Reg_Life_Expectancy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying Different Regression Models on the Life Expectancy dataset\n",
    "\n",
    "Dataset:https://www.kaggle.com/kumarajarshi/life-expectancy-who\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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",
    "from sklearn import preprocessing, linear_model, model_selection, metrics\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the Life Expectancy dataset, using pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12777511.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.407</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12633897.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.418</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.0</td>\n",
       "      <td>304</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6.53</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>125525.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.427</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>529</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12366165.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1483</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358879</td>\n",
       "      <td>12222251.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.434</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0     Afghanistan  2015  Developing              65.0            263.0   \n",
       "1     Afghanistan  2014  Developing              59.9            271.0   \n",
       "2     Afghanistan  2013  Developing              59.9            268.0   \n",
       "3     Afghanistan  2012  Developing              59.5            272.0   \n",
       "4     Afghanistan  2011  Developing              59.2            275.0   \n",
       "...           ...   ...         ...               ...              ...   \n",
       "2933     Zimbabwe  2004  Developing              44.3            723.0   \n",
       "2934     Zimbabwe  2003  Developing              44.5            715.0   \n",
       "2935     Zimbabwe  2002  Developing              44.8             73.0   \n",
       "2936     Zimbabwe  2001  Developing              45.3            686.0   \n",
       "2937     Zimbabwe  2000  Developing              46.0            665.0   \n",
       "\n",
       "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "0                62     0.01               71.279624         65.0      1154   \n",
       "1                64     0.01               73.523582         62.0       492   \n",
       "2                66     0.01               73.219243         64.0       430   \n",
       "3                69     0.01               78.184215         67.0      2787   \n",
       "4                71     0.01                7.097109         68.0      3013   \n",
       "...             ...      ...                     ...          ...       ...   \n",
       "2933             27     4.36                0.000000         68.0        31   \n",
       "2934             26     4.06                0.000000          7.0       998   \n",
       "2935             25     4.43                0.000000         73.0       304   \n",
       "2936             25     1.72                0.000000         76.0       529   \n",
       "2937             24     1.68                0.000000         79.0      1483   \n",
       "\n",
       "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
       "0     ...    6.0               8.16         65.0        0.1  584.259210   \n",
       "1     ...   58.0               8.18         62.0        0.1  612.696514   \n",
       "2     ...   62.0               8.13         64.0        0.1  631.744976   \n",
       "3     ...   67.0               8.52         67.0        0.1  669.959000   \n",
       "4     ...   68.0               7.87         68.0        0.1   63.537231   \n",
       "...   ...    ...                ...          ...        ...         ...   \n",
       "2933  ...   67.0               7.13         65.0       33.6  454.366654   \n",
       "2934  ...    7.0               6.52         68.0       36.7  453.351155   \n",
       "2935  ...   73.0               6.53         71.0       39.8   57.348340   \n",
       "2936  ...   76.0               6.16         75.0       42.1  548.587312   \n",
       "2937  ...   78.0               7.10         78.0       43.5  547.358879   \n",
       "\n",
       "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0     33736494.0                   17.2                 17.3   \n",
       "1       327582.0                   17.5                 17.5   \n",
       "2     31731688.0                   17.7                 17.7   \n",
       "3      3696958.0                   17.9                 18.0   \n",
       "4      2978599.0                   18.2                 18.2   \n",
       "...          ...                    ...                  ...   \n",
       "2933  12777511.0                    9.4                  9.4   \n",
       "2934  12633897.0                    9.8                  9.9   \n",
       "2935    125525.0                    1.2                  1.3   \n",
       "2936  12366165.0                    1.6                  1.7   \n",
       "2937  12222251.0                   11.0                 11.2   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "0                               0.479       10.1  \n",
       "1                               0.476       10.0  \n",
       "2                               0.470        9.9  \n",
       "3                               0.463        9.8  \n",
       "4                               0.454        9.5  \n",
       "...                               ...        ...  \n",
       "2933                            0.407        9.2  \n",
       "2934                            0.418        9.5  \n",
       "2935                            0.427       10.0  \n",
       "2936                            0.427        9.8  \n",
       "2937                            0.434        9.8  \n",
       "\n",
       "[2938 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Life_Expectancy_Data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the types of the features and the number of non-null examples.\n",
    "There are missing values on every feature except Country, Year, Status, infant deaths, percentage expenditure, Measles, under-five deaths and HIV/AIDS. Also, Country and Status features are objects. Before feeding the dataset to the model, they need to be dealt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   infant deaths                    2938 non-null   int64  \n",
      " 6   Alcohol                          2744 non-null   float64\n",
      " 7   percentage expenditure           2938 non-null   float64\n",
      " 8   Hepatitis B                      2385 non-null   float64\n",
      " 9   Measles                          2938 non-null   int64  \n",
      " 10   BMI                             2904 non-null   float64\n",
      " 11  under-five deaths                2938 non-null   int64  \n",
      " 12  Polio                            2919 non-null   float64\n",
      " 13  Total expenditure                2712 non-null   float64\n",
      " 14  Diphtheria                       2919 non-null   float64\n",
      " 15   HIV/AIDS                        2938 non-null   float64\n",
      " 16  GDP                              2490 non-null   float64\n",
      " 17  Population                       2286 non-null   float64\n",
      " 18   thinness  1-19 years            2904 non-null   float64\n",
      " 19   thinness 5-9 years              2904 non-null   float64\n",
      " 20  Income composition of resources  2771 non-null   float64\n",
      " 21  Schooling                        2775 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 505.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describing the dataset. The dataset consists of examples from 193 different countries from different years. It is shown that minimum value for year is 2000 and maximum is 2015; it consists of 15 year period. Also the status column is binary, can be converted to 1 and 0s. The population column has huge values in it, which is expected. The values can be showed as millions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
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       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
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       "      <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>count</th>\n",
       "      <td>2938</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2744.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2385.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2712.00000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>2.286000e+03</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2771.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Democratic People's Republic of Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Developing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.518720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.224932</td>\n",
       "      <td>164.796448</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>...</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.324084</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>4.839704</td>\n",
       "      <td>4.870317</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.613841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.523867</td>\n",
       "      <td>124.292079</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>4.052413</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>...</td>\n",
       "      <td>23.428046</td>\n",
       "      <td>2.49832</td>\n",
       "      <td>23.716912</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>14270.169342</td>\n",
       "      <td>6.101210e+07</td>\n",
       "      <td>4.420195</td>\n",
       "      <td>4.508882</td>\n",
       "      <td>0.210904</td>\n",
       "      <td>3.358920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.100000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.26000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>463.935626</td>\n",
       "      <td>1.957932e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.493000</td>\n",
       "      <td>10.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.75500</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.702500</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.49250</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5910.806335</td>\n",
       "      <td>7.420359e+06</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>17.60000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Country         Year      Status  \\\n",
       "count                                    2938  2938.000000        2938   \n",
       "unique                                    193          NaN           2   \n",
       "top     Democratic People's Republic of Korea          NaN  Developing   \n",
       "freq                                       16          NaN        2426   \n",
       "mean                                      NaN  2007.518720         NaN   \n",
       "std                                       NaN     4.613841         NaN   \n",
       "min                                       NaN  2000.000000         NaN   \n",
       "25%                                       NaN  2004.000000         NaN   \n",
       "50%                                       NaN  2008.000000         NaN   \n",
       "75%                                       NaN  2012.000000         NaN   \n",
       "max                                       NaN  2015.000000         NaN   \n",
       "\n",
       "        Life expectancy   Adult Mortality  infant deaths      Alcohol  \\\n",
       "count        2928.000000      2928.000000    2938.000000  2744.000000   \n",
       "unique               NaN              NaN            NaN          NaN   \n",
       "top                  NaN              NaN            NaN          NaN   \n",
       "freq                 NaN              NaN            NaN          NaN   \n",
       "mean           69.224932       164.796448      30.303948     4.602861   \n",
       "std             9.523867       124.292079     117.926501     4.052413   \n",
       "min            36.300000         1.000000       0.000000     0.010000   \n",
       "25%            63.100000        74.000000       0.000000     0.877500   \n",
       "50%            72.100000       144.000000       3.000000     3.755000   \n",
       "75%            75.700000       228.000000      22.000000     7.702500   \n",
       "max            89.000000       723.000000    1800.000000    17.870000   \n",
       "\n",
       "        percentage expenditure  Hepatitis B       Measles   ...        Polio  \\\n",
       "count              2938.000000  2385.000000    2938.000000  ...  2919.000000   \n",
       "unique                     NaN          NaN            NaN  ...          NaN   \n",
       "top                        NaN          NaN            NaN  ...          NaN   \n",
       "freq                       NaN          NaN            NaN  ...          NaN   \n",
       "mean                738.251295    80.940461    2419.592240  ...    82.550188   \n",
       "std                1987.914858    25.070016   11467.272489  ...    23.428046   \n",
       "min                   0.000000     1.000000       0.000000  ...     3.000000   \n",
       "25%                   4.685343    77.000000       0.000000  ...    78.000000   \n",
       "50%                  64.912906    92.000000      17.000000  ...    93.000000   \n",
       "75%                 441.534144    97.000000     360.250000  ...    97.000000   \n",
       "max               19479.911610    99.000000  212183.000000  ...    99.000000   \n",
       "\n",
       "        Total expenditure  Diphtheria      HIV/AIDS            GDP  \\\n",
       "count          2712.00000  2919.000000  2938.000000    2490.000000   \n",
       "unique                NaN          NaN          NaN            NaN   \n",
       "top                   NaN          NaN          NaN            NaN   \n",
       "freq                  NaN          NaN          NaN            NaN   \n",
       "mean              5.93819    82.324084     1.742103    7483.158469   \n",
       "std               2.49832    23.716912     5.077785   14270.169342   \n",
       "min               0.37000     2.000000     0.100000       1.681350   \n",
       "25%               4.26000    78.000000     0.100000     463.935626   \n",
       "50%               5.75500    93.000000     0.100000    1766.947595   \n",
       "75%               7.49250    97.000000     0.800000    5910.806335   \n",
       "max              17.60000    99.000000    50.600000  119172.741800   \n",
       "\n",
       "          Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "count   2.286000e+03            2904.000000          2904.000000   \n",
       "unique           NaN                    NaN                  NaN   \n",
       "top              NaN                    NaN                  NaN   \n",
       "freq             NaN                    NaN                  NaN   \n",
       "mean    1.275338e+07               4.839704             4.870317   \n",
       "std     6.101210e+07               4.420195             4.508882   \n",
       "min     3.400000e+01               0.100000             0.100000   \n",
       "25%     1.957932e+05               1.600000             1.500000   \n",
       "50%     1.386542e+06               3.300000             3.300000   \n",
       "75%     7.420359e+06               7.200000             7.200000   \n",
       "max     1.293859e+09              27.700000            28.600000   \n",
       "\n",
       "        Income composition of resources    Schooling  \n",
       "count                       2771.000000  2775.000000  \n",
       "unique                              NaN          NaN  \n",
       "top                                 NaN          NaN  \n",
       "freq                                NaN          NaN  \n",
       "mean                           0.627551    11.992793  \n",
       "std                            0.210904     3.358920  \n",
       "min                            0.000000     0.000000  \n",
       "25%                            0.493000    10.100000  \n",
       "50%                            0.677000    12.300000  \n",
       "75%                            0.779000    14.300000  \n",
       "max                            0.948000    20.700000  \n",
       "\n",
       "[11 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>33.736494</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>0.327582</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>31.731688</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>3.696958</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>2.978599</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12.777511</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.407</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12.633897</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.418</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.0</td>\n",
       "      <td>304</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6.53</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>0.125525</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.427</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>529</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12.366165</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1483</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358879</td>\n",
       "      <td>12.222251</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.434</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0     Afghanistan  2015  Developing              65.0            263.0   \n",
       "1     Afghanistan  2014  Developing              59.9            271.0   \n",
       "2     Afghanistan  2013  Developing              59.9            268.0   \n",
       "3     Afghanistan  2012  Developing              59.5            272.0   \n",
       "4     Afghanistan  2011  Developing              59.2            275.0   \n",
       "...           ...   ...         ...               ...              ...   \n",
       "2933     Zimbabwe  2004  Developing              44.3            723.0   \n",
       "2934     Zimbabwe  2003  Developing              44.5            715.0   \n",
       "2935     Zimbabwe  2002  Developing              44.8             73.0   \n",
       "2936     Zimbabwe  2001  Developing              45.3            686.0   \n",
       "2937     Zimbabwe  2000  Developing              46.0            665.0   \n",
       "\n",
       "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "0                62     0.01               71.279624         65.0      1154   \n",
       "1                64     0.01               73.523582         62.0       492   \n",
       "2                66     0.01               73.219243         64.0       430   \n",
       "3                69     0.01               78.184215         67.0      2787   \n",
       "4                71     0.01                7.097109         68.0      3013   \n",
       "...             ...      ...                     ...          ...       ...   \n",
       "2933             27     4.36                0.000000         68.0        31   \n",
       "2934             26     4.06                0.000000          7.0       998   \n",
       "2935             25     4.43                0.000000         73.0       304   \n",
       "2936             25     1.72                0.000000         76.0       529   \n",
       "2937             24     1.68                0.000000         79.0      1483   \n",
       "\n",
       "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
       "0     ...    6.0               8.16         65.0        0.1  584.259210   \n",
       "1     ...   58.0               8.18         62.0        0.1  612.696514   \n",
       "2     ...   62.0               8.13         64.0        0.1  631.744976   \n",
       "3     ...   67.0               8.52         67.0        0.1  669.959000   \n",
       "4     ...   68.0               7.87         68.0        0.1   63.537231   \n",
       "...   ...    ...                ...          ...        ...         ...   \n",
       "2933  ...   67.0               7.13         65.0       33.6  454.366654   \n",
       "2934  ...    7.0               6.52         68.0       36.7  453.351155   \n",
       "2935  ...   73.0               6.53         71.0       39.8   57.348340   \n",
       "2936  ...   76.0               6.16         75.0       42.1  548.587312   \n",
       "2937  ...   78.0               7.10         78.0       43.5  547.358879   \n",
       "\n",
       "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0      33.736494                   17.2                 17.3   \n",
       "1       0.327582                   17.5                 17.5   \n",
       "2      31.731688                   17.7                 17.7   \n",
       "3       3.696958                   17.9                 18.0   \n",
       "4       2.978599                   18.2                 18.2   \n",
       "...          ...                    ...                  ...   \n",
       "2933   12.777511                    9.4                  9.4   \n",
       "2934   12.633897                    9.8                  9.9   \n",
       "2935    0.125525                    1.2                  1.3   \n",
       "2936   12.366165                    1.6                  1.7   \n",
       "2937   12.222251                   11.0                 11.2   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "0                               0.479       10.1  \n",
       "1                               0.476       10.0  \n",
       "2                               0.470        9.9  \n",
       "3                               0.463        9.8  \n",
       "4                               0.454        9.5  \n",
       "...                               ...        ...  \n",
       "2933                            0.407        9.2  \n",
       "2934                            0.418        9.5  \n",
       "2935                            0.427       10.0  \n",
       "2936                            0.427        9.8  \n",
       "2937                            0.434        9.8  \n",
       "\n",
       "[2938 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Population'] = df['Population']/1000000\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the examples with missing values. Now, the dataset has no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1649 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          1649 non-null   object \n",
      " 1   Year                             1649 non-null   int64  \n",
      " 2   Status                           1649 non-null   object \n",
      " 3   Life expectancy                  1649 non-null   float64\n",
      " 4   Adult Mortality                  1649 non-null   float64\n",
      " 5   infant deaths                    1649 non-null   int64  \n",
      " 6   Alcohol                          1649 non-null   float64\n",
      " 7   percentage expenditure           1649 non-null   float64\n",
      " 8   Hepatitis B                      1649 non-null   float64\n",
      " 9   Measles                          1649 non-null   int64  \n",
      " 10   BMI                             1649 non-null   float64\n",
      " 11  under-five deaths                1649 non-null   int64  \n",
      " 12  Polio                            1649 non-null   float64\n",
      " 13  Total expenditure                1649 non-null   float64\n",
      " 14  Diphtheria                       1649 non-null   float64\n",
      " 15   HIV/AIDS                        1649 non-null   float64\n",
      " 16  GDP                              1649 non-null   float64\n",
      " 17  Population                       1649 non-null   float64\n",
      " 18   thinness  1-19 years            1649 non-null   float64\n",
      " 19   thinness 5-9 years              1649 non-null   float64\n",
      " 20  Income composition of resources  1649 non-null   float64\n",
      " 21  Schooling                        1649 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 296.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(axis=0)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at Turkeys numbers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>2682</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>75.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.45</td>\n",
       "      <td>181.908378</td>\n",
       "      <td>96.0</td>\n",
       "      <td>565</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.41</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12127.225220</td>\n",
       "      <td>0.773628</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.759</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>75.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.41</td>\n",
       "      <td>188.144035</td>\n",
       "      <td>97.0</td>\n",
       "      <td>7405</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>5.38</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12542.935650</td>\n",
       "      <td>75.787333</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.754</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>74.8</td>\n",
       "      <td>112.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1.54</td>\n",
       "      <td>20.751202</td>\n",
       "      <td>97.0</td>\n",
       "      <td>349</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.24</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1172.384300</td>\n",
       "      <td>74.569867</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.750</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2685</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>74.5</td>\n",
       "      <td>114.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1275.877569</td>\n",
       "      <td>96.0</td>\n",
       "      <td>111</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.29</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>11341.133950</td>\n",
       "      <td>7.349455</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.737</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2686</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2010</td>\n",
       "      <td>Developing</td>\n",
       "      <td>74.2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.49</td>\n",
       "      <td>32.782358</td>\n",
       "      <td>96.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>5.61</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1672.569300</td>\n",
       "      <td>72.326914</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.715</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2687</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developing</td>\n",
       "      <td>73.9</td>\n",
       "      <td>118.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.40</td>\n",
       "      <td>106.948290</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.80</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>936.499911</td>\n",
       "      <td>71.339185</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.709</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2688</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>73.5</td>\n",
       "      <td>121.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.39</td>\n",
       "      <td>22.672718</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.70</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>185.689750</td>\n",
       "      <td>0.074432</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.705</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2689</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developing</td>\n",
       "      <td>73.2</td>\n",
       "      <td>124.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.26</td>\n",
       "      <td>115.580110</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.40</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>979.492460</td>\n",
       "      <td>69.597281</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.697</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2690</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2006</td>\n",
       "      <td>Developing</td>\n",
       "      <td>72.8</td>\n",
       "      <td>126.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>103.523776</td>\n",
       "      <td>82.0</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.81</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>834.869161</td>\n",
       "      <td>6.876345</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.687</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2691</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2005</td>\n",
       "      <td>Developing</td>\n",
       "      <td>72.4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.29</td>\n",
       "      <td>832.955206</td>\n",
       "      <td>85.0</td>\n",
       "      <td>6200</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.45</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>7384.354661</td>\n",
       "      <td>0.679346</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.681</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>72.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>33</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.132767</td>\n",
       "      <td>77.0</td>\n",
       "      <td>8927</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.37</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>64.729530</td>\n",
       "      <td>0.677855</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.675</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>71.6</td>\n",
       "      <td>135.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.42</td>\n",
       "      <td>459.111306</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5844</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5.34</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4718.512910</td>\n",
       "      <td>0.668583</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.668</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>71.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>38</td>\n",
       "      <td>1.47</td>\n",
       "      <td>35.557290</td>\n",
       "      <td>72.0</td>\n",
       "      <td>7823</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>5.36</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>366.570000</td>\n",
       "      <td>6.514354</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.658</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2695</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>78.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>41</td>\n",
       "      <td>1.49</td>\n",
       "      <td>256.434175</td>\n",
       "      <td>77.0</td>\n",
       "      <td>30509</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>5.16</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3119.637160</td>\n",
       "      <td>64.191474</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.653</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>74.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>44</td>\n",
       "      <td>1.54</td>\n",
       "      <td>421.295660</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16244</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.95</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4316.553895</td>\n",
       "      <td>6.324121</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.641</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "2682  Turkey  2014  Developing              75.5             17.0   \n",
       "2683  Turkey  2013  Developing              75.2             19.0   \n",
       "2684  Turkey  2012  Developing              74.8            112.0   \n",
       "2685  Turkey  2011  Developing              74.5            114.0   \n",
       "2686  Turkey  2010  Developing              74.2            116.0   \n",
       "2687  Turkey  2009  Developing              73.9            118.0   \n",
       "2688  Turkey  2008  Developing              73.5            121.0   \n",
       "2689  Turkey  2007  Developing              73.2            124.0   \n",
       "2690  Turkey  2006  Developing              72.8            126.0   \n",
       "2691  Turkey  2005  Developing              72.4            129.0   \n",
       "2692  Turkey  2004  Developing              72.0            132.0   \n",
       "2693  Turkey  2003  Developing              71.6            135.0   \n",
       "2694  Turkey  2002  Developing              71.2            138.0   \n",
       "2695  Turkey  2001  Developing              78.0             14.0   \n",
       "2696  Turkey  2000  Developing              74.0            143.0   \n",
       "\n",
       "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "2682             16     1.45              181.908378         96.0       565   \n",
       "2683             17     1.41              188.144035         97.0      7405   \n",
       "2684             19     1.54               20.751202         97.0       349   \n",
       "2685             20     1.53             1275.877569         96.0       111   \n",
       "2686             21     1.49               32.782358         96.0         7   \n",
       "2687             23     1.40              106.948290         94.0         4   \n",
       "2688             25     1.39               22.672718         92.0         0   \n",
       "2689             26     1.26              115.580110         96.0         3   \n",
       "2690             28     1.29              103.523776         82.0        34   \n",
       "2691             30     1.29              832.955206         85.0      6200   \n",
       "2692             33     1.35                1.132767         77.0      8927   \n",
       "2693             35     1.42              459.111306         68.0      5844   \n",
       "2694             38     1.47               35.557290         72.0      7823   \n",
       "2695             41     1.49              256.434175         77.0     30509   \n",
       "2696             44     1.54              421.295660         71.0     16244   \n",
       "\n",
       "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS           GDP  \\\n",
       "2682  ...   96.0               5.41         96.0        0.1  12127.225220   \n",
       "2683  ...   98.0               5.38         98.0        0.1  12542.935650   \n",
       "2684  ...   97.0               5.24         97.0        0.1   1172.384300   \n",
       "2685  ...   97.0               5.29         97.0        0.1  11341.133950   \n",
       "2686  ...   97.0               5.61         97.0        0.1   1672.569300   \n",
       "2687  ...   96.0               6.80         96.0        0.1    936.499911   \n",
       "2688  ...   96.0               6.70         96.0        0.1    185.689750   \n",
       "2689  ...   96.0               6.40         96.0        0.1    979.492460   \n",
       "2690  ...    9.0               5.81          9.0        0.1    834.869161   \n",
       "2691  ...    9.0               5.45          9.0        0.1   7384.354661   \n",
       "2692  ...   85.0               5.37         85.0        0.1     64.729530   \n",
       "2693  ...   69.0               5.34         68.0        0.1   4718.512910   \n",
       "2694  ...   78.0               5.36         78.0        0.1    366.570000   \n",
       "2695  ...   88.0               5.16         88.0        0.1   3119.637160   \n",
       "2696  ...   85.0               4.95         85.0        0.1   4316.553895   \n",
       "\n",
       "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "2682    0.773628                    4.9                  4.7   \n",
       "2683   75.787333                    4.9                  4.7   \n",
       "2684   74.569867                    4.9                  4.7   \n",
       "2685    7.349455                    4.9                  4.7   \n",
       "2686   72.326914                    4.9                  4.7   \n",
       "2687   71.339185                    4.9                  4.8   \n",
       "2688    0.074432                    5.0                  4.8   \n",
       "2689   69.597281                    5.0                  4.8   \n",
       "2690    6.876345                    5.0                  4.8   \n",
       "2691    0.679346                    5.0                  4.9   \n",
       "2692    0.677855                    5.1                  4.9   \n",
       "2693    0.668583                    5.1                  5.0   \n",
       "2694    6.514354                    5.2                  5.0   \n",
       "2695   64.191474                    5.2                  5.1   \n",
       "2696    6.324121                    5.3                  5.2   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "2682                            0.759       14.5  \n",
       "2683                            0.754       14.4  \n",
       "2684                            0.750       14.3  \n",
       "2685                            0.737       13.8  \n",
       "2686                            0.715       13.0  \n",
       "2687                            0.709       12.5  \n",
       "2688                            0.705       12.5  \n",
       "2689                            0.697       12.3  \n",
       "2690                            0.687       11.9  \n",
       "2691                            0.681       11.9  \n",
       "2692                            0.675       12.0  \n",
       "2693                            0.668       11.9  \n",
       "2694                            0.658       11.5  \n",
       "2695                            0.653       11.1  \n",
       "2696                            0.641       10.7  \n",
       "\n",
       "[15 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Turkey =df[df['Country'] == 'Turkey']\n",
    "Turkey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that Turkey's life expectancy was 74 at year 2000, peaked at 78 one year after and hit the bottom with 71 at 2002. Then it increases lineary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Turkey['Year'], Turkey['Life expectancy '], color='red')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Life Expectancy')\n",
    "plt.title(\"Turkey\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Developing    1407\n",
       "Developed      242\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This boxplot shows that developed countries are stable because the range of the live expectancy age of developed countries is short, also the developing countries may have very different life expenctacy rates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([df[df['Status']=='Developing']['Life expectancy '], df[df['Status']=='Developed']['Life expectancy ']], labels=['Developing','Developed'])\n",
    "plt.ylabel('Life expectancy')\n",
    "plt.xlabel('Countries')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe shows the countries with lowest and highest life expectancy. We can see African countries generally have the lowest life expectancy and the European ones have the highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>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>1583</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1.10</td>\n",
       "      <td>3.885395</td>\n",
       "      <td>64.0</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>79.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>64.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>29.979898</td>\n",
       "      <td>1.213711</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.388</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12.777511</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.407</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>2005</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.5</td>\n",
       "      <td>675.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.67</td>\n",
       "      <td>57.903698</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>89.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>862.946312</td>\n",
       "      <td>1.949543</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.437</td>\n",
       "      <td>10.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12.633897</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.418</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2005</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.6</td>\n",
       "      <td>717.0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.14</td>\n",
       "      <td>8.717409</td>\n",
       "      <td>65.0</td>\n",
       "      <td>420</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.44</td>\n",
       "      <td>68.0</td>\n",
       "      <td>30.3</td>\n",
       "      <td>444.765750</td>\n",
       "      <td>0.129432</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.406</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>France</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>89.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.90</td>\n",
       "      <td>7002.785925</td>\n",
       "      <td>47.0</td>\n",
       "      <td>604</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>45413.657100</td>\n",
       "      <td>6.437499</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.877</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.88</td>\n",
       "      <td>271.254553</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2277.536130</td>\n",
       "      <td>0.014162</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.837</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.03</td>\n",
       "      <td>941.756291</td>\n",
       "      <td>88.0</td>\n",
       "      <td>443</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>11.30</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4792.652880</td>\n",
       "      <td>0.089825</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.920</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>7163.348923</td>\n",
       "      <td>98.0</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>47439.396840</td>\n",
       "      <td>0.112957</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.05</td>\n",
       "      <td>510.932701</td>\n",
       "      <td>96.0</td>\n",
       "      <td>267</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>8.36</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3279.414000</td>\n",
       "      <td>4.522683</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.849</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1649 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "1583    Malawi  2002  Developing              44.0             67.0   \n",
       "2933  Zimbabwe  2004  Developing              44.3            723.0   \n",
       "1484   Lesotho  2005  Developing              44.5            675.0   \n",
       "2934  Zimbabwe  2003  Developing              44.5            715.0   \n",
       "2932  Zimbabwe  2005  Developing              44.6            717.0   \n",
       "...        ...   ...         ...               ...              ...   \n",
       "937     France  2008  Developing              89.0             88.0   \n",
       "2056  Portugal  2014   Developed              89.0             78.0   \n",
       "995    Germany  2014   Developed              89.0             69.0   \n",
       "241    Belgium  2014   Developed              89.0             76.0   \n",
       "2433     Spain  2007   Developed              89.0             72.0   \n",
       "\n",
       "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "1583             46     1.10                3.885395         64.0        92   \n",
       "2933             27     4.36                0.000000         68.0        31   \n",
       "1484              5     2.67               57.903698         87.0         0   \n",
       "2934             26     4.06                0.000000          7.0       998   \n",
       "2932             28     4.14                8.717409         65.0       420   \n",
       "...             ...      ...                     ...          ...       ...   \n",
       "937               3    11.90             7002.785925         47.0       604   \n",
       "2056              0     9.88              271.254553         98.0         0   \n",
       "995               2    11.03              941.756291         88.0       443   \n",
       "241               0    12.60             7163.348923         98.0        70   \n",
       "2433              2    11.05              510.932701         96.0       267   \n",
       "\n",
       "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS           GDP  \\\n",
       "1583  ...   79.0               4.82         64.0       24.7     29.979898   \n",
       "2933  ...   67.0               7.13         65.0       33.6    454.366654   \n",
       "1484  ...   88.0               6.30         89.0       34.8    862.946312   \n",
       "2934  ...    7.0               6.52         68.0       36.7    453.351155   \n",
       "2932  ...   69.0               6.44         68.0       30.3    444.765750   \n",
       "...   ...    ...                ...          ...        ...           ...   \n",
       "937   ...   98.0               1.57         98.0        0.1  45413.657100   \n",
       "2056  ...   98.0               9.50         98.0        0.1   2277.536130   \n",
       "995   ...   94.0              11.30         95.0        0.1   4792.652880   \n",
       "241   ...   99.0               1.59         99.0        0.1  47439.396840   \n",
       "2433  ...   96.0               8.36         96.0        0.1   3279.414000   \n",
       "\n",
       "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "1583    1.213711                    7.7                  7.6   \n",
       "2933   12.777511                    9.4                  9.4   \n",
       "1484    1.949543                    9.3                  9.2   \n",
       "2934   12.633897                    9.8                  9.9   \n",
       "2932    0.129432                    9.0                  9.0   \n",
       "...          ...                    ...                  ...   \n",
       "937     6.437499                    0.6                  0.6   \n",
       "2056    0.014162                    0.7                  0.5   \n",
       "995     0.089825                    1.1                  1.1   \n",
       "241     0.112957                    1.0                  1.0   \n",
       "2433    4.522683                    0.6                  0.5   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "1583                            0.388       10.4  \n",
       "2933                            0.407        9.2  \n",
       "1484                            0.437       10.7  \n",
       "2934                            0.418        9.5  \n",
       "2932                            0.406        9.3  \n",
       "...                               ...        ...  \n",
       "937                             0.877       16.1  \n",
       "2056                            0.837       16.8  \n",
       "995                             0.920       17.0  \n",
       "241                             0.890       16.3  \n",
       "2433                            0.849       16.0  \n",
       "\n",
       "[1649 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"Life expectancy \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that the countries with high  GDP (Gross domestic product) have high life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(df['GDP'], df['Life expectancy '])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the Status column to 1s and 0s to use it on the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1407\n",
       "0     242\n",
       "Name: Status, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_encoder= preprocessing.LabelEncoder()\n",
    "df.loc[:,'Status'] = l_encoder.fit_transform(df.loc[:,'Status'])\n",
    "df['Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      16\n",
       "1      16\n",
       "65     15\n",
       "48     15\n",
       "54     15\n",
       "       ..\n",
       "86      5\n",
       "88      4\n",
       "115     4\n",
       "53      2\n",
       "38      1\n",
       "Name: Country, Length: 133, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'Country'] = l_encoder.fit_transform(df.loc[:, 'Country'])\n",
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation map of the features. 1 means positively correlated, -1 means negatively correlated. Infant deaths and Under-Five deaths are positively correlated so we need to drop one of them. GDP and percentage expenditure are highly correlated too. Also thinness 1-19 years and thinness 5-9 are highly correlated. I would like to drop one of the features, if their correlation is bigger than 0.75 or less than -0.75. So I will drop Infant deaths (because under-five also includes it), percentage expenditure (I think GDP has more important informations) and thinness 5-9 years (because thinness 1-19 also includes it) as well as Income composition of resources which has 0.78 correlation with Schooling.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,20)) \n",
    "sns.heatmap(df.corr(), annot=True, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset after dropping the correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</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>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.01</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>33.736494</td>\n",
       "      <td>17.2</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.01</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>0.327582</td>\n",
       "      <td>17.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.01</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>31.731688</td>\n",
       "      <td>17.7</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.01</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>3.696958</td>\n",
       "      <td>17.9</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.01</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>2.978599</td>\n",
       "      <td>18.2</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>132</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31</td>\n",
       "      <td>27.1</td>\n",
       "      <td>42</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12.777511</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>132</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>7.0</td>\n",
       "      <td>998</td>\n",
       "      <td>26.7</td>\n",
       "      <td>41</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12.633897</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>132</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>73.0</td>\n",
       "      <td>304</td>\n",
       "      <td>26.3</td>\n",
       "      <td>40</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6.53</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>0.125525</td>\n",
       "      <td>1.2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>132</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>76.0</td>\n",
       "      <td>529</td>\n",
       "      <td>25.9</td>\n",
       "      <td>39</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12.366165</td>\n",
       "      <td>1.6</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>132</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1483</td>\n",
       "      <td>25.5</td>\n",
       "      <td>39</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358879</td>\n",
       "      <td>12.222251</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1649 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  Status  Life expectancy   Adult Mortality  Alcohol  \\\n",
       "0           0  2015       1              65.0            263.0     0.01   \n",
       "1           0  2014       1              59.9            271.0     0.01   \n",
       "2           0  2013       1              59.9            268.0     0.01   \n",
       "3           0  2012       1              59.5            272.0     0.01   \n",
       "4           0  2011       1              59.2            275.0     0.01   \n",
       "...       ...   ...     ...               ...              ...      ...   \n",
       "2933      132  2004       1              44.3            723.0     4.36   \n",
       "2934      132  2003       1              44.5            715.0     4.06   \n",
       "2935      132  2002       1              44.8             73.0     4.43   \n",
       "2936      132  2001       1              45.3            686.0     1.72   \n",
       "2937      132  2000       1              46.0            665.0     1.68   \n",
       "\n",
       "      Hepatitis B  Measles    BMI   under-five deaths   Polio  \\\n",
       "0            65.0      1154   19.1                  83    6.0   \n",
       "1            62.0       492   18.6                  86   58.0   \n",
       "2            64.0       430   18.1                  89   62.0   \n",
       "3            67.0      2787   17.6                  93   67.0   \n",
       "4            68.0      3013   17.2                  97   68.0   \n",
       "...           ...       ...    ...                 ...    ...   \n",
       "2933         68.0        31   27.1                  42   67.0   \n",
       "2934          7.0       998   26.7                  41    7.0   \n",
       "2935         73.0       304   26.3                  40   73.0   \n",
       "2936         76.0       529   25.9                  39   76.0   \n",
       "2937         79.0      1483   25.5                  39   78.0   \n",
       "\n",
       "      Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0                  8.16         65.0        0.1  584.259210   33.736494   \n",
       "1                  8.18         62.0        0.1  612.696514    0.327582   \n",
       "2                  8.13         64.0        0.1  631.744976   31.731688   \n",
       "3                  8.52         67.0        0.1  669.959000    3.696958   \n",
       "4                  7.87         68.0        0.1   63.537231    2.978599   \n",
       "...                 ...          ...        ...         ...         ...   \n",
       "2933               7.13         65.0       33.6  454.366654   12.777511   \n",
       "2934               6.52         68.0       36.7  453.351155   12.633897   \n",
       "2935               6.53         71.0       39.8   57.348340    0.125525   \n",
       "2936               6.16         75.0       42.1  548.587312   12.366165   \n",
       "2937               7.10         78.0       43.5  547.358879   12.222251   \n",
       "\n",
       "       thinness  1-19 years  Schooling  \n",
       "0                      17.2       10.1  \n",
       "1                      17.5       10.0  \n",
       "2                      17.7        9.9  \n",
       "3                      17.9        9.8  \n",
       "4                      18.2        9.5  \n",
       "...                     ...        ...  \n",
       "2933                    9.4        9.2  \n",
       "2934                    9.8        9.5  \n",
       "2935                    1.2       10.0  \n",
       "2936                    1.6        9.8  \n",
       "2937                   11.0        9.8  \n",
       "\n",
       "[1649 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =df.drop(labels=['Income composition of resources', ' thinness 5-9 years', \n",
    "                'infant deaths', 'percentage expenditure'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y label of the model will be Life expectancy column. So we seperate it from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</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>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.01</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>33.736494</td>\n",
       "      <td>17.2</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.01</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>0.327582</td>\n",
       "      <td>17.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.01</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>31.731688</td>\n",
       "      <td>17.7</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>272.0</td>\n",
       "      <td>0.01</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>3.696958</td>\n",
       "      <td>17.9</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>275.0</td>\n",
       "      <td>0.01</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>2.978599</td>\n",
       "      <td>18.2</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>132</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>723.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31</td>\n",
       "      <td>27.1</td>\n",
       "      <td>42</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12.777511</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>132</td>\n",
       "      <td>2003</td>\n",
       "      <td>1</td>\n",
       "      <td>715.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>7.0</td>\n",
       "      <td>998</td>\n",
       "      <td>26.7</td>\n",
       "      <td>41</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12.633897</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>132</td>\n",
       "      <td>2002</td>\n",
       "      <td>1</td>\n",
       "      <td>73.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>73.0</td>\n",
       "      <td>304</td>\n",
       "      <td>26.3</td>\n",
       "      <td>40</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6.53</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>0.125525</td>\n",
       "      <td>1.2</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>132</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>686.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>76.0</td>\n",
       "      <td>529</td>\n",
       "      <td>25.9</td>\n",
       "      <td>39</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12.366165</td>\n",
       "      <td>1.6</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>132</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1483</td>\n",
       "      <td>25.5</td>\n",
       "      <td>39</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358879</td>\n",
       "      <td>12.222251</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1649 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  Status  Adult Mortality  Alcohol  Hepatitis B  Measles   \\\n",
       "0           0  2015       1            263.0     0.01         65.0      1154   \n",
       "1           0  2014       1            271.0     0.01         62.0       492   \n",
       "2           0  2013       1            268.0     0.01         64.0       430   \n",
       "3           0  2012       1            272.0     0.01         67.0      2787   \n",
       "4           0  2011       1            275.0     0.01         68.0      3013   \n",
       "...       ...   ...     ...              ...      ...          ...       ...   \n",
       "2933      132  2004       1            723.0     4.36         68.0        31   \n",
       "2934      132  2003       1            715.0     4.06          7.0       998   \n",
       "2935      132  2002       1             73.0     4.43         73.0       304   \n",
       "2936      132  2001       1            686.0     1.72         76.0       529   \n",
       "2937      132  2000       1            665.0     1.68         79.0      1483   \n",
       "\n",
       "       BMI   under-five deaths   Polio  Total expenditure  Diphtheria   \\\n",
       "0      19.1                  83    6.0               8.16         65.0   \n",
       "1      18.6                  86   58.0               8.18         62.0   \n",
       "2      18.1                  89   62.0               8.13         64.0   \n",
       "3      17.6                  93   67.0               8.52         67.0   \n",
       "4      17.2                  97   68.0               7.87         68.0   \n",
       "...     ...                 ...    ...                ...          ...   \n",
       "2933   27.1                  42   67.0               7.13         65.0   \n",
       "2934   26.7                  41    7.0               6.52         68.0   \n",
       "2935   26.3                  40   73.0               6.53         71.0   \n",
       "2936   25.9                  39   76.0               6.16         75.0   \n",
       "2937   25.5                  39   78.0               7.10         78.0   \n",
       "\n",
       "       HIV/AIDS         GDP  Population   thinness  1-19 years  Schooling  \n",
       "0           0.1  584.259210   33.736494                   17.2       10.1  \n",
       "1           0.1  612.696514    0.327582                   17.5       10.0  \n",
       "2           0.1  631.744976   31.731688                   17.7        9.9  \n",
       "3           0.1  669.959000    3.696958                   17.9        9.8  \n",
       "4           0.1   63.537231    2.978599                   18.2        9.5  \n",
       "...         ...         ...         ...                    ...        ...  \n",
       "2933       33.6  454.366654   12.777511                    9.4        9.2  \n",
       "2934       36.7  453.351155   12.633897                    9.8        9.5  \n",
       "2935       39.8   57.348340    0.125525                    1.2       10.0  \n",
       "2936       42.1  548.587312   12.366165                    1.6        9.8  \n",
       "2937       43.5  547.358879   12.222251                   11.0        9.8  \n",
       "\n",
       "[1649 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Life expectancy ']\n",
    "df = df.drop(labels='Life expectancy ', axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([65. , 59.9, 59.9, ..., 44.8, 45.3, 46. ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.to_numpy(dtype='float64')\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividing the dataset into train, validation and test sets. \n",
    "%80 training set, %10 validation set, %10 test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape -->(1319, 17)\n",
      "X_valid shape -->(165, 17)\n",
      "X_test shape -->(165, 17)\n",
      "y_train shape -->(1319,)\n",
      "y_valid shape -->(165,)\n",
      "y_test shape -->(165,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = model_selection.train_test_split(df, y, test_size=0.2, random_state=42)\n",
    "x_valid, x_test, y_valid, y_test = model_selection.train_test_split(x_test, y_test, test_size=0.5, random_state=42)\n",
    "print(f'X_train shape -->{x_train.shape}')\n",
    "print(f'X_valid shape -->{x_valid.shape}')\n",
    "print(f'X_test shape -->{x_test.shape}')\n",
    "print(f'y_train shape -->{y_train.shape}')\n",
    "print(f'y_valid shape -->{y_valid.shape}')\n",
    "print(f'y_test shape -->{y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization the features using Sklearn. Fitted the train set (because we only want to use these values) and transformed all sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_valid = scaler.transform(x_valid)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last version of the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.51128185,  0.78367891,  0.41023228, ...,  0.17827985,\n",
       "         0.44055795, -0.42801883],\n",
       "       [-1.61551902, -1.43186106,  0.41023228, ..., -0.16605599,\n",
       "        -0.79715621,  1.49260545],\n",
       "       [ 0.74703494, -0.20100552,  0.41023228, ...,  0.22946212,\n",
       "        -0.79715621,  0.39002484],\n",
       "       ...,\n",
       "       [ 0.07935664,  0.29133669, -2.43764338, ..., -0.17616066,\n",
       "        -0.44972767,  1.59930679],\n",
       "       [ 1.31199349,  0.04516559,  0.41023228, ..., -0.11980142,\n",
       "         0.78798649,  0.28332349],\n",
       "       [ 0.6443152 ,  0.5375078 ,  0.41023228, ..., -0.19886689,\n",
       "        -0.58001337, -1.63730078]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 1: Linear Regression\n",
    "\n",
    "Using Linear Regression as the first model. Model is trained on the train set and made predictions for validation set. Mean Squared Error is used to compute the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.462229070381309"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(X=x_train, y=y_train)\n",
    "val_hat = model.predict(x_valid)\n",
    "metrics.mean_squared_error(y_valid, val_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 2: Poisson Regressor\n",
    "\n",
    "Using Poisson Regressor as the second model. Defined the hyper-parameter alpha as 0.1 and maximum iterations is 100. The Mean Squared Error is less than the Linear Regression, so this is our best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.39391590226522"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poisson = linear_model.PoissonRegressor(alpha=0.1, max_iter=500)\n",
    "poisson.fit(X=x_train, y=y_train)\n",
    "poi_hat = poisson.predict(x_valid)\n",
    "metrics.mean_squared_error(y_valid, poi_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 3: Gamma Regressor\n",
    "\n",
    "Using Gamma Regressor as the last model. Used the same parameters as the Poisson Regressor. The Mean Squared Error is higher, so we will use the Poisson Regressor on our test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.52565351688887"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma = linear_model.GammaRegressor(alpha=0.1, max_iter=500)\n",
    "gamma.fit(x_train, y_train)\n",
    "gamma_val = gamma.predict(x_valid)\n",
    "metrics.mean_squared_error(y_valid, gamma_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We test the test set with the Poisson Regressor. Used a different metric which is similar to accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8115671625253258"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi_test = poisson.predict(x_test)\n",
    "metrics.r2_score(y_test, poi_test)"
   ]
  }
 ],
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