_test_split"
   ],
   "execution_count": 27,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "train_test_split(inputs, targets)"
   ],
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 28,
     "data": {
      "text/plain": "[      Year  Status  Adult Mortality  Infant Deaths    Alcohol  \\\n 2159  2007       0            295.0             20   6.780000   \n 1347  2014       0             22.0              5   6.290000   \n 2890  2015       0            224.0             37   4.614856   \n 1773  2006       0            434.0             78   1.200000   \n 2595  2005       0             12.0              0   1.750000   \n 1177  2008       1             58.0              0   8.490000   \n 7     2008       0            287.0             80   0.030000   \n 2862  2011       0            158.0              9   6.740000   \n 375   2008       0             84.0              0   0.670000   \n 212   2011       0             14.0              0   8.510000   \n 2911  2010       0            363.0             30   2.470000   \n 143   2000       1             96.0              0  13.200000   \n 586   2005       0            144.0             15   4.380000   \n 319   2000       0            243.0             15   2.320000   \n 424   2007       0            355.0             24   4.540000   \n 2395  2013       0            371.0             43   7.340000   \n 1486  2003       0            648.0              5   1.990000   \n 2106  2012       0            175.0              1  10.680000   \n 774   2011       0             16.0              6   5.910000   \n 141   2002       1              9.0              0  12.500000   \n 1522  2015       1            165.0              0   4.614856   \n 62    2001       0             44.0             97   2.580000   \n 621   2002       0            424.0              9   2.550000   \n 118   2009       1             66.0              1  10.620000   \n 1908  2000       0             45.0            576   9.230000   \n 1921  2004       1             77.0              0   6.220000   \n 1280  2001       0             74.0              1   2.530000   \n 2661  2003       0            191.0              0   5.530000   \n 1113  2008       0            218.0              1   7.320000   \n 536   2007       0            394.0             46   0.420000   \n ...    ...     ...              ...            ...        ...   \n 1371  2006       0            388.0             68   1.760000   \n 601   2006       0            262.0              2   0.250000   \n 2281  2015       0            168.0              0   4.614856   \n 2880  2009       0            134.0             29   3.860000   \n 1086  2003       0            338.0             34   0.180000   \n 1400  2009       0             87.0              1   0.100000   \n 2678  2002       0             19.0              4   1.170000   \n 2462  2010       0            243.0             62   1.770000   \n 2269  2011       0            127.0              1   9.560000   \n 1038  2003       0             81.0              0   9.460000   \n 521   2006       0             56.0             17   1.540000   \n 593   2014       0             23.0              1   0.010000   \n 2725  2004       0            485.0            104   9.890000   \n 2189  2010       0            162.0              0   7.000000   \n 648   2008       1            116.0              0  12.060000   \n 313   2006       0            219.0             11   3.230000   \n 1342  2003       0            127.0              4   0.540000   \n 2935  2002       0             73.0             25   4.430000   \n 1684  2014       0            122.0             31   5.260000   \n 1763  2000       0             16.0             27   0.450000   \n 1505  2000       0             39.0             14   4.460000   \n 2286  2010       0             18.0              0   6.510000   \n 2370  2006       0              2.0              0   0.990000   \n 1014  2011       0            263.0             40   1.640000   \n 1286  2011       1              6.0              2   6.980000   \n 757   2011       0            268.0              1   0.390000   \n 1453  2004       1             26.0              0   8.810000   \n 1913  2012       1             61.0              0   6.210000   \n 1960  2014       0            119.0              1   6.740000   \n 1849  2011       1             72.0              0   9.470000   \n \n       Percentage Expenditure  Hepatitis B  Measles        BMI  \\\n 2159                7.409772    97.000000       26  16.500000   \n 1347               24.444737    95.000000      321  52.300000   \n 2890                0.000000    69.000000      468  41.300000   \n 1773               58.596445    75.000000      183  18.900000   \n 2595                0.000000    53.000000        5  54.900000   \n 1177             7613.814999    80.960842        0  58.100000   \n 7                  25.873925    64.000000     1599  15.700000   \n 2862                0.000000    78.000000        0  59.900000   \n 375              2538.941033    96.000000        3  34.200000   \n 212               173.982563    91.000000        0  51.400000   \n 2911              184.364910    83.000000    15754   2.700000   \n 143              3557.455507    33.000000        0   5.100000   \n 586               531.980818    93.000000        0   5.500000   \n 319                 0.000000    77.000000      122  42.600000   \n 424                 1.726595    99.000000       43  14.800000   \n 2395              978.590529     7.000000       25  49.500000   \n 1486                5.300902    17.000000        1  26.400000   \n 2106                0.000000    94.000000       11  51.500000   \n 774               843.787699     8.000000        2  54.700000   \n 141              3979.057728    81.000000        0  51.200000   \n 1522                0.000000    94.000000       50  62.400000   \n 62                 30.359936    80.960842     9046  15.800000   \n 621                 0.000000    80.960842     2788   2.300000   \n 118              7172.275229    94.000000      104  63.400000   \n 1908               22.481776    80.960842   212183  16.900000   \n 1921             1009.825011    80.960842        7  55.900000   \n 1280               22.273326    96.000000       19  58.700000   \n 2661               78.722161    76.000000        0  36.000000   \n 1113              493.327929    93.000000        0   4.900000   \n 536                 5.962924    80.960842      441  16.100000   \n ...                      ...          ...      ...        ...   \n 1371               52.603854     8.000000     1847  16.900000   \n 601                77.028396    69.000000       85  19.600000   \n 2281                0.000000    98.000000        0  37.400000   \n 2880                0.000000    94.000000     6582  13.400000   \n 1086                2.395241    80.960842     2497  17.700000   \n 1400             3001.628237    99.000000        0  67.900000   \n 2678              262.818494    93.000000       98  49.700000   \n 2462              172.009788    75.000000      680  38.235394   \n 2269              915.319079    94.000000      370  57.700000   \n 1038             2124.921517    92.000000        0  59.200000   \n 521                46.901179    80.960842        3  18.600000   \n 593                73.865849    88.000000        0  23.700000   \n 2725               36.606093    62.000000      141  14.200000   \n 2189                0.000000    99.000000        0  49.300000   \n 648              2425.403891    97.000000       51  59.400000   \n 313                 0.000000    83.000000        0  46.700000   \n 1342              228.783811    97.000000       76  56.300000   \n 2935                0.000000    73.000000      304  26.300000   \n 1684              168.173753    84.000000        3  62.800000   \n 1763               63.421400    43.000000     7368  44.800000   \n 1505               12.197097    80.960842     5977   2.200000   \n 2286               16.492323    99.000000        0  33.500000   \n 2370              240.485120    99.000000        0  43.000000   \n 1014              225.221947    91.000000      120  26.100000   \n 1286             5439.691769    96.000000     5189  61.500000   \n 757                20.359747    87.000000       49  32.700000   \n 1453               80.032837    98.000000        0  56.600000   \n 1913             2094.190960    80.960842        4  59.800000   \n 1960             1842.463782     8.000000        0  57.100000   \n 1849             8342.405764    95.000000      597  65.300000   \n \n       Under-Five Deaths   Polio  Total Expenditure  Diphtheria  HIV/AIDS  \\\n 2159                  30   98.0           8.490000        97.0       5.0   \n 1347                   5   95.0           4.360000        95.0       0.1   \n 2890                  47   63.0           5.930163        69.0       0.1   \n 1773                 113   73.0           6.510000        78.0      16.3   \n 2595                   0   98.0           7.950000        97.0       0.1   \n 1177                   0   98.0           8.800000        98.0       0.1   \n 7                    110   64.0           8.330000        64.0       0.1   \n 2862                  10   78.0           5.240000        78.0       0.1   \n 375                    0   97.0           2.230000        98.0       0.1   \n 212                    0   91.0           6.670000        91.0       0.1   \n 2911                  45    8.0           4.410000        83.0       6.8   \n 143                    0   71.0           1.600000        81.0       0.1   \n 586                   18   93.0           5.820000        93.0       0.1   \n 319                   20   74.0           5.670000        75.0       0.1   \n 424                   36   93.0           1.300000        99.0       3.4   \n 2395                  54   73.0           8.780000        73.0       4.5   \n 1486                   7    9.0           7.130000         9.0      33.8   \n 2106                   1   92.0          11.870000        92.0       0.1   \n 774                    7   84.0           4.220000        84.0       0.7   \n 141                    0   82.0           1.270000        83.0       0.1   \n 1522                   0   93.0           5.930163        93.0       0.1   \n 62                   159   41.0           5.380000        38.0       2.1   \n 621                   14   41.0           2.450000        41.0       7.2   \n 118                    1   92.0           9.500000        92.0       0.1   \n 1908                 943   31.0           2.840000        29.0       4.9   \n 1921                   0   92.0           9.440000        92.0       0.1   \n 1280                   1   93.0           7.610000        92.0       0.1   \n 2661                   1   91.0           5.130000        91.0       0.5   \n 1113                   1   93.0           7.460000        93.0       0.8   \n 536                   80   38.0           3.350000        28.0       4.9   \n ...                  ...    ...                ...         ...       ...   \n 1371                 104   77.0           4.240000         8.0      10.3   \n 601                    2   69.0           4.570000        69.0       0.1   \n 2281                   0   97.0           5.930163        97.0       0.1   \n 2880                  35   97.0           6.400000        96.0       0.1   \n 1086                  55   61.0           3.450000        57.0       2.9   \n 1400                   1   99.0           3.870000        99.0       0.1   \n 2678                   5   96.0           5.320000        96.0       0.1   \n 2462                  92    9.0           7.970000         9.0       0.3   \n 2269                   1   94.0           9.720000        94.0       0.1   \n 1038                   1   93.0           8.610000        94.0       0.1   \n 521                   26   47.0           3.990000        51.0      10.0   \n 593                    2   87.0           6.750000        87.0       0.8   \n 2725                 164   58.0           8.900000        62.0       8.9   \n 2189                   0   99.0           4.710000        99.0       0.1   \n 648                    0   96.0           7.700000        96.0       0.1   \n 313                   15   82.0           5.190000        83.0       0.2   \n 1342                   4   97.0           9.150000        97.0       0.1   \n 2935                  40   73.0           6.530000        71.0      39.8   \n 1684                  36   87.0           6.300000        87.0       0.1   \n 1763                  32   95.0           4.180000        95.0       0.1   \n 1505                  20   56.0           5.910000        46.0       3.1   \n 2286                   0   99.0           3.590000        99.0       0.1   \n 2370                   0   99.0           6.660000        99.0       0.1   \n 1014                  58   91.0           4.810000        91.0       1.3   \n 1286                   2   96.0           9.270000        96.0       0.1   \n 757                    2   87.0           8.710000        87.0       2.7   \n 1453                   0   99.0           6.510000        99.0       0.1   \n 1913                   0   95.0           9.160000        95.0       0.1   \n 1960                   1    8.0           8.300000         8.0       0.1   \n 1849                   0   95.0          11.240000        95.0       0.1   \n \n                GDP    Population  Thinness 1-19 years  Thinness 5-9 years  \\\n 2159     44.853339  9.447420e+05             6.800000            6.700000   \n 1347   1286.565110  1.728922e+07             2.400000            2.500000   \n 2890   7494.210719  1.276454e+07            13.600000           13.400000   \n 1773    385.756714  2.154746e+07             3.800000            3.700000   \n 2595   7494.210719  1.276454e+07             2.500000            2.600000   \n 1177  55575.291960  3.174140e+05             0.900000            0.900000   \n 7       373.361116  2.729431e+06            18.800000           18.900000   \n 2862   7494.210719  1.276454e+07             1.600000            1.500000   \n 375   37951.286000  1.276454e+07             6.100000            5.600000   \n 212   15534.157400  1.276454e+07             3.800000            3.800000   \n 2911   1463.213573  1.385330e+05             6.700000            6.500000   \n 143   24517.267450  8.115660e+05             1.700000            1.900000   \n 586    3386.256000  4.328563e+07             2.400000            2.100000   \n 319    7494.210719  1.276454e+07             1.500000            1.400000   \n 424      17.799950  7.939573e+06             8.000000            8.000000   \n 2395   6876.953821  5.331196e+07             5.400000            6.500000   \n 1486     63.636279  1.918970e+05             1.200000            1.100000   \n 2106   7494.210719  1.276454e+07             2.700000            2.900000   \n 774    5759.643000  1.279500e+04             3.300000            3.200000   \n 141   26351.375680  8.819570e+05             1.700000            1.900000   \n 1522  14252.428530  2.949100e+04             2.600000            2.600000   \n 62      526.168743  1.698327e+07             1.700000            1.700000   \n 621    7494.210719  1.276454e+07             9.300000            8.900000   \n 118   42742.998980  2.169170e+05             0.700000            0.600000   \n 1908    379.119326  1.223529e+06            14.300000           14.400000   \n 1921   5757.269160  4.591910e+05             0.800000            0.700000   \n 1280    236.196460  6.439000e+03             1.100000            1.100000   \n 2661    884.518666  1.284520e+05             6.400000            6.900000   \n 1113   2576.124957  7.463140e+05             5.600000            5.400000   \n 536      81.683884  1.775780e+05             1.000000            1.000000   \n ...            ...           ...                  ...                 ...   \n 1371    697.663850  3.752500e+04             8.500000            8.400000   \n 601     648.387170  6.264250e+05             7.400000            7.300000   \n 2281   1539.464000  9.341900e+04             5.700000            6.000000   \n 2880   7494.210719  1.276454e+07            14.600000           15.500000   \n 1086     37.193178  9.398480e+05             9.700000            9.700000   \n 1400  37567.312100  1.276454e+07             3.300000            3.200000   \n 2678   2346.593700  9.864326e+06             6.500000            6.400000   \n 2462   1476.478870  3.438596e+07             4.850622            4.881423   \n 2269   6423.291783  7.234990e+05             2.200000            2.200000   \n 1038  18477.578410  1.928700e+04             0.800000            0.800000   \n 521     347.673678  4.217580e+05             9.600000            9.600000   \n 593     852.954379  7.593850e+05             6.800000            6.600000   \n 2725    288.236949  2.756844e+07             6.600000            6.600000   \n 2189   7494.210719  1.276454e+07             3.500000            3.500000   \n 648   15893.865600  4.434580e+05             1.700000            1.700000   \n 313    7494.210719  1.276454e+07             1.300000            1.200000   \n 1342   1889.213962  5.396774e+06             3.900000            3.900000   \n 2935     57.348340  1.255250e+05             1.200000            1.300000   \n 1684   1452.277660  1.242216e+06             1.600000            1.500000   \n 1763   1332.382358  2.884962e+07             6.700000            6.600000   \n 1505    183.414999  2.884522e+06             9.200000            9.200000   \n 2286    184.684470  8.977000e+03             5.800000            6.200000   \n 2370    948.659251  4.814220e+05             1.300000            1.300000   \n 1014   1574.978653  2.512180e+07             6.900000            6.800000   \n 1286  38334.684770  5.937945e+07             0.500000            0.500000   \n 757     143.986898  8.659370e+05             5.600000            5.500000   \n 1453   6351.812500  2.263122e+06             2.500000            2.600000   \n 1913  11563.727000  5.185730e+05             0.700000            0.700000   \n 1960  12593.737400  3.939860e+05             1.900000            1.800000   \n 1849  38426.558100  1.276454e+07             0.300000            0.300000   \n \n       Income Composition of Resources  Schooling  \n 2159                         0.424000  10.100000  \n 1347                         0.789000  15.000000  \n 2890                         0.499000   9.000000  \n 1773                         0.353000   7.900000  \n 2595                         0.000000  11.900000  \n 1177                         0.892000  18.200000  \n 7                            0.433000   8.700000  \n 2862                         0.756000  14.100000  \n 375                          0.840000  14.200000  \n 212                          0.780000  15.500000  \n 2911                         0.533000  11.800000  \n 143                          0.833000  15.400000  \n 586                          0.658000  11.100000  \n 319                          0.600000  13.300000  \n 424                          0.309000   7.200000  \n 2395                         0.652000  12.800000  \n 1486                         0.440000  10.500000  \n 2106                         0.627419  11.999639  \n 774                          0.703000  13.000000  \n 141                          0.847000  16.100000  \n 1522                         0.846000  16.500000  \n 62                           0.391000   5.100000  \n 621                          0.488000   9.500000  \n 118                          0.925000  19.100000  \n 1908                         0.000000   7.600000  \n 1921                         0.924000  17.500000  \n 1280                         0.850000  15.100000  \n 2661                         0.729000  12.000000  \n 1113                         0.618000  10.700000  \n 536                          0.306000   5.700000  \n ...                               ...        ...  \n 1371                         0.483000   9.700000  \n 601                          0.451000   9.900000  \n 2281                         0.781000  14.100000  \n 2880                         0.641000  11.700000  \n 1086                         0.338000   5.900000  \n 1400                         0.788000  13.600000  \n 2678                         0.662000  13.300000  \n 2462                         0.461000   7.000000  \n 2269                         0.757000  13.500000  \n 1038                         0.823000  15.200000  \n 521                          0.323000   6.000000  \n 593                          0.497000  11.100000  \n 2725                         0.427000  11.600000  \n 2189                         0.711000  13.300000  \n 648                          0.800000  14.300000  \n 313                          0.625000  14.300000  \n 1342                         0.717000  13.200000  \n 2935                         0.427000  10.000000  \n 1684                         0.754000  13.100000  \n 1763                         0.519000   8.000000  \n 1505                         0.338000   8.700000  \n 2286                         0.740000  13.200000  \n 2370                         0.470000   8.400000  \n 1014                         0.554000  10.900000  \n 1286                         0.872000  16.400000  \n 757                          0.451000   5.900000  \n 1453                         0.776000  15.500000  \n 1913                         0.941000  17.600000  \n 1960                         0.780000  13.000000  \n 1849                         0.901000  19.700000  \n \n [2196 rows x 20 columns],\n       Year  Status  Adult Mortality  Infant Deaths    Alcohol  \\\n 2495  2009       0             46.0              3   4.900000   \n 1489  2000       0            543.0              5   3.100000   \n 439   2008       0            437.0             60   2.690000   \n 1211  2006       0            191.0            159   0.060000   \n 664   2008       0             12.0              1   4.260000   \n 1256  2009       1             77.0              0  11.410000   \n 1080  2009       0            297.0             29   0.180000   \n 1780  2015       0            199.0             39   4.614856   \n 2681  2015       0             16.0             15   4.614856   \n 65    2014       0            131.0              0   8.560000   \n 24    2007       0              9.0              1   5.580000   \n 453   2010       0            123.0              0   4.750000   \n 2117  2001       0            228.0              1  10.980000   \n 1066  2007       0            192.0             13   2.400000   \n 2439  2001       1             84.0              2   9.860000   \n 2352  2008       1             96.0              0  10.940000   \n 1184  2001       1             72.0              0   6.370000   \n 307   2012       0            194.0              8   3.890000   \n 180   2011       0             76.0              0   1.660000   \n 1779  2000       0             43.0             93   1.140000   \n 2041  2013       1             12.0              2  11.630000   \n 1381  2012       0             24.0              0   0.010000   \n 2909  2012       0            349.0             29   2.590000   \n 1536  2001       1             21.0              0  10.200000   \n 173   2002       0             19.0              0  10.850000   \n 2239  2009       0             97.0              8   0.090000   \n 1886  2006       0            277.0             54   0.110000   \n 401   2014       0            268.0             39   0.010000   \n 1229  2004       0            139.0             28   0.010000   \n 2374  2002       0             22.0              0   1.080000   \n ...    ...     ...              ...            ...        ...   \n 1201  2000       0            224.0           1800   0.930000   \n 1476  2013       0            518.0              4   0.010000   \n 2876  2013       0            129.0             28   4.000000   \n 553   2006       0             91.0              2   7.330000   \n 2774  2003       1             86.0              4  11.850000   \n 2270  2010       0            128.0              1   9.650000   \n 166   2009       0            168.0              0   9.290000   \n 1711  2003       0             18.0              0   3.150000   \n 2064  2006       1             96.0              0  13.110000   \n 2205  2010       0            144.0              0   3.010000   \n 1004  2005       1             85.0              3  11.670000   \n 2086  2000       0             88.0              0   0.500000   \n 1196  2005       0            211.0           1500   1.270000   \n 2448  2008       0            172.0              4   2.100000   \n 2342  2002       1            145.0              0  10.780000   \n 2295  2001       0            187.0              0   6.720000   \n 2181  2002       0            174.0              0  11.850000   \n 1793  2002       0            235.0             71   0.410000   \n 2333  2011       1            122.0              0  10.240000   \n 273   2014       0            252.0             25   0.010000   \n 131   2012       1              7.0              0  12.260000   \n 2263  2001       0            251.0             26   0.340000   \n 2829  2012       0            184.0             18   0.010000   \n 1473  2000       0            112.0              1   2.260000   \n 737   2015       1             71.0              0   4.614856   \n 98    2013       0            123.0              1   3.790000   \n 1843  2001       1             82.0              1   9.950000   \n 2692  2004       0            132.0             33   1.350000   \n 2859  2014       0            158.0              9   6.470000   \n 2094  2008       0             75.0              2   9.310000   \n \n       Percentage Expenditure  Hepatitis B  Measles   BMI  Under-Five Deaths   \\\n 2495               47.616452    88.000000       26  29.3                   4   \n 1489               29.866165    80.960842      660  24.9                   7   \n 439                 0.000000    74.000000       12  23.8                  85   \n 1211               72.015932    66.000000    20422  19.7                 194   \n 664               708.615797    95.000000        0  56.2                   1   \n 1256             7620.823456    80.960842      164  58.2                   0   \n 1080               24.847636    57.000000      264   2.3                  46   \n 1780                0.000000    89.000000        6  23.8                  50   \n 2681                0.000000    97.000000      342  66.1                  18   \n 65               2422.999774    99.000000        0  47.0                   0   \n 24                 32.246552    98.000000       22  51.7                   1   \n 453               286.890730    98.000000        0  27.5                   0   \n 2117                0.000000    94.000000      457  46.8                   1   \n 1066              380.963243    85.000000        0  45.2                  16   \n 2439             2044.169761    83.000000        0  58.2                   2   \n 2352              392.958854    80.960842        0  55.8                   0   \n 1184             5048.274426    80.960842        0  54.8                   0   \n 307                 0.000000    93.000000        0   5.6                  11   \n 180               197.773468    99.000000        0   6.8                   0   \n 1779               47.172507    80.960842     7375  16.5                 136   \n 2041               23.435335    96.000000       84   6.4                   2   \n 1381              147.454975    94.000000        0  76.2                   0   \n 2909              196.915250    78.000000      896  21.7                  43   \n 1536               42.872087    95.000000        7  57.2                   0   \n 173                 0.000000    89.000000        0  56.0                   0   \n 2239              117.922800    98.000000       82  63.1                  10   \n 1886               43.421931    80.960842       59  15.7                 110   \n 401                 8.386340    91.000000      343  18.8                  63   \n 1229                0.000000    95.000000        3  47.6                  33   \n 2374              156.284797    75.000000        0  39.6                   0   \n ...                      ...          ...      ...   ...                 ...   \n 1201               19.266157    80.960842    38835  11.4                2500   \n 1476              153.344315    93.000000      516  31.4                   6   \n 2876                0.000000    59.000000     1123  16.0                  35   \n 553              1275.689625    95.000000        0  58.1                   2   \n 2774                0.000000    80.960842      460  59.5                   4   \n 2270              774.439662    89.000000       20  57.0                   1   \n 166                 0.000000    95.000000        0   6.7                   0   \n 1711                0.000000    89.000000        0  62.8                   0   \n 2064             2884.020194    97.000000        0  54.9                   0   \n 2205              434.794068    57.000000        8  72.5                   0   \n 1004             6012.925840     9.000000      778  57.6                   3   \n 2086             1559.287158    89.000000       46  62.4                   0   \n 1196                3.509637     8.000000    36711  13.5                2000   \n 2448               17.814243    98.000000       33  18.5                   4   \n 2342                0.000000    99.000000        0  51.6                   0   \n 2295              643.703575    96.000000        0  27.6                   0   \n 2181                0.000000    14.000000        0  37.9                   0   \n 1793                3.421881    80.960842      736  14.6                  96   \n 2333                0.000000    99.000000        0  56.5                   0   \n 273                90.122068    78.000000      786  25.2                  39   \n 131              7878.372355    92.000000       36  56.1                   0   \n 2263                6.064221    80.960842    24789  17.9                  49   \n 2829               16.714063    99.000000        0  42.2                  21   \n 1473              404.387943    83.000000        5  57.9                   1   \n 737                 0.000000    80.960842        9  58.8                   0   \n 98                279.429081    95.000000       10  53.3                   1   \n 1843             3054.515582    80.960842        0  52.6                   1   \n 2692                1.132767    77.000000     8927  56.8                  39   \n 2859                0.000000    78.000000        0  61.5                  10   \n 2094                0.000000    94.000000        2  28.6                   2   \n \n       Polio  Total Expenditure  Diphtheria  HIV/AIDS           GDP  \\\n 2495   89.0           8.460000        88.0      33.7    332.517125   \n 1489   82.0           6.920000        83.0      29.8    474.819788   \n 439    58.0           6.210000        74.0       4.1   7494.210719   \n 1211   78.0           2.910000        72.0       0.1   1586.254000   \n 664    99.0           1.790000        95.0       0.1   5376.447622   \n 1256   94.0           9.530000        94.0       0.1  51983.788920   \n 1080    6.0           3.850000        57.0       2.3    436.689554   \n 1780   89.0           5.930163        89.0       0.3   1194.591370   \n 2681   97.0           5.930163        97.0       0.1   1979.525690   \n 65     96.0           5.540000        99.0       0.2  12888.296670   \n 24     99.0           6.100000        98.0       0.1    363.136850   \n 453    99.0           4.830000        99.0       0.4   3312.825985   \n 2117   98.0           7.000000        97.0       0.1   7494.210719   \n 1066   85.0           6.860000        85.0       0.4   2489.955836   \n 2439   95.0           7.240000        96.0       0.1  15323.611400   \n 2352   97.0           8.470000        97.0       0.1   2751.812700   \n 1184   92.0           9.120000        92.0       0.1  28585.925400   \n 307    93.0           5.560000        93.0       0.1   7494.210719   \n 180    99.0           3.400000        99.0       0.1   2281.124200   \n 1779   69.0           6.160000         7.0      12.2    277.648659   \n 2041   95.0           6.400000        99.0       0.1   1378.549110   \n 1381   92.0           1.370000        94.0       0.1   1763.815497   \n 2909    7.0           4.910000        78.0       5.6   1734.936120   \n 1536   97.0           6.260000        95.0       0.1    353.147337   \n 173    93.0           5.260000        94.0       0.1   7494.210719   \n 2239   98.0           4.900000        98.0       0.1   1694.293100   \n 1886   52.0           7.390000        51.0       1.5    258.463873   \n 401    91.0           4.960000        91.0       0.6     75.146411   \n 1229   98.0           5.890000        99.0       0.1   7494.210719   \n 2374   78.0           6.900000        78.0       0.1    784.956287   \n ...     ...                ...         ...       ...           ...   \n 1201   57.0           4.260000        58.0       0.3    438.864634   \n 1476    9.0          11.700000        93.0       9.6   1196.133500   \n 2876   93.0           7.170000        59.0       0.1   7494.210719   \n 553    94.0           6.180000        94.0       0.1   9484.681227   \n 2774   91.0           7.810000        91.0       0.1   7494.210719   \n 2270   91.0           1.900000        91.0       0.1   5411.877440   \n 166    97.0           7.430000        96.0       0.1   7494.210719   \n 1711   88.0           9.690000        92.0       0.1   7494.210719   \n 2064   97.0           9.670000        97.0       0.1  19821.444630   \n 2205   57.0           5.700000        62.0       0.1   3453.487434   \n 1004   95.0           1.520000        96.0       0.1  34696.629200   \n 2086   91.0           2.180000         8.0       0.1  29986.291500   \n 1196   65.0           4.280000        65.0       0.3     77.819000   \n 2448   98.0           3.440000        98.0       0.1    254.489192   \n 2342   98.0           5.630000        99.0       0.1   7494.210719   \n 2295   96.0           4.450000        96.0       0.1   7663.137800   \n 2181    9.0           6.330000        74.0       0.1   7494.210719   \n 1793   84.0           2.500000        79.0       0.4    143.776510   \n 2333   99.0           7.960000        99.0       0.1   7494.210719   \n 273    74.0           4.590000        78.0       1.1    943.686575   \n 131    92.0          11.170000        92.0       0.1  48333.572730   \n 2263   49.0           4.980000        52.0       0.6    481.287397   \n 2829   99.0           6.490000        99.0       0.1    174.468298   \n 1473   83.0           1.860000        83.0       0.1   5334.933290   \n 737    93.0           5.930163        93.0       0.1   5314.644160   \n 98     96.0           4.550000        95.0       0.1   3843.591213   \n 1843   97.0           7.790000        97.0       0.1  26584.121690   \n 2692   85.0           5.370000        85.0       0.1     64.729530   \n 2859   79.0           5.260000        78.0       0.1   7494.210719   \n 2094   92.0           6.160000        94.0       0.1   7494.210719   \n \n         Population  Thinness 1-19 years  Thinness 5-9 years  \\\n 2495  1.186750e+05                  5.8                 5.9   \n 1489  1.868699e+06                 11.5                11.6   \n 439   1.276454e+07                  6.6                 6.6   \n 1211  2.298382e+07                  1.8                 1.8   \n 664   1.276454e+07                  3.4                 3.2   \n 1256  4.535375e+06                  0.3                 0.2   \n 1080  1.556524e+06                  8.4                 8.4   \n 1780  5.243669e+06                 12.8                13.0   \n 2681  7.827147e+07                  4.9                 4.8   \n 65    1.276454e+07                  3.3                 3.3   \n 24    2.971700e+04                  1.6                 1.7   \n 453   5.238400e+04                  7.4                 7.4   \n 2117  1.276454e+07                  3.5                 3.6   \n 1066  1.372860e+05                  1.3                 1.3   \n 2439  4.854120e+05                  0.6                 0.5   \n 2352  2.213160e+05                  1.7                 1.7   \n 1184  2.849680e+05                  1.0                 0.9   \n 307   1.276454e+07                  1.2                 1.1   \n 180   1.276454e+07                  6.1                 6.0   \n 1779  1.867687e+06                  4.2                 4.1   \n 2041  3.841960e+05                  2.0                 2.1   \n 1381  1.661300e+04                  0.1                 0.1   \n 2909  1.469994e+07                  6.5                 6.3   \n 1536  3.478180e+05                  3.3                 3.3   \n 173   1.276454e+07                  2.6                 2.6   \n 2239  1.276454e+07                  7.3                 7.3   \n 1886  1.413264e+06                 11.6                11.5   \n 401   1.758598e+07                  8.2                 7.7   \n 1229  1.276454e+07                  7.4                 7.7   \n 2374  4.352620e+05                  1.4                 1.4   \n ...            ...                  ...                 ...   \n 1201  1.535912e+06                 27.7                28.6   \n 1476  2.117361e+06                  6.1                 5.9   \n 2876  1.276454e+07                 14.3                14.9   \n 553   1.631979e+07                  0.9                 0.9   \n 2774  1.276454e+07                  0.7                 0.5   \n 2270  7.291436e+06                  2.2                 2.3   \n 166   1.276454e+07                  2.5                 2.5   \n 1711  1.276454e+07                  0.2                 0.2   \n 2064  1.522288e+06                  0.7                 0.5   \n 2205  1.862500e+04                  0.2                 0.2   \n 1004  8.246942e+07                  1.1                 1.1   \n 2086  1.276454e+07                  4.8                 4.5   \n 1196  1.144119e+09                 27.2                28.1   \n 2448  1.981700e+04                 15.4                15.4   \n 2342  1.276454e+07                  1.5                 1.6   \n 2295  8.122000e+03                  6.2                 6.6   \n 2181  1.276454e+07                  4.4                 4.5   \n 1793  4.714220e+05                 13.3                13.7   \n 2333  1.276454e+07                  1.2                 1.3   \n 273   1.286712e+06                  7.1                 6.9   \n 131   8.429991e+06                  1.8                 2.0   \n 2263  1.134497e+06                 12.1                12.1   \n 2829  2.977450e+05                  3.0                 3.1   \n 1473  3.235366e+06                  4.9                 4.8   \n 737   5.683483e+06                  1.1                 0.9   \n 98    2.893590e+05                  2.1                 2.1   \n 1843  1.646180e+05                  1.1                 1.1   \n 2692  6.778550e+05                  5.1                 4.9   \n 2859  1.276454e+07                  1.6                 1.5   \n 2094  1.276454e+07                  1.5                 1.0   \n \n       Income Composition of Resources  Schooling  \n 2495                         0.519000  10.800000  \n 1489                         0.445000   9.600000  \n 439                          0.627419  11.999639  \n 1211                         0.632000  10.900000  \n 664                          0.771000  17.100000  \n 1256                         0.909000  17.900000  \n 1080                         0.376000   8.100000  \n 1780                         0.552000   9.100000  \n 2681                         0.764000  14.500000  \n 65                           0.782000  13.900000  \n 24                           0.703000  11.600000  \n 453                          0.627000  12.700000  \n 2117                         0.627419  11.999639  \n 1066                         0.578000  10.100000  \n 2439                         0.825000  15.700000  \n 2352                         0.869000  16.900000  \n 1184                         0.854000  17.100000  \n 307                          0.655000  13.800000  \n 180                          0.812000  14.400000  \n 1779                         0.291000   5.400000  \n 2041                         0.838000  15.400000  \n 1381                         0.581000  11.900000  \n 2909                         0.554000  12.300000  \n 1536                         0.757000  14.700000  \n 173                          0.781000  12.100000  \n 2239                         0.787000  13.000000  \n 1886                         0.286000   3.700000  \n 401                          0.398000   7.700000  \n 1229                         0.686000  11.800000  \n 2374                         0.446000   7.300000  \n ...                               ...        ...  \n 1201                         0.489000   8.300000  \n 1476                         0.484000  11.100000  \n 2876                         0.668000  12.300000  \n 553                          0.796000  14.900000  \n 2774                         0.627419  11.999639  \n 2270                         0.755000  13.600000  \n 166                          0.791000  12.600000  \n 1711                         0.612000  10.400000  \n 2064                         0.793000  15.400000  \n 2205                         0.690000  12.900000  \n 1004                         0.884000  16.400000  \n 2086                         0.808000  13.000000  \n 1196                         0.526000   9.400000  \n 2448                         0.731000  13.300000  \n 2342                         0.763000  13.300000  \n 2295                         0.714000  12.200000  \n 2181                         0.681000  12.500000  \n 1793                         0.435000   7.600000  \n 2333                         0.829000  15.000000  \n 273                          0.475000  10.700000  \n 131                          0.884000  15.700000  \n 2263                         0.381000   5.400000  \n 2829                         0.673000  12.000000  \n 1473                         0.000000  14.900000  \n 737                          0.923000  19.200000  \n 98                           0.736000  12.700000  \n 1843                         0.878000  16.700000  \n 2692                         0.675000  12.000000  \n 2859                         0.771000  14.200000  \n 2094                         0.627419  11.999639  \n \n [732 rows x 20 columns],\n 2159    59.6\n 1347    69.9\n 2890    65.7\n 1773    51.2\n 2595    73.6\n 1177    81.4\n 7       58.1\n 2862    73.8\n 375     77.2\n 212     74.9\n 2911    58.0\n 143     78.1\n 586     73.1\n 319     62.6\n 424     54.8\n 2395    69.0\n 1486    45.5\n 2106    79.0\n 774     73.1\n 141     78.7\n 1522    73.6\n 62      45.7\n 621     52.6\n 118     81.7\n 1908    47.1\n 1921    79.8\n 1280    79.3\n 2661    69.3\n 1113    66.3\n 536     49.4\n         ... \n 1371    55.3\n 601     63.0\n 2281    73.2\n 2880    75.0\n 1086    53.3\n 1400    73.9\n 2678    73.5\n 2462    62.5\n 2269    74.6\n 1038    79.1\n 521     46.3\n 593     63.2\n 2725    51.3\n 2189    72.5\n 648     76.0\n 313     66.2\n 1342    72.3\n 2935    44.8\n 1684    76.6\n 1763    68.6\n 1505    51.9\n 2286    72.4\n 2370    67.6\n 1014    61.2\n 1286    82.0\n 757     61.8\n 1453    71.0\n 1913    81.3\n 1960    77.6\n 1849    86.0\n Name: Life Expectancy, Length: 2196, dtype: float64,\n 2495    52.6\n 1489    49.3\n 439     54.0\n 1211    67.3\n 664     77.9\n 1256    79.7\n 1080    57.3\n 1780    66.6\n 2681    75.8\n 65      76.2\n 24      75.9\n 453     72.5\n 2117    67.6\n 1066    75.0\n 2439    79.4\n 2352    78.9\n 1184    80.0\n 307     69.8\n 180     76.1\n 1779    49.0\n 2041    77.1\n 1381    65.7\n 2909    59.2\n 1536    71.2\n 173     73.1\n 2239    73.4\n 1886    54.5\n 401     59.3\n 1229    71.8\n 2374    66.5\n         ... \n 1201    62.5\n 1476    52.1\n 2876    75.7\n 553     78.9\n 2774    78.3\n 2270    74.4\n 166     74.6\n 1711    67.5\n 2064    78.5\n 2205    72.6\n 1004    79.2\n 2086    76.2\n 1196    64.4\n 2448    72.3\n 2342    73.7\n 2295    72.0\n 2181    72.0\n 1793    62.8\n 2333    75.6\n 273     59.7\n 131     88.0\n 2263    57.9\n 2829    68.8\n 1473    72.7\n 737     86.0\n 98      74.4\n 1843    78.3\n 2692    72.0\n 2859    73.9\n 2094    83.0\n Name: Life Expectancy, Length: 732, dtype: float64]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(inputs, targets, train_size = 0.8, random_state = 20)"
   ],
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n  FutureWarning)\n",
     "name": "stderr"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "print(x_train.shape, y_train.shape)"
   ],
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "stream",
     "text": "(2342, 20) (2342,)\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "print(x_test.shape, y_test.shape)"
   ],
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "stream",
     "text": "(586, 20) (586,)\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ],
   "execution_count": 32,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)"
   ],
   "execution_count": 33,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "regressor.fit(x_train, y_train)"
   ],
   "execution_count": 34,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 34,
     "data": {
      "text/plain": "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n           max_features='auto', max_leaf_nodes=None,\n           min_impurity_decrease=0.0, min_impurity_split=None,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n           oob_score=False, random_state=0, verbose=0, warm_start=False)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "regressor.score(x_train, y_train)"
   ],
   "execution_count": 35,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 35,
     "data": {
      "text/plain": "0.9917162434333671"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "pred = regressor.predict(x_test)\n",
    "pred"
   ],
   "execution_count": 36,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 36,
     "data": {
      "text/plain": "array([78.72, 49.76, 80.55, 62.94, 49.51, 73.62, 73.9 , 74.3 , 53.53,\n       76.71, 79.6 , 74.18, 55.07, 82.31, 74.22, 74.2 , 72.85, 81.9 ,\n       80.39, 75.41, 74.28, 72.61, 52.39, 75.06, 62.86, 83.85, 49.26,\n       80.31, 62.15, 70.84, 79.2 , 44.42, 75.08, 64.78, 74.82, 63.35,\n       67.88, 57.64, 82.34, 79.75, 54.58, 55.45, 72.93, 63.82, 74.05,\n       69.93, 54.8 , 63.34, 56.95, 67.15, 56.46, 76.04, 72.71, 45.93,\n       71.96, 76.66, 82.51, 82.34, 52.94, 72.97, 75.68, 56.55, 64.02,\n       68.76, 68.38, 83.02, 76.61, 83.34, 64.35, 76.67, 72.27, 60.1 ,\n       74.01, 74.86, 62.39, 65.96, 58.21, 79.83, 54.59, 65.35, 57.09,\n       72.41, 79.36, 66.  , 73.33, 74.18, 69.49, 72.94, 72.12, 74.78,\n       82.76, 54.27, 73.72, 51.73, 64.01, 74.67, 55.84, 61.15, 63.12,\n       67.46, 60.08, 67.53, 55.77, 63.77, 64.39, 59.4 , 72.04, 68.76,\n       60.13, 47.13, 74.06, 69.49, 63.18, 68.46, 77.15, 67.56, 58.79,\n       71.57, 69.64, 78.68, 78.17, 67.32, 70.71, 72.06, 72.52, 62.28,\n       53.82, 74.87, 73.74, 72.96, 79.77, 65.42, 73.61, 63.16, 57.47,\n       73.73, 53.56, 72.76, 80.53, 61.23, 70.73, 81.83, 82.76, 54.65,\n       82.81, 76.46, 58.38, 63.16, 78.96, 73.75, 74.46, 65.49, 64.78,\n       74.91, 73.07, 65.47, 61.6 , 76.65, 72.39, 73.79, 57.26, 53.88,\n       71.36, 73.47, 74.75, 75.43, 53.06, 69.89, 72.18, 52.31, 56.09,\n       61.42, 78.3 , 79.54, 77.72, 75.92, 66.65, 51.69, 76.26, 52.58,\n       65.17, 56.07, 63.87, 75.7 , 77.81, 67.74, 73.25, 73.66, 77.62,\n       61.83, 82.37, 70.87, 70.28, 60.46, 47.73, 67.37, 58.47, 82.25,\n       67.51, 71.75, 72.9 , 81.13, 78.09, 55.28, 62.48, 73.27, 63.63,\n       57.04, 75.16, 74.82, 74.63, 72.67, 73.23, 77.46, 67.26, 57.56,\n       55.47, 63.31, 54.2 , 78.81, 75.12, 65.86, 73.66, 57.81, 68.94,\n       64.99, 62.9 , 72.36, 74.5 , 73.41, 74.09, 83.9 , 79.3 , 73.83,\n       82.68, 61.52, 58.75, 64.81, 66.97, 45.93, 76.39, 63.  , 79.23,\n       56.9 , 80.26, 67.11, 51.31, 79.36, 73.  , 72.91, 65.16, 75.91,\n       81.52, 57.17, 65.98, 73.34, 75.36, 72.94, 73.95, 64.25, 82.38,\n       73.12, 60.48, 58.39, 58.52, 77.15, 57.66, 73.84, 79.9 , 75.7 ,\n       73.68, 71.61, 70.78, 54.34, 66.79, 73.23, 84.64, 64.24, 73.08,\n       72.66, 52.9 , 76.37, 74.31, 66.48, 67.07, 82.18, 69.07, 71.26,\n       66.56, 77.81, 79.17, 69.78, 74.46, 66.73, 71.91, 58.89, 74.05,\n       72.12, 67.96, 55.39, 55.53, 53.73, 72.78, 69.99, 79.71, 74.9 ,\n       75.98, 75.53, 69.21, 81.65, 73.72, 60.15, 75.  , 77.21, 72.96,\n       70.08, 71.78, 69.39, 79.47, 70.47, 78.1 , 47.04, 83.21, 80.65,\n       63.8 , 73.74, 76.91, 68.28, 64.62, 53.48, 45.1 , 75.22, 74.84,\n       67.73, 78.44, 62.88, 74.06, 79.03, 73.55, 75.25, 79.67, 69.57,\n       52.66, 82.35, 70.67, 70.79, 80.92, 54.89, 73.43, 76.75, 51.87,\n       72.91, 75.17, 71.21, 56.21, 54.05, 78.03, 73.57, 72.63, 75.08,\n       73.74, 58.49, 80.29, 82.92, 82.45, 82.28, 69.98, 78.66, 69.94,\n       73.3 , 73.59, 67.75, 71.46, 74.42, 73.2 , 72.47, 51.56, 52.43,\n       82.51, 82.65, 70.41, 77.91, 55.77, 76.67, 47.48, 74.75, 62.68,\n       54.67, 74.25, 73.58, 52.7 , 62.21, 73.04, 61.12, 71.99, 83.68,\n       80.91, 78.47, 72.37, 76.97, 73.82, 53.64, 45.37, 60.85, 68.52,\n       47.36, 73.27, 61.01, 78.84, 76.96, 64.35, 66.74, 74.42, 75.21,\n       63.71, 75.03, 74.11, 62.26, 67.32, 82.76, 77.37, 58.07, 73.09,\n       58.67, 72.56, 63.33, 70.95, 59.15, 72.83, 62.96, 60.25, 73.64,\n       49.88, 71.99, 71.55, 64.54, 68.79, 78.73, 75.01, 74.45, 72.77,\n       69.99, 81.25, 82.19, 64.45, 53.83, 59.17, 79.78, 73.58, 67.75,\n       52.76, 52.13, 80.17, 52.36, 70.66, 69.36, 65.05, 52.  , 74.71,\n       70.16, 66.98, 72.32, 75.87, 55.92, 72.84, 81.37, 58.01, 75.11,\n       72.57, 60.26, 79.96, 67.12, 56.78, 57.12, 71.13, 57.02, 78.88,\n       78.6 , 72.28, 64.34, 68.49, 79.33, 54.3 , 76.11, 75.66, 73.2 ,\n       83.67, 74.55, 58.11, 72.64, 76.29, 64.06, 78.22, 52.17, 71.29,\n       81.73, 73.51, 79.11, 84.84, 72.57, 69.65, 73.43, 78.99, 57.63,\n       69.96, 52.25, 74.19, 83.27, 80.79, 71.95, 74.93, 78.44, 77.04,\n       75.37, 72.01, 54.17, 63.82, 80.3 , 79.04, 66.79, 53.73, 56.22,\n       63.96, 72.07, 64.22, 73.79, 74.71, 73.45, 72.37, 74.99, 45.08,\n       58.34, 81.71, 68.9 , 68.8 , 63.17, 78.97, 82.84, 67.16, 69.09,\n       74.31, 74.83, 80.25, 74.23, 72.3 , 82.26, 75.34, 59.16, 67.54,\n       72.62, 61.64, 57.85, 76.29, 71.53, 78.71, 81.39, 74.12, 74.01,\n       77.09, 83.54, 75.58, 72.85, 76.19, 82.  , 71.71, 82.04, 78.17,\n       65.76, 72.31, 64.09, 73.32, 63.51, 78.7 , 56.55, 72.81, 58.97,\n       77.34, 65.85, 75.35, 74.42, 71.17, 69.32, 68.62, 73.75, 56.48,\n       51.68])"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "regressor.score(x_test, y_test)"
   ],
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 37,
     "data": {
      "text/plain": "0.9624842170870651"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Checking the null values\n",
    "data.isnull().sum()"
   ],
   "execution_count": 38,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 38,
     "data": {
      "text/plain": "Country                              0\nYear                                 0\nStatus                               0\nLife Expectancy                     10\nAdult Mortality                     10\nInfant Deaths                        0\nAlcohol                            194\nPercentage Expenditure               0\nHepatitis B                        553\nMeasles                              0\nBMI                                 34\nUnder-Five Deaths                    0\nPolio                               19\nTotal Expenditure                  226\nDiphtheria                          19\nHIV/AIDS                             0\nGDP                                448\nPopulation                         652\nThinness 1-19 years                 34\nThinness 5-9 years                  34\nIncome Composition of Resources    167\nSchooling                          163\ndtype: int64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ],
   "execution_count": 39,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 39,
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f614568f438>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "scrolled": true
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "sns.heatmap(data.corr(),annot=True, linecolor=\"white\", linewidths=0.5)\n",
    "plt.title(\"Correlation\")"
   ],
   "execution_count": 40,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 40,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Correlation')"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1440x1080 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "plt.scatter(y_test, pred)\n",
    "plt.xlabel('Actual Value', fontsize = 20)\n",
    "plt.ylabel('Predicted Value', fontsize = 20)\n",
    "plt.show()"
   ],
   "execution_count": 41,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "sns.distplot((y_test-pred),bins=30)"
   ],
   "execution_count": 42,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 42,
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f612913ec88>"
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from sklearn import metrics"
   ],
   "execution_count": 43,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, pred))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, pred))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, pred)))"
   ],
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "stream",
     "text": "MAE: 1.156655290102389\nMSE: 3.2687901023890786\nRMSE: 1.8079795635982943\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "!pip install watson-machine-learning-client"
   ],
   "execution_count": 45,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Requirement already satisfied: watson-machine-learning-client in /opt/conda/envs/Python36/lib/python3.6/site-packages (1.0.376)\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (2.21.0)\nRequirement already satisfied: tabulate in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (0.8.2)\nRequirement already satisfied: tqdm in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (4.31.1)\nRequirement already satisfied: pandas in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (0.24.1)\nRequirement already satisfied: ibm-cos-sdk in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (2.4.3)\nRequirement already satisfied: urllib3 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (1.24.1)\nRequirement already satisfied: certifi in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (2020.4.5.1)\nRequirement already satisfied: lomond in /opt/conda/envs/Python36/lib/python3.6/site-packages (from watson-machine-learning-client) (0.3.3)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->watson-machine-learning-client) (2.8)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->watson-machine-learning-client) (3.0.4)\nRequirement already satisfied: numpy>=1.12.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from pandas->watson-machine-learning-client) (1.15.4)\nRequirement already satisfied: pytz>=2011k in /opt/conda/envs/Python36/lib/python3.6/site-packages (from pandas->watson-machine-learning-client) (2018.9)\nRequirement already satisfied: python-dateutil>=2.5.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from pandas->watson-machine-learning-client) (2.7.5)\nRequirement already satisfied: ibm-cos-sdk-core==2.*,>=2.0.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from ibm-cos-sdk->watson-machine-learning-client) (2.4.3)\nRequirement already satisfied: ibm-cos-sdk-s3transfer==2.*,>=2.0.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from ibm-cos-sdk->watson-machine-learning-client) (2.4.3)\nRequirement already satisfied: six>=1.10.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from lomond->watson-machine-learning-client) (1.12.0)\nRequirement already satisfied: jmespath<1.0.0,>=0.7.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from ibm-cos-sdk-core==2.*,>=2.0.0->ibm-cos-sdk->watson-machine-learning-client) (0.9.3)\nRequirement already satisfied: docutils>=0.10 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from ibm-cos-sdk-core==2.*,>=2.0.0->ibm-cos-sdk->watson-machine-learning-client) (0.14)\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from watson_machine_learning_client import WatsonMachineLearningAPIClient"
   ],
   "execution_count": 46,
   "outputs": [
    {
     "output_type": "stream",
     "text": "2020-06-19 18:25:13,030 - watson_machine_learning_client.metanames - WARNING - 'AUTHOR_EMAIL' meta prop is deprecated. It will be ignored.\n",
     "name": "stderr"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "wml_credentials = {\n",
    "    #fill your Service Credentials\n",
    "}\n"
   ],
   "execution_count": 47,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "client = WatsonMachineLearningAPIClient( wml_credentials )",
   "execution_count": 48,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "model_props = {client.repository.ModelMetaNames.AUTHOR_NAME: \"Narayana Raj G\", \n               client.repository.ModelMetaNames.AUTHOR_EMAIL: \"gonarayanraj49@gmail.com\", \n               client.repository.ModelMetaNames.NAME: \"Life Predicting Expectancy\"}",
   "execution_count": 49,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "model_artifact = client.repository.store_model(regressor, meta_props=model_props)",
   "execution_count": 50,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "published_model_uid = client.repository.get_model_uid(model_artifact)",
   "execution_count": 51,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "published_model_uid",
   "execution_count": 52,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 52,
     "data": {
      "text/plain": "'c6c9171f-5c33-402c-9ce8-98ced4363949'"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "deployment = client.deployments.create(published_model_uid, name=\"Life Predicting Expectancy\")",
   "execution_count": 53,
   "outputs": [
    {
     "output_type": "stream",
     "text": "\n\n#######################################################################################\n\nSynchronous deployment creation for uid: 'c6c9171f-5c33-402c-9ce8-98ced4363949' started\n\n#######################################################################################\n\n\nINITIALIZING\nDEPLOY_SUCCESS\n\n\n------------------------------------------------------------------------------------------------\nSuccessfully finished deployment creation, deployment_uid='70b36464-cb87-4a0b-9162-d1a0d6298fb7'\n------------------------------------------------------------------------------------------------\n\n\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "scoring_endpoint = client.deployments.get_scoring_url(deployment)",
   "execution_count": 54,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "scoring_endpoint",
   "execution_count": 55,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 55,
     "data": {
      "text/plain": "'https://eu-gb.ml.cloud.ibm.com/v3/wml_instances/0b356bff-08fd-4c13-9970-cb3b93470977/deployments/70b36464-cb87-4a0b-9162-d1a0d6298fb7/online'"
     },
     "metadata": {}
    }
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "version": "3.6.9",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}