ML_RegressionModels_LifeExpectancy_NaraG_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e62ac7ff",
   "metadata": {},
   "source": [
    "# Final_Regressor_31072023\n",
    "\n",
    "#### Content:\n",
    "#### 1. Preprocessing\n",
    "#### 2. Multilanear Regression Model \n",
    "#### 3. Decision Tree Regression\n",
    "#### 4. Random Forest Regression \n",
    "#### 5. Support Vector Machines\n",
    "#### 6. Catboost\n",
    "#### 7. XGB Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a84d992",
   "metadata": {},
   "source": [
    "# Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "52214b1d",
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "92de918a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'C:\\Users\\user\\Downloads\\Life Expectancy Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "ab40ce44",
   "metadata": {},
   "outputs": [
    {
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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>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Life expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>19.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "      <td>65.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "      <td>59.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "      <td>59.2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>2933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</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>27.1</td>\n",
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       "      <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",
       "      <td>44.3</td>\n",
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       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</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>26.7</td>\n",
       "      <td>...</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",
       "      <td>44.5</td>\n",
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       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</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>26.3</td>\n",
       "      <td>...</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",
       "      <td>44.8</td>\n",
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       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</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>25.9</td>\n",
       "      <td>...</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",
       "      <td>45.3</td>\n",
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       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</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>25.5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358878</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",
       "      <td>46.0</td>\n",
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       "          Country  Year      Status  Adult Mortality  infant deaths  Alcohol  \\\n",
       "0     Afghanistan  2015  Developing            263.0             62     0.01   \n",
       "1     Afghanistan  2014  Developing            271.0             64     0.01   \n",
       "2     Afghanistan  2013  Developing            268.0             66     0.01   \n",
       "3     Afghanistan  2012  Developing            272.0             69     0.01   \n",
       "4     Afghanistan  2011  Developing            275.0             71     0.01   \n",
       "...           ...   ...         ...              ...            ...      ...   \n",
       "2933     Zimbabwe  2004  Developing            723.0             27     4.36   \n",
       "2934     Zimbabwe  2003  Developing            715.0             26     4.06   \n",
       "2935     Zimbabwe  2002  Developing             73.0             25     4.43   \n",
       "2936     Zimbabwe  2001  Developing            686.0             25     1.72   \n",
       "2937     Zimbabwe  2000  Developing            665.0             24     1.68   \n",
       "\n",
       "      percentage expenditure  Hepatitis B  Measles    BMI   ...  \\\n",
       "0                  71.279624         65.0      1154   19.1  ...   \n",
       "1                  73.523582         62.0       492   18.6  ...   \n",
       "2                  73.219243         64.0       430   18.1  ...   \n",
       "3                  78.184215         67.0      2787   17.6  ...   \n",
       "4                   7.097109         68.0      3013   17.2  ...   \n",
       "...                      ...          ...       ...    ...  ...   \n",
       "2933                0.000000         68.0        31   27.1  ...   \n",
       "2934                0.000000          7.0       998   26.7  ...   \n",
       "2935                0.000000         73.0       304   26.3  ...   \n",
       "2936                0.000000         76.0       529   25.9  ...   \n",
       "2937                0.000000         79.0      1483   25.5  ...   \n",
       "\n",
       "      Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0                  8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1                  8.18         62.0        0.1  612.696514    327582.0   \n",
       "2                  8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3                  8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4                  7.87         68.0        0.1   63.537231   2978599.0   \n",
       "...                 ...          ...        ...         ...         ...   \n",
       "2933               7.13         65.0       33.6  454.366654  12777511.0   \n",
       "2934               6.52         68.0       36.7  453.351155  12633897.0   \n",
       "2935               6.53         71.0       39.8   57.348340    125525.0   \n",
       "2936               6.16         75.0       42.1  548.587312  12366165.0   \n",
       "2937               7.10         78.0       43.5  547.358878  12222251.0   \n",
       "\n",
       "       thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                      17.2                 17.3   \n",
       "1                      17.5                 17.5   \n",
       "2                      17.7                 17.7   \n",
       "3                      17.9                 18.0   \n",
       "4                      18.2                 18.2   \n",
       "...                     ...                  ...   \n",
       "2933                    9.4                  9.4   \n",
       "2934                    9.8                  9.9   \n",
       "2935                    1.2                  1.3   \n",
       "2936                    1.6                  1.7   \n",
       "2937                   11.0                 11.2   \n",
       "\n",
       "      Income composition of resources  Schooling  Life expectancy   \n",
       "0                               0.479       10.1              65.0  \n",
       "1                               0.476       10.0              59.9  \n",
       "2                               0.470        9.9              59.9  \n",
       "3                               0.463        9.8              59.5  \n",
       "4                               0.454        9.5              59.2  \n",
       "...                               ...        ...               ...  \n",
       "2933                            0.407        9.2              44.3  \n",
       "2934                            0.418        9.5              44.5  \n",
       "2935                            0.427       10.0              44.8  \n",
       "2936                            0.427        9.8              45.3  \n",
       "2937                            0.434        9.8              46.0  \n",
       "\n",
       "[2938 rows x 22 columns]"
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       "      <th>top</th>\n",
       "      <td>Afghanistan</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>164.796448</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.321247</td>\n",
       "      <td>...</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",
       "      <td>69.224932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.613841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124.292079</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>4.052413</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>20.044034</td>\n",
       "      <td>...</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",
       "      <td>9.523867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</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",
       "      <td>36.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>...</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",
       "      <td>63.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>...</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",
       "      <td>72.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.702500</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.200000</td>\n",
       "      <td>...</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",
       "      <td>75.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>...</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",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country         Year      Status  Adult Mortality  infant deaths  \\\n",
       "count          2938  2938.000000        2938      2928.000000    2938.000000   \n",
       "unique          193          NaN           2              NaN            NaN   \n",
       "top     Afghanistan          NaN  Developing              NaN            NaN   \n",
       "freq             16          NaN        2426              NaN            NaN   \n",
       "mean            NaN  2007.518720         NaN       164.796448      30.303948   \n",
       "std             NaN     4.613841         NaN       124.292079     117.926501   \n",
       "min             NaN  2000.000000         NaN         1.000000       0.000000   \n",
       "25%             NaN  2004.000000         NaN        74.000000       0.000000   \n",
       "50%             NaN  2008.000000         NaN       144.000000       3.000000   \n",
       "75%             NaN  2012.000000         NaN       228.000000      22.000000   \n",
       "max             NaN  2015.000000         NaN       723.000000    1800.000000   \n",
       "\n",
       "            Alcohol  percentage expenditure  Hepatitis B       Measles   \\\n",
       "count   2744.000000             2938.000000  2385.000000    2938.000000   \n",
       "unique          NaN                     NaN          NaN            NaN   \n",
       "top             NaN                     NaN          NaN            NaN   \n",
       "freq            NaN                     NaN          NaN            NaN   \n",
       "mean       4.602861              738.251295    80.940461    2419.592240   \n",
       "std        4.052413             1987.914858    25.070016   11467.272489   \n",
       "min        0.010000                0.000000     1.000000       0.000000   \n",
       "25%        0.877500                4.685343    77.000000       0.000000   \n",
       "50%        3.755000               64.912906    92.000000      17.000000   \n",
       "75%        7.702500              441.534144    97.000000     360.250000   \n",
       "max       17.870000            19479.911610    99.000000  212183.000000   \n",
       "\n",
       "               BMI   ...  Total expenditure  Diphtheria      HIV/AIDS  \\\n",
       "count   2904.000000  ...         2712.00000  2919.000000  2938.000000   \n",
       "unique          NaN  ...                NaN          NaN          NaN   \n",
       "top             NaN  ...                NaN          NaN          NaN   \n",
       "freq            NaN  ...                NaN          NaN          NaN   \n",
       "mean      38.321247  ...            5.93819    82.324084     1.742103   \n",
       "std       20.044034  ...            2.49832    23.716912     5.077785   \n",
       "min        1.000000  ...            0.37000     2.000000     0.100000   \n",
       "25%       19.300000  ...            4.26000    78.000000     0.100000   \n",
       "50%       43.500000  ...            5.75500    93.000000     0.100000   \n",
       "75%       56.200000  ...            7.49250    97.000000     0.800000   \n",
       "max       87.300000  ...           17.60000    99.000000    50.600000   \n",
       "\n",
       "                  GDP    Population   thinness  1-19 years  \\\n",
       "count     2490.000000  2.286000e+03            2904.000000   \n",
       "unique            NaN           NaN                    NaN   \n",
       "top               NaN           NaN                    NaN   \n",
       "freq              NaN           NaN                    NaN   \n",
       "mean      7483.158469  1.275338e+07               4.839704   \n",
       "std      14270.169342  6.101210e+07               4.420195   \n",
       "min          1.681350  3.400000e+01               0.100000   \n",
       "25%        463.935626  1.957932e+05               1.600000   \n",
       "50%       1766.947595  1.386542e+06               3.300000   \n",
       "75%       5910.806335  7.420359e+06               7.200000   \n",
       "max     119172.741800  1.293859e+09              27.700000   \n",
       "\n",
       "         thinness 5-9 years  Income composition of resources    Schooling  \\\n",
       "count           2904.000000                      2771.000000  2775.000000   \n",
       "unique                  NaN                              NaN          NaN   \n",
       "top                     NaN                              NaN          NaN   \n",
       "freq                    NaN                              NaN          NaN   \n",
       "mean               4.870317                         0.627551    11.992793   \n",
       "std                4.508882                         0.210904     3.358920   \n",
       "min                0.100000                         0.000000     0.000000   \n",
       "25%                1.500000                         0.493000    10.100000   \n",
       "50%                3.300000                         0.677000    12.300000   \n",
       "75%                7.200000                         0.779000    14.300000   \n",
       "max               28.600000                         0.948000    20.700000   \n",
       "\n",
       "        Life expectancy   \n",
       "count        2928.000000  \n",
       "unique               NaN  \n",
       "top                  NaN  \n",
       "freq                 NaN  \n",
       "mean           69.224932  \n",
       "std             9.523867  \n",
       "min            36.300000  \n",
       "25%            63.100000  \n",
       "50%            72.100000  \n",
       "75%            75.700000  \n",
       "max            89.000000  \n",
       "\n",
       "[11 rows x 22 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "041e7cfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Country has 193 unique values. Drop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "d9e5b517",
   "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>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>...</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Life expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>19.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <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>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>27.1</td>\n",
       "      <td>...</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",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</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>26.7</td>\n",
       "      <td>...</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",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</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>26.3</td>\n",
       "      <td>...</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",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</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>25.9</td>\n",
       "      <td>...</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",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</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>25.5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358878</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",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Adult Mortality  infant deaths  Alcohol  \\\n",
       "0     Afghanistan  2015  Developing            263.0             62     0.01   \n",
       "1     Afghanistan  2014  Developing            271.0             64     0.01   \n",
       "2     Afghanistan  2013  Developing            268.0             66     0.01   \n",
       "3     Afghanistan  2012  Developing            272.0             69     0.01   \n",
       "4     Afghanistan  2011  Developing            275.0             71     0.01   \n",
       "...           ...   ...         ...              ...            ...      ...   \n",
       "2933     Zimbabwe  2004  Developing            723.0             27     4.36   \n",
       "2934     Zimbabwe  2003  Developing            715.0             26     4.06   \n",
       "2935     Zimbabwe  2002  Developing             73.0             25     4.43   \n",
       "2936     Zimbabwe  2001  Developing            686.0             25     1.72   \n",
       "2937     Zimbabwe  2000  Developing            665.0             24     1.68   \n",
       "\n",
       "      percentage expenditure  Hepatitis B  Measles    BMI   ...  \\\n",
       "0                  71.279624         65.0      1154   19.1  ...   \n",
       "1                  73.523582         62.0       492   18.6  ...   \n",
       "2                  73.219243         64.0       430   18.1  ...   \n",
       "3                  78.184215         67.0      2787   17.6  ...   \n",
       "4                   7.097109         68.0      3013   17.2  ...   \n",
       "...                      ...          ...       ...    ...  ...   \n",
       "2933                0.000000         68.0        31   27.1  ...   \n",
       "2934                0.000000          7.0       998   26.7  ...   \n",
       "2935                0.000000         73.0       304   26.3  ...   \n",
       "2936                0.000000         76.0       529   25.9  ...   \n",
       "2937                0.000000         79.0      1483   25.5  ...   \n",
       "\n",
       "      Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0                  8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1                  8.18         62.0        0.1  612.696514    327582.0   \n",
       "2                  8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3                  8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4                  7.87         68.0        0.1   63.537231   2978599.0   \n",
       "...                 ...          ...        ...         ...         ...   \n",
       "2933               7.13         65.0       33.6  454.366654  12777511.0   \n",
       "2934               6.52         68.0       36.7  453.351155  12633897.0   \n",
       "2935               6.53         71.0       39.8   57.348340    125525.0   \n",
       "2936               6.16         75.0       42.1  548.587312  12366165.0   \n",
       "2937               7.10         78.0       43.5  547.358878  12222251.0   \n",
       "\n",
       "       thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                      17.2                 17.3   \n",
       "1                      17.5                 17.5   \n",
       "2                      17.7                 17.7   \n",
       "3                      17.9                 18.0   \n",
       "4                      18.2                 18.2   \n",
       "...                     ...                  ...   \n",
       "2933                    9.4                  9.4   \n",
       "2934                    9.8                  9.9   \n",
       "2935                    1.2                  1.3   \n",
       "2936                    1.6                  1.7   \n",
       "2937                   11.0                 11.2   \n",
       "\n",
       "      Income composition of resources  Schooling  Life expectancy   \n",
       "0                               0.479       10.1              65.0  \n",
       "1                               0.476       10.0              59.9  \n",
       "2                               0.470        9.9              59.9  \n",
       "3                               0.463        9.8              59.5  \n",
       "4                               0.454        9.5              59.2  \n",
       "...                               ...        ...               ...  \n",
       "2933                            0.407        9.2              44.3  \n",
       "2934                            0.418        9.5              44.5  \n",
       "2935                            0.427       10.0              44.8  \n",
       "2936                            0.427        9.8              45.3  \n",
       "2937                            0.434        9.8              46.0  \n",
       "\n",
       "[2938 rows x 22 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "8092009f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                             object\n",
       "Year                                 int64\n",
       "Status                              object\n",
       "Adult Mortality                    float64\n",
       "infant deaths                        int64\n",
       "Alcohol                            float64\n",
       "percentage expenditure             float64\n",
       "Hepatitis B                        float64\n",
       "Measles                              int64\n",
       " BMI                               float64\n",
       "under-five deaths                    int64\n",
       "Polio                              float64\n",
       "Total expenditure                  float64\n",
       "Diphtheria                         float64\n",
       " HIV/AIDS                          float64\n",
       "GDP                                float64\n",
       "Population                         float64\n",
       " thinness  1-19 years              float64\n",
       " thinness 5-9 years                float64\n",
       "Income composition of resources    float64\n",
       "Schooling                          float64\n",
       "Life expectancy                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "fc37df5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Adult Mortality                     10\n",
       "infant deaths                        0\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Hepatitis B                        553\n",
       "Measles                              0\n",
       " BMI                                34\n",
       "under-five deaths                    0\n",
       "Polio                               19\n",
       "Total expenditure                  226\n",
       "Diphtheria                          19\n",
       " HIV/AIDS                            0\n",
       "GDP                                448\n",
       "Population                         652\n",
       " thinness  1-19 years               34\n",
       " thinness 5-9 years                 34\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       "Life expectancy                     10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "eaea43f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in data[['Adult Mortality','Alcohol','Hepatitis B',' BMI ','Polio','Total expenditure','Diphtheria ','GDP','Population',' thinness  1-19 years',' thinness 5-9 years','Income composition of resources','Schooling','Life expectancy ']]:\n",
    "    if data[i].dtype == 'object':\n",
    "        mode = data[i].mode()[0]\n",
    "        data[i].fillna(mode, inplace=True)\n",
    "    else:\n",
    "        mean = data[i].mean()\n",
    "        data[i].fillna(mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "d0aebdec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                            0\n",
       "Year                               0\n",
       "Status                             0\n",
       "Adult Mortality                    0\n",
       "infant deaths                      0\n",
       "Alcohol                            0\n",
       "percentage expenditure             0\n",
       "Hepatitis B                        0\n",
       "Measles                            0\n",
       " BMI                               0\n",
       "under-five deaths                  0\n",
       "Polio                              0\n",
       "Total expenditure                  0\n",
       "Diphtheria                         0\n",
       " HIV/AIDS                          0\n",
       "GDP                                0\n",
       "Population                         0\n",
       " thinness  1-19 years              0\n",
       " thinness 5-9 years                0\n",
       "Income composition of resources    0\n",
       "Schooling                          0\n",
       "Life expectancy                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "02b180b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Adult Mortality', 'infant deaths',\n",
       "       'Alcohol', 'percentage expenditure', 'Hepatitis B', 'Measles ', ' BMI ',\n",
       "       'under-five deaths ', 'Polio', 'Total expenditure', 'Diphtheria ',\n",
       "       ' HIV/AIDS', 'GDP', 'Population', ' thinness  1-19 years',\n",
       "       ' thinness 5-9 years', 'Income composition of resources', 'Schooling',\n",
       "       'Life expectancy '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "495e7684",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_titles = data[['Country', 'Year', 'Status', 'Adult Mortality', 'infant deaths',\n",
    "                'Alcohol', 'percentage expenditure', 'Hepatitis B', 'Measles ', ' BMI ',\n",
    "                'under-five deaths ', 'Polio', 'Total expenditure', 'Diphtheria ',\n",
    "                ' HIV/AIDS', 'GDP', 'Population', ' thinness  1-19 years',\n",
    "                ' thinness 5-9 years', 'Income composition of resources', 'Schooling',\n",
    "                'Life expectancy ']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "ff54e8d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_titles = [title.replace(' ', '').capitalize() for title in column_titles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "c8909992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Country', 'Year', 'Status', 'Adultmortality', 'Infantdeaths', 'Alcohol', 'Percentageexpenditure', 'Hepatitisb', 'Measles', 'Bmi', 'Under-fivedeaths', 'Polio', 'Totalexpenditure', 'Diphtheria', 'Hiv/aids', 'Gdp', 'Population', 'Thinness1-19years', 'Thinness5-9years', 'Incomecompositionofresources', 'Schooling', 'Lifeexpectancy']\n"
     ]
    }
   ],
   "source": [
    "print(cleaned_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "a5514adb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Updated DataFrame:\n",
      "          Country  Year      Status  Adultmortality  Infantdeaths  Alcohol  \\\n",
      "0     Afghanistan  2015  Developing           263.0            62     0.01   \n",
      "1     Afghanistan  2014  Developing           271.0            64     0.01   \n",
      "2     Afghanistan  2013  Developing           268.0            66     0.01   \n",
      "3     Afghanistan  2012  Developing           272.0            69     0.01   \n",
      "4     Afghanistan  2011  Developing           275.0            71     0.01   \n",
      "...           ...   ...         ...             ...           ...      ...   \n",
      "2933     Zimbabwe  2004  Developing           723.0            27     4.36   \n",
      "2934     Zimbabwe  2003  Developing           715.0            26     4.06   \n",
      "2935     Zimbabwe  2002  Developing            73.0            25     4.43   \n",
      "2936     Zimbabwe  2001  Developing           686.0            25     1.72   \n",
      "2937     Zimbabwe  2000  Developing           665.0            24     1.68   \n",
      "\n",
      "      Percentageexpenditure  Hepatitisb  Measles   Bmi  ...  Totalexpenditure  \\\n",
      "0                 71.279624        65.0     1154  19.1  ...              8.16   \n",
      "1                 73.523582        62.0      492  18.6  ...              8.18   \n",
      "2                 73.219243        64.0      430  18.1  ...              8.13   \n",
      "3                 78.184215        67.0     2787  17.6  ...              8.52   \n",
      "4                  7.097109        68.0     3013  17.2  ...              7.87   \n",
      "...                     ...         ...      ...   ...  ...               ...   \n",
      "2933               0.000000        68.0       31  27.1  ...              7.13   \n",
      "2934               0.000000         7.0      998  26.7  ...              6.52   \n",
      "2935               0.000000        73.0      304  26.3  ...              6.53   \n",
      "2936               0.000000        76.0      529  25.9  ...              6.16   \n",
      "2937               0.000000        79.0     1483  25.5  ...              7.10   \n",
      "\n",
      "      Diphtheria  Hiv/aids         Gdp  Population  Thinness1-19years  \\\n",
      "0           65.0       0.1  584.259210  33736494.0               17.2   \n",
      "1           62.0       0.1  612.696514    327582.0               17.5   \n",
      "2           64.0       0.1  631.744976  31731688.0               17.7   \n",
      "3           67.0       0.1  669.959000   3696958.0               17.9   \n",
      "4           68.0       0.1   63.537231   2978599.0               18.2   \n",
      "...          ...       ...         ...         ...                ...   \n",
      "2933        65.0      33.6  454.366654  12777511.0                9.4   \n",
      "2934        68.0      36.7  453.351155  12633897.0                9.8   \n",
      "2935        71.0      39.8   57.348340    125525.0                1.2   \n",
      "2936        75.0      42.1  548.587312  12366165.0                1.6   \n",
      "2937        78.0      43.5  547.358878  12222251.0               11.0   \n",
      "\n",
      "      Thinness5-9years  Incomecompositionofresources  Schooling  \\\n",
      "0                 17.3                         0.479       10.1   \n",
      "1                 17.5                         0.476       10.0   \n",
      "2                 17.7                         0.470        9.9   \n",
      "3                 18.0                         0.463        9.8   \n",
      "4                 18.2                         0.454        9.5   \n",
      "...                ...                           ...        ...   \n",
      "2933               9.4                         0.407        9.2   \n",
      "2934               9.9                         0.418        9.5   \n",
      "2935               1.3                         0.427       10.0   \n",
      "2936               1.7                         0.427        9.8   \n",
      "2937              11.2                         0.434        9.8   \n",
      "\n",
      "      Lifeexpectancy  \n",
      "0               65.0  \n",
      "1               59.9  \n",
      "2               59.9  \n",
      "3               59.5  \n",
      "4               59.2  \n",
      "...              ...  \n",
      "2933            44.3  \n",
      "2934            44.5  \n",
      "2935            44.8  \n",
      "2936            45.3  \n",
      "2937            46.0  \n",
      "\n",
      "[2938 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "column_mapping = {\n",
    "    'Country': 'Country',\n",
    "    'Year': 'Year',\n",
    "    'Status': 'Status',\n",
    "    'Adult Mortality': 'Adultmortality',\n",
    "    'infant deaths': 'Infantdeaths',\n",
    "    'Alcohol': 'Alcohol',\n",
    "    'percentage expenditure': 'Percentageexpenditure',\n",
    "    'Hepatitis B': 'Hepatitisb',\n",
    "    'Measles ': 'Measles',\n",
    "    ' BMI ': 'Bmi',\n",
    "    'under-five deaths ': 'Under-fivedeaths',\n",
    "    'Polio': 'Polio',\n",
    "    'Total expenditure': 'Totalexpenditure',\n",
    "    'Diphtheria ': 'Diphtheria',\n",
    "    ' HIV/AIDS': 'Hiv/aids',\n",
    "    'GDP': 'Gdp',\n",
    "    'Population': 'Population',\n",
    "    ' thinness  1-19 years': 'Thinness1-19years',\n",
    "    ' thinness 5-9 years': 'Thinness5-9years',\n",
    "    'Income composition of resources': 'Incomecompositionofresources',\n",
    "    'Schooling': 'Schooling',\n",
    "    'Life expectancy ': 'Lifeexpectancy'\n",
    "}\n",
    "\n",
    "\n",
    "data = data.rename(columns=column_mapping)\n",
    "\n",
    "print(\"\\nUpdated DataFrame:\")\n",
    "print(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "9e865788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Adultmortality</th>\n",
       "      <th>Infantdeaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Percentageexpenditure</th>\n",
       "      <th>Hepatitisb</th>\n",
       "      <th>Measles</th>\n",
       "      <th>Bmi</th>\n",
       "      <th>...</th>\n",
       "      <th>Totalexpenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>Hiv/aids</th>\n",
       "      <th>Gdp</th>\n",
       "      <th>Population</th>\n",
       "      <th>Thinness1-19years</th>\n",
       "      <th>Thinness5-9years</th>\n",
       "      <th>Incomecompositionofresources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>Lifeexpectancy</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>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>19.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>...</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <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>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>27.1</td>\n",
       "      <td>...</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",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</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>26.7</td>\n",
       "      <td>...</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",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</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>26.3</td>\n",
       "      <td>...</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",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</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>25.9</td>\n",
       "      <td>...</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",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</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>25.5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358878</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",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Adultmortality  Infantdeaths  Alcohol  \\\n",
       "0     Afghanistan  2015  Developing           263.0            62     0.01   \n",
       "1     Afghanistan  2014  Developing           271.0            64     0.01   \n",
       "2     Afghanistan  2013  Developing           268.0            66     0.01   \n",
       "3     Afghanistan  2012  Developing           272.0            69     0.01   \n",
       "4     Afghanistan  2011  Developing           275.0            71     0.01   \n",
       "...           ...   ...         ...             ...           ...      ...   \n",
       "2933     Zimbabwe  2004  Developing           723.0            27     4.36   \n",
       "2934     Zimbabwe  2003  Developing           715.0            26     4.06   \n",
       "2935     Zimbabwe  2002  Developing            73.0            25     4.43   \n",
       "2936     Zimbabwe  2001  Developing           686.0            25     1.72   \n",
       "2937     Zimbabwe  2000  Developing           665.0            24     1.68   \n",
       "\n",
       "      Percentageexpenditure  Hepatitisb  Measles   Bmi  ...  Totalexpenditure  \\\n",
       "0                 71.279624        65.0     1154  19.1  ...              8.16   \n",
       "1                 73.523582        62.0      492  18.6  ...              8.18   \n",
       "2                 73.219243        64.0      430  18.1  ...              8.13   \n",
       "3                 78.184215        67.0     2787  17.6  ...              8.52   \n",
       "4                  7.097109        68.0     3013  17.2  ...              7.87   \n",
       "...                     ...         ...      ...   ...  ...               ...   \n",
       "2933               0.000000        68.0       31  27.1  ...              7.13   \n",
       "2934               0.000000         7.0      998  26.7  ...              6.52   \n",
       "2935               0.000000        73.0      304  26.3  ...              6.53   \n",
       "2936               0.000000        76.0      529  25.9  ...              6.16   \n",
       "2937               0.000000        79.0     1483  25.5  ...              7.10   \n",
       "\n",
       "      Diphtheria  Hiv/aids         Gdp  Population  Thinness1-19years  \\\n",
       "0           65.0       0.1  584.259210  33736494.0               17.2   \n",
       "1           62.0       0.1  612.696514    327582.0               17.5   \n",
       "2           64.0       0.1  631.744976  31731688.0               17.7   \n",
       "3           67.0       0.1  669.959000   3696958.0               17.9   \n",
       "4           68.0       0.1   63.537231   2978599.0               18.2   \n",
       "...          ...       ...         ...         ...                ...   \n",
       "2933        65.0      33.6  454.366654  12777511.0                9.4   \n",
       "2934        68.0      36.7  453.351155  12633897.0                9.8   \n",
       "2935        71.0      39.8   57.348340    125525.0                1.2   \n",
       "2936        75.0      42.1  548.587312  12366165.0                1.6   \n",
       "2937        78.0      43.5  547.358878  12222251.0               11.0   \n",
       "\n",
       "      Thinness5-9years  Incomecompositionofresources  Schooling  \\\n",
       "0                 17.3                         0.479       10.1   \n",
       "1                 17.5                         0.476       10.0   \n",
       "2                 17.7                         0.470        9.9   \n",
       "3                 18.0                         0.463        9.8   \n",
       "4                 18.2                         0.454        9.5   \n",
       "...                ...                           ...        ...   \n",
       "2933               9.4                         0.407        9.2   \n",
       "2934               9.9                         0.418        9.5   \n",
       "2935               1.3                         0.427       10.0   \n",
       "2936               1.7                         0.427        9.8   \n",
       "2937              11.2                         0.434        9.8   \n",
       "\n",
       "      Lifeexpectancy  \n",
       "0               65.0  \n",
       "1               59.9  \n",
       "2               59.9  \n",
       "3               59.5  \n",
       "4               59.2  \n",
       "...              ...  \n",
       "2933            44.3  \n",
       "2934            44.5  \n",
       "2935            44.8  \n",
       "2936            45.3  \n",
       "2937            46.0  \n",
       "\n",
       "[2938 rows x 22 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "b4b74248",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Country', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "98edb8f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23676\\1992969647.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  data.corr()['Lifeexpectancy']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Year                            0.169623\n",
       "Adultmortality                 -0.696359\n",
       "Infantdeaths                   -0.196535\n",
       "Alcohol                         0.391598\n",
       "Percentageexpenditure           0.381791\n",
       "Hepatitisb                      0.203771\n",
       "Measles                        -0.157574\n",
       "Bmi                             0.559255\n",
       "Under-fivedeaths               -0.222503\n",
       "Polio                           0.461574\n",
       "Totalexpenditure                0.207981\n",
       "Diphtheria                      0.475418\n",
       "Hiv/aids                       -0.556457\n",
       "Gdp                             0.430493\n",
       "Population                     -0.019638\n",
       "Thinness1-19years              -0.472162\n",
       "Thinness5-9years               -0.466629\n",
       "Incomecompositionofresources    0.692483\n",
       "Schooling                       0.715066\n",
       "Lifeexpectancy                  1.000000\n",
       "Name: Lifeexpectancy, dtype: float64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()['Lifeexpectancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "fa469b45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                              int64\n",
       "Status                           object\n",
       "Adultmortality                  float64\n",
       "Infantdeaths                      int64\n",
       "Alcohol                         float64\n",
       "Percentageexpenditure           float64\n",
       "Hepatitisb                      float64\n",
       "Measles                           int64\n",
       "Bmi                             float64\n",
       "Under-fivedeaths                  int64\n",
       "Polio                           float64\n",
       "Totalexpenditure                float64\n",
       "Diphtheria                      float64\n",
       "Hiv/aids                        float64\n",
       "Gdp                             float64\n",
       "Population                      float64\n",
       "Thinness1-19years               float64\n",
       "Thinness5-9years                float64\n",
       "Incomecompositionofresources    float64\n",
       "Schooling                       float64\n",
       "Lifeexpectancy                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "837cdf23",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Population', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "5db61982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       Year      Status  Adultmortality  Infantdeaths  Alcohol  \\\n",
       "0     2015  Developing           263.0            62     0.01   \n",
       "1     2014  Developing           271.0            64     0.01   \n",
       "2     2013  Developing           268.0            66     0.01   \n",
       "3     2012  Developing           272.0            69     0.01   \n",
       "4     2011  Developing           275.0            71     0.01   \n",
       "...    ...         ...             ...           ...      ...   \n",
       "2933  2004  Developing           723.0            27     4.36   \n",
       "2934  2003  Developing           715.0            26     4.06   \n",
       "2935  2002  Developing            73.0            25     4.43   \n",
       "2936  2001  Developing           686.0            25     1.72   \n",
       "2937  2000  Developing           665.0            24     1.68   \n",
       "\n",
       "      Percentageexpenditure  Hepatitisb  Measles   Bmi  Under-fivedeaths  \\\n",
       "0                 71.279624        65.0     1154  19.1                83   \n",
       "1                 73.523582        62.0      492  18.6                86   \n",
       "2                 73.219243        64.0      430  18.1                89   \n",
       "3                 78.184215        67.0     2787  17.6                93   \n",
       "4                  7.097109        68.0     3013  17.2                97   \n",
       "...                     ...         ...      ...   ...               ...   \n",
       "2933               0.000000        68.0       31  27.1                42   \n",
       "2934               0.000000         7.0      998  26.7                41   \n",
       "2935               0.000000        73.0      304  26.3                40   \n",
       "2936               0.000000        76.0      529  25.9                39   \n",
       "2937               0.000000        79.0     1483  25.5                39   \n",
       "\n",
       "      Polio  Totalexpenditure  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  547.358878   \n",
       "\n",
       "      Thinness1-19years  Thinness5-9years  Incomecompositionofresources  \\\n",
       "0                  17.2              17.3                         0.479   \n",
       "1                  17.5              17.5                         0.476   \n",
       "2                  17.7              17.7                         0.470   \n",
       "3                  17.9              18.0                         0.463   \n",
       "4                  18.2              18.2                         0.454   \n",
       "...                 ...               ...                           ...   \n",
       "2933                9.4               9.4                         0.407   \n",
       "2934                9.8               9.9                         0.418   \n",
       "2935                1.2               1.3                         0.427   \n",
       "2936                1.6               1.7                         0.427   \n",
       "2937               11.0              11.2                         0.434   \n",
       "\n",
       "      Schooling  Lifeexpectancy  \n",
       "0          10.1            65.0  \n",
       "1          10.0            59.9  \n",
       "2           9.9            59.9  \n",
       "3           9.8            59.5  \n",
       "4           9.5            59.2  \n",
       "...         ...             ...  \n",
       "2933        9.2            44.3  \n",
       "2934        9.5            44.5  \n",
       "2935       10.0            44.8  \n",
       "2936        9.8            45.3  \n",
       "2937        9.8            46.0  \n",
       "\n",
       "[2938 rows x 20 columns]>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "095c85a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Status', 'Adultmortality', 'Infantdeaths', 'Alcohol',\n",
       "       'Percentageexpenditure', 'Hepatitisb', 'Measles', 'Bmi',\n",
       "       'Under-fivedeaths', 'Polio', 'Totalexpenditure', 'Diphtheria',\n",
       "       'Hiv/aids', 'Gdp', 'Thinness1-19years', 'Thinness5-9years',\n",
       "       'Incomecompositionofresources', 'Schooling', 'Lifeexpectancy'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "0502e67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                              int64\n",
       "Status                           object\n",
       "Adultmortality                  float64\n",
       "Infantdeaths                      int64\n",
       "Alcohol                         float64\n",
       "Percentageexpenditure           float64\n",
       "Hepatitisb                      float64\n",
       "Measles                           int64\n",
       "Bmi                             float64\n",
       "Under-fivedeaths                  int64\n",
       "Polio                           float64\n",
       "Totalexpenditure                float64\n",
       "Diphtheria                      float64\n",
       "Hiv/aids                        float64\n",
       "Gdp                             float64\n",
       "Thinness1-19years               float64\n",
       "Thinness5-9years                float64\n",
       "Incomecompositionofresources    float64\n",
       "Schooling                       float64\n",
       "Lifeexpectancy                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "bc0d6337",
   "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>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.572980</td>\n",
       "      <td>Adultmortality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.734244</td>\n",
       "      <td>Infantdeaths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.143452</td>\n",
       "      <td>Alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.547664</td>\n",
       "      <td>Percentageexpenditure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.394803</td>\n",
       "      <td>Measles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.824246</td>\n",
       "      <td>Bmi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.229985</td>\n",
       "      <td>Totalexpenditure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.546564</td>\n",
       "      <td>Hiv/aids</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.460500</td>\n",
       "      <td>Gdp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.896922</td>\n",
       "      <td>Thinness1-19years</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        VIF               Features\n",
       "0  3.572980         Adultmortality\n",
       "1  1.734244           Infantdeaths\n",
       "2  3.143452                Alcohol\n",
       "3  5.547664  Percentageexpenditure\n",
       "4  1.394803                Measles\n",
       "5  4.824246                    Bmi\n",
       "6  6.229985       Totalexpenditure\n",
       "7  1.546564               Hiv/aids\n",
       "8  6.460500                    Gdp\n",
       "9  2.896922      Thinness1-19years"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "variables = data[[#'Year',\n",
    "                  'Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure', \n",
    "                  #'Hepatitisb', \n",
    "                  'Measles',\n",
    "                  'Bmi',\n",
    "                  #'Under-fivedeaths', \n",
    "                  #'Polio', \n",
    "                  'Totalexpenditure', \n",
    "                  #'Diphtheria',\n",
    "                  'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years',\n",
    "                  #'Thinness5-9years',\n",
    "                  #'Incomecompositionofresources',\n",
    "                  #'Schooling'\n",
    "]]\n",
    "vif = pd.DataFrame()\n",
    "vif['VIF'] = [variance_inflation_factor(variables.values, i ) for i in range(variables.shape[1])]\n",
    "vif['Features'] = variables.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "b71c782d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "            'Measles',\n",
    "                  'Bmi',\n",
    "            'Totalexpenditure',\n",
    "            'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years','Lifeexpectancy']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "3a503125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Adultmortality</th>\n",
       "      <th>Infantdeaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Percentageexpenditure</th>\n",
       "      <th>Measles</th>\n",
       "      <th>Bmi</th>\n",
       "      <th>Totalexpenditure</th>\n",
       "      <th>Hiv/aids</th>\n",
       "      <th>Gdp</th>\n",
       "      <th>Thinness1-19years</th>\n",
       "      <th>Lifeexpectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>1154</td>\n",
       "      <td>19.1</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>17.2</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>17.5</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>8.13</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>17.7</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>8.52</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>17.9</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>18.2</td>\n",
       "      <td>59.2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31</td>\n",
       "      <td>27.1</td>\n",
       "      <td>7.13</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>9.4</td>\n",
       "      <td>44.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>998</td>\n",
       "      <td>26.7</td>\n",
       "      <td>6.52</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>9.8</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>304</td>\n",
       "      <td>26.3</td>\n",
       "      <td>6.53</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>1.2</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>529</td>\n",
       "      <td>25.9</td>\n",
       "      <td>6.16</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>1.6</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1483</td>\n",
       "      <td>25.5</td>\n",
       "      <td>7.10</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358878</td>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adultmortality  Infantdeaths  Alcohol  Percentageexpenditure  Measles  \\\n",
       "0              263.0            62     0.01              71.279624     1154   \n",
       "1              271.0            64     0.01              73.523582      492   \n",
       "2              268.0            66     0.01              73.219243      430   \n",
       "3              272.0            69     0.01              78.184215     2787   \n",
       "4              275.0            71     0.01               7.097109     3013   \n",
       "...              ...           ...      ...                    ...      ...   \n",
       "2933           723.0            27     4.36               0.000000       31   \n",
       "2934           715.0            26     4.06               0.000000      998   \n",
       "2935            73.0            25     4.43               0.000000      304   \n",
       "2936           686.0            25     1.72               0.000000      529   \n",
       "2937           665.0            24     1.68               0.000000     1483   \n",
       "\n",
       "       Bmi  Totalexpenditure  Hiv/aids         Gdp  Thinness1-19years  \\\n",
       "0     19.1              8.16       0.1  584.259210               17.2   \n",
       "1     18.6              8.18       0.1  612.696514               17.5   \n",
       "2     18.1              8.13       0.1  631.744976               17.7   \n",
       "3     17.6              8.52       0.1  669.959000               17.9   \n",
       "4     17.2              7.87       0.1   63.537231               18.2   \n",
       "...    ...               ...       ...         ...                ...   \n",
       "2933  27.1              7.13      33.6  454.366654                9.4   \n",
       "2934  26.7              6.52      36.7  453.351155                9.8   \n",
       "2935  26.3              6.53      39.8   57.348340                1.2   \n",
       "2936  25.9              6.16      42.1  548.587312                1.6   \n",
       "2937  25.5              7.10      43.5  547.358878               11.0   \n",
       "\n",
       "      Lifeexpectancy  \n",
       "0               65.0  \n",
       "1               59.9  \n",
       "2               59.9  \n",
       "3               59.5  \n",
       "4               59.2  \n",
       "...              ...  \n",
       "2933            44.3  \n",
       "2934            44.5  \n",
       "2935            44.8  \n",
       "2936            45.3  \n",
       "2937            46.0  \n",
       "\n",
       "[2938 rows x 11 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "8dd543a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HYmBgINrb20dsnzVrVvT09JjPcYx6DEyfPj1OOeWU+POf/1zb1tvbG9u2batVG2NrzZo1sXTp0vjkJz8ZK1euHLHLbM6cObF169YYGhqqbdu0aVOcddZZ8aY3vakey03lu9/9bvziF7+I9evX1z4iIr74xS/G6tWrzacBzJgxIyZOnBh//etfR2zfsWNHTJ061Yzq7IVvQP/2t7+N2L5jx45429veZj7HMxa/srBixYoyd+7csnHjxtr7DHzgAx/wPgN18MQTT5SZM2eWz3/+82Xv3r0jPnp7e8szzzxT5syZUzo7O8tjjz1W1q1bV9rb20t3d3e9l57Wi3+10Hwaw2233VY6OjrKhg0bypNPPll+8IMflOnTp5c//elPZlRnQ0ND5eqrry4f+tCHyqZNm8quXbtKV1dXaWtrKw8//LD5HEellFJGOziGhoZixYoV0d3dHYcPH445c+bE9ddfH295y1tG+645yqpVq6Krq+uYl1155ZVx0003xSOPPBLLli2Lbdu2xWmnnRaLFi2KBQsWjPFKecHb3/72WL58ecyfPz8iwnwaxN133x333ntv/POf/4xp06bFNddcE+9///sjwozqbf/+/bFy5cp44IEHYv/+/dHa2hrXXXddzJ07NyLM51jGJAYAgMblDxUBQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyY36HyoCxtbChQvjwQcfHLHtDW94Q8yYMSO+8IUv1N545URuPyLixz/+8QndDtA4vOkQnGQWLlwYBw4ciBtuuCEijrwD6L/+9a/4yU9+Eg8++GB0d3fHOeec86pvf+fOnRFx5A/zACcHewbgJHTKKafE7NmzR2x797vfHfPmzYvu7u7o7Ox81bctAuDkIwYgiQkTJkS1Wo1KpRIRR/YgnHXWWXHmmWfGmjVrore3N+bOnRvLly+P3/3ud7Fq1ap45plnYtasWfHtb3+79rdEHCaAk48YgJNQKSUGBwdrp/ft2xc/+tGPor+/Pz72sY/Vrvfzn/88ZsyYEcuWLYunn346li5dGgsWLIiWlpbo7OyMffv2xbJly+Jb3/pWrF69ul6fDjDKxACchDZv3hwzZ858yfbrrrsupk2bVjs/MDAQt956a7zxjW+MiIhf/epX8cc//jE2btwYb33rWyMiYvv27fHTn/50bBYO1IUYgJPQzJkz48Ybb4yII3sGent74/e//310dXXFoUOH4tprr42IiGnTptVCICLitNNOiylTptRCICJi8uTJ8fzzz4/tJwCMKTEAJ6GJEydGe3v7iG0XXnhhHDp0KH74wx/Gpz71qYg48oOGR5swYcKYrBFoHN50CBJpa2uLwcHB2L17d72XAjQQMQCJPPzwwzFu3LgRhwEAHCaAk9CBAwfiL3/5S+38wMBA/PrXv44NGzbEVVddFVOmTKnf4oCGIwbgJLRt27a46qqrauer1WpMnTo1rr322li8eHEdVwY0Im9HDADJ+ZkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJDc/wIrcD/0fFm3hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set()\n",
    "\n",
    "for i in data[['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years','Lifeexpectancy']]:\n",
    "    sns.boxplot(data=data,x=data[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "171f6aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "q1=data.quantile(0.25)\n",
    "q3=data.quantile(0.75)\n",
    "IQR=q3-q1\n",
    "\n",
    "Lower = q1-1.5*IQR\n",
    "Upper = q3+1.5*IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "fa921ab1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23676\\2509646702.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[i] = np.where(data[i] > Upper[i],Upper[i], data[i])\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_23676\\2509646702.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[i] = np.where(data[i] < Lower[i], Lower[i], data[i])\n"
     ]
    }
   ],
   "source": [
    "for i in data [['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years','Lifeexpectancy']]:\n",
    "    data[i] = np.where(data[i] > Upper[i],Upper[i], data[i])\n",
    "    data[i] = np.where(data[i] < Lower[i], Lower[i], data[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "7740de71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAG1CAYAAABkoPeiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbeklEQVR4nO3df3TV9X348VcgEAbVYanFbh4rk29AIFiLWJVaQCt1/qDKYB0s1Pmjc8PTzl8dUDq1thWPVtECRelEbfGszB/F2fXHznDd7EidCqJUqKLSTn6IiiAqJpC8v38478xgFVfMjb4ej3M4J/ncT+59575yc5/5fG5ITSmlBACQVpdqLwAAqC4xAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgudo93bGUEm1t/rPCt6NLlxr3WSdkLp2PmXRO5tI5vZ25dOlSEzU1NW+53x7HQFtbic2bX9nT3dOrre0S++3XK1566dXYubOt2svhv5hL52MmnZO5dE5vdy7vf3+v6Nr1rWPAaQIASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBIrrbaC4AsSinR0tJc7WV0Oq2tXeK117pGc/NrsXNn2165zlJKRETU1NTslevbW7p3r+t0a4IIMQAdpqWlOf7yL8+q9jKoonnzFkRdXY9qLwN24TQBACTnyABUQa//d1rUdPHweyeUtp3xyhOLI6Jz3M9vXg90Vr4bQRXUdKmt+pNUBu5n2DNOEwBAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkqh4DpZQopVR7GQBQFZ3hebCqMVBKiZkzvxIzZ36l6ncEAHS0zvI8WFu1W46IlpbmWLPm8crbdXU9qrkcAOhQneV5sOqnCQCA6hIDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASK622gt4Q3Nzc7WXsFe1tnaJ117rGs3Nr8XOnW3VXg7/pZpzea99jfP2vZu+BnwP6xid5WuiqjFQSqm8ff75f1nFlUDHKqVETbUXQYfwfY499eavlY7mNAEAJFfVIwM1Nf/9s9F1182Lurq6Kq5m76qt7RK9e/eKLVtecYitE6nmXJqbmys/Gb75a5/3tnfr9znfwzpGZ/m+0GleM1BXVxd1dT2qvYy9pra2S/To0SPq6lqja1cPpM7CXKimd9P3OY+VXJwmAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSq63mjXfvXhf9+9dX3gaATDrL82BVY6CmpiamT7+08jYAZNJZngerGgMRIgCA3DrD86DXDABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJFdb7QVARqVtZ7WX8J715vu2M9zPnWEN8FbEAFTBK08srvYSUnA/w55xmgAAknNkADpI9+51MW/egmovo9Opre0SvXv3ii1bXomdO9v2ynWWUiIioqamZq9c397SvXtdtZcAuyUGoIPU1NREXV2Pai+j06mt7RI9evSIurrW6Np178QA8PY4TQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJ1ZRSyp7sWEqJtrY92pX/0rVrl2htbav2MvgfzKXzMZPOyVw6p7czly5daqKmpuYt99vjGAAA3pucJgCA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODOwF69atiwEDBuzy7/bbb4+IiFWrVkVjY2N85CMfiVGjRsVNN91U5RXnsHjx4jjppJOioaEhTj755PjRj35UucxMOt7999+/28fJgAED4vjjj48Ic6mGHTt2xKxZs2LUqFFx+OGHx6RJk2LZsmWVy82kOl555ZX46le/GiNHjoxhw4bFlClT4te//nXl8r0+l8JvbcmSJaWhoaE8++yzZdOmTZV/27dvL5s3by4f+9jHyowZM8qaNWvKHXfcURoaGsodd9xR7WW/py1evLgceuih5ZZbbilr164tc+bMKQMHDizLli0zkyppbm5u9/jYtGlT+dnPflYGDRpU/v7v/95cquT6668vI0aMKPfdd19Zu3ZtmTFjRvnoRz9aNm7caCZVdM4555Rjjz223HvvvWXNmjXly1/+cjnmmGPK5s2b35G5iIG9YN68eWXs2LG7veyGG24oxx57bNmxY0dl2zXXXFM+9alPddTy0mlrayujR48uV155ZbvtZ511VrnhhhvMpJNoaWkpJ598cjn//PNLKR4r1TJ27Ngyc+bMyvvbtm0r9fX15cc//rGZVMmqVatKfX19+elPf1rZ1traWsaMGVPmzJnzjszFaYK94Je//GX0799/t5c9+OCDMXz48Kitra1sO+qoo+Lpp5+OF154oaOWmMpTTz0V69ati1NPPbXd9ptuuinOPfdcM+kkbrvtttiwYUNMnz49IjxWqqV3797xL//yL/HMM89Ea2trLFq0KLp37x6HHnqomVTJ008/HRERRxxxRGVbly5dYuDAgfHAAw+8I3MRA3vB448/Hi+88EJMmjQpjjnmmJg4cWLcd999ERGxcePGOOCAA9rt/8EPfjAiItavX9/ha81g7dq1ERHx6quvxtlnnx1HH310TJgwIe69996IMJPOoLm5OW644YY444wzKve9uVTHjBkzora2No4//vhoaGiIWbNmxXXXXRcHHXSQmVTJ/vvvHxGvPybebN26dfHCCy+8I3MRA7+llpaWWLt2bbz88stx/vnnx/z586OhoSE+97nPRVNTU7z22mvRvXv3dh9TV1cXEa9/Q2Tve/nllyMiYurUqXHKKafEggULYsSIETFlyhQz6STuvvvuaG5ujsmTJ1e2mUt1PPnkk7HvvvvG3LlzY9GiRTFu3LiYOnVqrF692kyq5LDDDotDDjkkLr300tiwYUO0tLTELbfcEqtWrYqWlpZ3ZC61b70Lv0n37t3jgQceiNra2spwhgwZEk8++WTcdNNN0aNHj2hpaWn3MW8Mq2fPnh2+3gy6desWERFnn312nH766RERceihh8Zjjz0WN998s5l0AosXL44xY8bEfvvtV9lmLh1v3bp18cUvfjFuueWWyiHphoaGWLNmTcyePdtMqqRbt24xd+7cmDZtWowaNSpqa2tj1KhRMX78+Fi5cmW0tLTs9bk4MrAX9OzZc5dKq6+vj2effTYOOOCA2LRpU7vL3ni/b9++HbbGTN44fFZfX99ue//+/eOZZ54xkyrbvHlzLF++PE466aR2282l4z3yyCOxY8eOaGhoaLf9sMMOi7Vr15pJFfXr1y8WLVoU//Ef/xFNTU0xd+7c2LJlSxx88MHvyFzEwG9p9erVcfjhh8eDDz7YbvvKlSujf//+MXz48HjooYeitbW1cllTU1P069cv+vTp09HLTWHQoEHRq1evWLFiRbvtjz/+eBx00EFmUmXLli2LmpqaOPLII9ttN5eO96EPfSgiXn8R9Js9/vjj8eEPf9hMquTll1+OxsbGWLlyZfzu7/5u7LvvvrFt27ZYunRpHHvsse/MXH6r33+gtLa2lgkTJpRTTjmlPPDAA2XNmjXliiuuKEOGDCmrV68uzz//fBk+fHiZOnVqeeKJJ8qdd95ZGhoayl133VXtpb+nzZ07txx++OHlnnvuKb/61a/Kt771rTJw4MDy85//3EyqbPbs2WXMmDG7bDeXjtfa2lomTZpUTjzxxNLU1FSefvrpMmvWrHLooYeW5cuXm0kVNTY2lokTJ5bVq1eXVatWlUmTJpWxY8eWHTt2vCNzqSmllL0UM2lt3rw5vvGNb8S//du/xUsvvRSDBg2Kiy++uHIO7pFHHomvf/3r8dhjj8X+++8fZ511VjQ2NlZ51e99N998cyxcuDCeffbZOOSQQ+Lzn/98fPKTn4wIM6mmyy67LFatWhWLFi3a5TJz6Xhbt26N6667Ln7605/G1q1bo76+Pi688MLKkRszqY5NmzbF1772tWhqaoouXbrE6NGj46//+q/j/e9/f0Ts/bmIAQBIzmsGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgA9gq/pQzvXmIA3obJkye3+0t7/9O0adPiuOOOq7y/cePGaGxsjIaGhjj66KNj+/btHbHMDrdmzZqYOHFitZcB/B/5q4WwF02ZMiU++9nPVt6/9dZbY/ny5XH11VdH375943d+53equLp3zo9+9KNYvnx5tZcB/B+JAdiLDjrooHbvb9myJT74wQ/u8hf6ADoTpwlgL3rzaYLjjjsu7rrrrli/fn0MGDAgZs+eHRGvB8Ill1wSxxxzTDQ0NMQf//EfR1NTU7vraWtri/nz58cJJ5wQQ4YMiU996lPx3e9+t3L5L37xixg8eHBMmzatsu3FF1+MESNGxOTJk6OtrS3uuuuuGDBgQKxYsSJOP/30GDp0aJx66qnxwx/+sN1tNTc3x1VXXRUjR46MIUOG7HafUkrcdtttcfLJJ8fQoUPjhBNOiG9/+9tRSonZs2fHnDlzIiLafZ6bN2+Or3zlKzF69OgYMmRIHHnkkXHeeefFM888U7neyZMnx4wZM2L+/PkxatSoaGhoiD/5kz/Z5S9Orly5Ms4555wYNmxYHHXUUXHBBRfEhg0bYufOnfHxj388Lrrool1m8Yd/+Icxffr0PRscJCcG4B0yZ86cGDlyZOy///6xaNGimDBhQjQ3N8cZZ5wRS5YsiQsuuCDmzJkTBxxwQJxzzjntguCyyy6Lb37zmzF27Ni44YYb4sQTT4wrrrgi5s6dGxERgwcPjnPPPTe+//3vVz7u0ksvjZaWlrjqqquiS5f/fmife+65cfzxx8ecOXOiX79+ceGFF8aSJUsi4vUn+fPOOy++973vxZlnnhnz5s2Lww8/PC644IJYvHhx5Tquvfba+PrXvx4jR46MefPmxYQJE2LWrFnxrW99KyZMmBDjx4+PiKh8nqWUOPfcc+Pf//3f46KLLoqbbroppkyZEkuXLo1LLrmk3f30k5/8JJYsWRJf/vKX49prr43nn38+vvCFL1T+POvq1atj4sSJsX379rjyyivj8ssvj8ceeyzOOuusKKXEaaedFv/8z/8cL7/8cuU6V6xYEU899VSMGzduL04U3sN+67+zCIk0NjaWxsbG//XyqVOnltGjR/+v7y9atKjU19eXhx9+uLKtra2t/Omf/mkZN25cKaWUp556qgwYMKDceOON7a571qxZpaGhoWzevLmUUkpLS0s57bTTypgxY8r3v//9Ul9fX+65557K/nfeeWepr68vs2fPbndbn/70pyu39bOf/azU19eXf/zHf2x3WxdffHEZMWJE2bFjR9m6dWsZPHhwueKKK9rtM3PmzHLmmWeWUkr55je/Werr6yuXbdy4sUyePLk88MAD7T7mq1/9ahk8eHC7+/Owww4r27Ztq2x743N59NFHSymlfP7zny8jRowor732WmWfFStWlNGjR5dHH320PPXUU6W+vr7ccccdlcsvvfTS8slPfrK0tbUV4K05MgAdqKmpKfbff/8YPHhw7Ny5M3bu3Bmtra0xevToWLlyZWzdujV+/vOfRykljjvuuMo+O3fujOOOOy6am5vjoYceioiIbt26xZVXXhnr1q2L6dOnx6mnnhqnnHLKLrf56U9/uvJ2TU1NnHDCCfGLX/witm/fHk1NTVFTUxMjR47c5baee+65eOKJJ+Lhhx+OHTt2xAknnNDueqdNmxYLFizY7efZt2/f+M53vhNHHHFErF+/PpqammLhwoWxbNmy2LFjR7t9+/fvH+973/vafWxEVH7z4qGHHopPfOITUVdXV9ln6NChce+998aQIUOiX79+MWzYsLj77rsjIqKlpSV++MMfxmmnnRY1NTV7PBvIzAsIoQNt2bIlnnvuuRg8ePBuL3/uuediy5YtERFx8skn73afZ599tvL2gAEDYvDgwfHwww+3+5XGN3vjyfUNffr0iVJKbNu2LbZs2RKllPjoRz+624/dtGlTbN26NSKi8nfU99Q//MM/xLXXXhsbNmyI3r17x8CBA6NHjx677Pc/f8PijVMcbW1tEfH6fdanT5/feFvjx4+PL33pS7F+/fpYsWJFvPTSS3H66ae/rfVCZmIAOtA+++wTBx98cHzjG9/Y7eUHHnhg7LvvvhHx+q8l9urVa5d9fu/3fq/y9u233x4PP/xwDBw4MK644oo45phjonfv3u32f/HFF9sFwfPPPx9du3aN3r17xz777BM9e/aM73znO7tdz4c//OFYtmxZRLz+gsA/+IM/qFy2YcOG+NWvfhXDhg3b5eMefPDBmDp1ajQ2NsbZZ58dBxxwQEREXHXVVZUjG3tqn332ic2bN++y/V//9V9j4MCB0bdv3zjxxBPja1/7WvzkJz+J5cuXx9FHH93ufgJ+M6cJoAMdeeSRsWHDhujTp080NDRU/jU1NcXf/u3fRteuXWP48OER8fqT+Jv32bJlS1x33XWVIwfr16+PK6+8MsaNGxfz58+P7du3x+WXX77Lbd57772Vt0sp8U//9E8xbNiw6N69exx55JHx6quvRiml3W098cQTMXfu3Ni5c2cMHTo0unXrVnnR4RtuvfXW+Ku/+quoqalp94LFiIjly5dHW1tbfOELX6iEQGtrayxdujQi/vun/j1xxBFHxH333RctLS2Vbb/85S/jz//8z+PRRx+NiIiePXvGSSedFD/4wQ/ivvvuc1QA3iZHBuBt2rhxY9xyyy27bO/fv/9bfuy4ceNi4cKFceaZZ8Zf/MVfxIc+9KFYunRpfPvb347Gxsbo1q1b1NfXx9ixY+Nv/uZvYt26dTFkyJB4+umnY9asWXHggQfGwQcfHKWUmDFjRvTo0SOmTp0avXv3jgsvvDAuv/zyGDNmTJx44omV27z66qujpaUl+vXrF7fffns8+eSTceutt0ZExMiRI2P48OExZcqUmDJlShxyyCHxyCOPxOzZs+PjH/945dTAZz/72bj11luje/fucdRRR8Wjjz4aCxcujAsvvDBqa2srRzN+8IMfxGGHHRZDhw6NiIjLL788/uiP/iheeumlWLhwYaxevToiIl599dV2rxP4TaZMmRKf+cxn4nOf+1ycccYZ0dLSEtdff30MHjw4PvGJT1T2Gz9+fHzmM5+J973vfTFmzJg9um7gdWIA3qZf//rXMXPmzF2278lPoz179ozbbrstrrnmmrj66qtj27Zt8fu///tx0UUXxVlnnVXZb+bMmXHjjTfG9773vdi4cWP06dMnTjrppDj//POja9eucdttt8XSpUtj1qxZldMCEydOjHvuuScuu+yyytGFiNd/TfHGG2+M//zP/4xBgwbFggUL4ogjjoiI18/Pz58/P66//vq48cYb44UXXoi+ffvGn/3Zn8V5551XuY4vfvGL8YEPfCD+7u/+LhYsWBAHHnhgfOlLX4pJkyZFRMSYMWPi7rvvjmnTpsX48ePjsssui0suuSRuvvnm+PGPfxwf+MAH4mMf+1jMmTMnzjvvvHjooYdi5MiRe3R/Dxo0KL773e/GNddcExdccEH06tUrRo4cGRdffHF07969st9HPvKR2G+//WLMmDG7fW0C8L+rKcVfF4H3orvuuiumT58eS5YsiQMPPLDay3nHPfLIIzFhwoS48847Y8iQIdVeDryrODIAvKvdf//9cf/998fixYvjqKOOEgLwf+AFhMC72osvvhg333xz9OnTZ7enb4C35jQBACTnyAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAk9/8Bn327FxqBkEIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in data[['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years','Lifeexpectancy']]:\n",
    "    sns.boxplot(data=data,x=data[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9481f476",
   "metadata": {},
   "source": [
    "# Multiple Liner Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "9c5c2d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "94c8ed46",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=data[['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years']]\n",
    "\n",
    "y=data['Lifeexpectancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "98c5ba3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sm.add_constant(x1)\n",
    "\n",
    "results = sm.OLS(y,x).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "23729bd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>Lifeexpectancy</td>  <th>  R-squared:         </th> <td>   0.806</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.805</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1215.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 23 Jul 2023</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:54:42</td>     <th>  Log-Likelihood:    </th> <td> -8368.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2938</td>      <th>  AIC:               </th> <td>1.676e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2927</td>      <th>  BIC:               </th> <td>1.683e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    10</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                 <td>   73.6230</td> <td>    0.401</td> <td>  183.806</td> <td> 0.000</td> <td>   72.838</td> <td>   74.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Adultmortality</th>        <td>   -0.0192</td> <td>    0.001</td> <td>  -22.133</td> <td> 0.000</td> <td>   -0.021</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Infantdeaths</th>          <td>   -0.0658</td> <td>    0.006</td> <td>  -11.655</td> <td> 0.000</td> <td>   -0.077</td> <td>   -0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alcohol</th>               <td>    0.2274</td> <td>    0.024</td> <td>    9.572</td> <td> 0.000</td> <td>    0.181</td> <td>    0.274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Percentageexpenditure</th> <td>    0.0022</td> <td>    0.000</td> <td>    7.300</td> <td> 0.000</td> <td>    0.002</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Measles</th>               <td>   -0.0003</td> <td>    0.000</td> <td>   -1.107</td> <td> 0.268</td> <td>   -0.001</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bmi</th>                   <td>    0.0234</td> <td>    0.005</td> <td>    4.554</td> <td> 0.000</td> <td>    0.013</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Totalexpenditure</th>      <td>    0.1110</td> <td>    0.036</td> <td>    3.065</td> <td> 0.002</td> <td>    0.040</td> <td>    0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Hiv/aids</th>              <td>   -6.5382</td> <td>    0.151</td> <td>  -43.330</td> <td> 0.000</td> <td>   -6.834</td> <td>   -6.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Gdp</th>                   <td>    0.0001</td> <td> 2.08e-05</td> <td>    6.346</td> <td> 0.000</td> <td> 9.14e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Thinness1-19years</th>     <td>   -0.1497</td> <td>    0.026</td> <td>   -5.732</td> <td> 0.000</td> <td>   -0.201</td> <td>   -0.099</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>70.038</td> <th>  Durbin-Watson:     </th> <td>   0.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 141.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.127</td> <th>  Prob(JB):          </th> <td>1.68e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.045</td> <th>  Cond. No.          </th> <td>3.96e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.96e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         Lifeexpectancy   R-squared:                       0.806\n",
       "Model:                            OLS   Adj. R-squared:                  0.805\n",
       "Method:                 Least Squares   F-statistic:                     1215.\n",
       "Date:                Sun, 23 Jul 2023   Prob (F-statistic):               0.00\n",
       "Time:                        23:54:42   Log-Likelihood:                -8368.8\n",
       "No. Observations:                2938   AIC:                         1.676e+04\n",
       "Df Residuals:                    2927   BIC:                         1.683e+04\n",
       "Df Model:                          10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "const                    73.6230      0.401    183.806      0.000      72.838      74.408\n",
       "Adultmortality           -0.0192      0.001    -22.133      0.000      -0.021      -0.017\n",
       "Infantdeaths             -0.0658      0.006    -11.655      0.000      -0.077      -0.055\n",
       "Alcohol                   0.2274      0.024      9.572      0.000       0.181       0.274\n",
       "Percentageexpenditure     0.0022      0.000      7.300      0.000       0.002       0.003\n",
       "Measles                  -0.0003      0.000     -1.107      0.268      -0.001       0.000\n",
       "Bmi                       0.0234      0.005      4.554      0.000       0.013       0.033\n",
       "Totalexpenditure          0.1110      0.036      3.065      0.002       0.040       0.182\n",
       "Hiv/aids                 -6.5382      0.151    -43.330      0.000      -6.834      -6.242\n",
       "Gdp                       0.0001   2.08e-05      6.346      0.000    9.14e-05       0.000\n",
       "Thinness1-19years        -0.1497      0.026     -5.732      0.000      -0.201      -0.099\n",
       "==============================================================================\n",
       "Omnibus:                       70.038   Durbin-Watson:                   0.631\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              141.721\n",
       "Skew:                          -0.127   Prob(JB):                     1.68e-31\n",
       "Kurtosis:                       4.045   Cond. No.                     3.96e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.96e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "2e703895",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "932645cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data [['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years']]\n",
    "y=data['Lifeexpectancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "630097c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "ee704460",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "420b4167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "b4ad7218",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "c2046927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([71.37491442, 79.18907848, 75.74478403, 77.67923469, 49.0337887 ,\n",
       "       50.36786504, 74.59400857, 70.22426132, 76.12617169, 56.32865356,\n",
       "       50.21866941, 52.59727251, 59.25361412, 72.81264062, 69.66924606,\n",
       "       58.21559999, 61.05557945, 79.97844845, 60.33908345, 79.48709877,\n",
       "       80.40706949, 80.93866905, 72.23756925, 73.49849981, 71.09931534,\n",
       "       59.89968322, 61.93033397, 66.71255998, 75.05054772, 74.05677074,\n",
       "       66.73023415, 76.96530268, 73.87693342, 64.99481569, 80.80952353,\n",
       "       57.70739627, 57.55538605, 72.49789945, 69.44227014, 75.58136311,\n",
       "       53.51432306, 56.06879391, 77.17888176, 79.56406746, 75.15118908,\n",
       "       59.40322203, 80.17364467, 71.01165218, 67.00296793, 65.39100088,\n",
       "       58.51466121, 72.06210412, 77.15375112, 75.65384514, 73.39778884,\n",
       "       53.85822336, 77.8223637 , 73.85656505, 71.37636737, 67.58156385,\n",
       "       76.64010804, 53.39828121, 71.67284012, 68.32435745, 53.14301755,\n",
       "       53.92935837, 53.34278152, 81.53309101, 58.12456403, 77.17302531,\n",
       "       79.94193022, 74.70978948, 72.62774869, 53.16704037, 52.63592718,\n",
       "       72.94495236, 66.44002968, 74.5089553 , 53.9816472 , 65.12992238,\n",
       "       64.5967131 , 62.51329747, 69.52376808, 68.57302835, 66.35834198,\n",
       "       79.37156062, 75.96865846, 66.59551895, 76.21609944, 79.49973538,\n",
       "       74.31078806, 59.58037109, 71.52686646, 76.60041151, 76.83145048,\n",
       "       67.08170921, 76.7152185 , 68.32007675, 67.31187103, 61.11370042,\n",
       "       79.93872555, 52.33914593, 69.22711248, 53.10458362, 74.82674116,\n",
       "       79.06046476, 73.98966832, 66.40290946, 62.80345424, 54.25609132,\n",
       "       70.52156118, 69.02454762, 53.67617019, 67.58032585, 62.47409042,\n",
       "       78.00048675, 59.61809824, 53.81986129, 55.47373271, 66.30275988,\n",
       "       65.75882348, 74.07053467, 79.05063219, 57.53446454, 64.60326099,\n",
       "       63.96093674, 69.46517838, 58.10963275, 74.27365811, 72.8783292 ,\n",
       "       73.92964488, 79.9282872 , 72.32471836, 70.81716769, 58.00537199,\n",
       "       71.72821353, 73.95126898, 76.75362195, 77.27576429, 74.06168854,\n",
       "       69.06714404, 68.06060638, 60.70302433, 60.93355569, 50.34214664,\n",
       "       69.61330607, 72.70270103, 76.03934198, 52.74417906, 77.78956358,\n",
       "       77.10350323, 80.31399668, 68.04618611, 61.36880087, 80.99259007,\n",
       "       78.93085303, 80.92155365, 76.71168092, 81.66826777, 77.1987601 ,\n",
       "       74.97761032, 80.0542202 , 72.4519645 , 77.1276087 , 71.01449344,\n",
       "       66.38953682, 62.20367565, 75.96492728, 77.22129349, 75.64519769,\n",
       "       79.38185936, 72.77137721, 74.58788453, 55.14481135, 69.53198279,\n",
       "       71.04251506, 69.40152116, 61.55191898, 60.84043095, 72.79221819,\n",
       "       81.28711046, 64.75167857, 80.87639034, 71.37904152, 77.26315589,\n",
       "       51.42644259, 66.09748017, 49.17167555, 72.91669703, 74.50770786,\n",
       "       57.47902987, 71.86213745, 79.4955212 , 67.65313094, 77.05457561,\n",
       "       65.75327046, 73.74277722, 70.03302352, 78.36739534, 75.58620194,\n",
       "       73.10167532, 53.6521381 , 74.16949186, 68.18820146, 66.37083883,\n",
       "       58.07517304, 74.93171695, 71.98368104, 70.328922  , 81.17198809,\n",
       "       57.48766592, 51.55479133, 74.79150136, 54.96005563, 61.77922822,\n",
       "       65.01898426, 68.49273701, 74.25886744, 57.26086904, 68.86007419,\n",
       "       78.8399596 , 75.50472767, 75.29652372, 78.34237724, 78.94873493,\n",
       "       51.08534478, 57.70862876, 77.22252647, 54.15826556, 60.68407545,\n",
       "       69.87036942, 71.8210659 , 71.39884092, 74.45019643, 73.43477261,\n",
       "       53.04162894, 75.18332894, 79.73775108, 50.28330081, 73.4123977 ,\n",
       "       74.6376539 , 70.24506484, 73.54315952, 71.84070875, 61.77378623,\n",
       "       53.93385865, 79.75979161, 73.79643868, 75.78686651, 60.27609737,\n",
       "       70.08809618, 73.98309192, 54.39967781, 80.59863983, 72.54848683,\n",
       "       71.19687404, 57.18858744, 81.13073083, 53.68865233, 55.18631814,\n",
       "       69.07229625, 72.8796661 , 67.55306757, 50.77043086, 74.03935913,\n",
       "       81.11888711, 65.33951755, 80.50255388, 53.22007271, 59.53025627,\n",
       "       60.36369218, 49.09309819, 73.69840702, 70.12824733, 60.49390672,\n",
       "       73.75126496, 64.08137227, 72.24949629, 67.24597517, 69.73899839,\n",
       "       71.16425237, 79.99805737, 62.96772236, 75.40532016, 76.02158465,\n",
       "       68.25294874, 78.05858901, 50.13341494, 69.44075982, 75.96060075,\n",
       "       65.24374881, 69.74611238, 67.79052193, 75.53553378, 73.92764173,\n",
       "       62.1293827 , 80.09446401, 70.18045806, 57.21290225, 70.27551242,\n",
       "       72.24384456, 76.22033481, 73.84721709, 71.8138653 , 75.58571495,\n",
       "       51.85176482, 58.54900355, 53.68904193, 72.12528111, 75.74667526,\n",
       "       74.97761531, 52.30772151, 73.42375618, 62.65894099, 73.52145335,\n",
       "       59.69738385, 57.15050958, 73.36842569, 57.24850874, 75.61227271,\n",
       "       54.56388432, 79.88979543, 52.17282936, 69.58159706, 69.72592645,\n",
       "       76.88401679, 71.97542918, 72.45602909, 73.56338606, 78.83147192,\n",
       "       76.96719902, 71.04762279, 63.29888661, 71.38354097, 74.98733359,\n",
       "       80.0736959 , 74.0288143 , 77.49753792, 75.52977636, 72.57139719,\n",
       "       70.59107817, 77.93429981, 69.53048018, 71.00996454, 73.48092463,\n",
       "       53.1135626 , 59.29296282, 73.68566553, 69.4844215 , 76.11334919,\n",
       "       62.45930876, 65.82437696, 60.30018566, 56.15425227, 72.58613904,\n",
       "       75.7616869 , 72.76347986, 73.50795726, 74.86449221, 74.2784799 ,\n",
       "       60.88555734, 75.54547528, 77.62062279, 70.13182092, 70.84657747,\n",
       "       53.21107561, 55.87648831, 73.77464384, 54.31342973, 76.96999993,\n",
       "       74.77134299, 56.10650977, 72.84899415, 77.25228696, 76.96132139,\n",
       "       69.9535599 , 53.89601173, 68.61431604, 69.89782712, 65.72438823,\n",
       "       51.15722376, 80.58949634, 82.12433376, 76.4046709 , 59.83199127,\n",
       "       54.16577975, 75.63275742, 53.20656255, 73.95721522, 75.65699606,\n",
       "       52.24606656, 81.04184963, 76.60721624, 80.97110997, 68.62145692,\n",
       "       66.28689453, 79.13209407, 72.87555833, 80.50115469, 73.15330962,\n",
       "       50.99559172, 77.58398957, 53.99543578, 71.13760181, 64.09108398,\n",
       "       62.78093963, 66.47740998, 70.78546575, 66.53885959, 72.43664105,\n",
       "       73.87174014, 79.88551342, 75.87572676, 77.77544669, 73.26971231,\n",
       "       65.652937  , 62.26014607, 72.45105832, 67.55420625, 69.09663148,\n",
       "       69.08778955, 55.87466027, 70.50078743, 74.84631371, 73.69086525,\n",
       "       74.6611818 , 60.41604889, 61.98844917, 73.82429858, 74.81551593,\n",
       "       57.63613194, 77.6460086 , 70.10384437, 67.44199751, 50.88418017,\n",
       "       57.93012392, 50.98110355, 70.82492381, 68.66448816, 75.56960417,\n",
       "       76.16117215, 72.83503518, 73.47428375, 80.09652531, 76.34455226,\n",
       "       70.27697097, 77.58289565, 79.9381561 , 82.30083489, 61.44685195,\n",
       "       80.81399677, 51.48549151, 69.80430977, 75.13194146, 50.88978351,\n",
       "       77.21682   , 80.72241961, 49.41417126, 64.87639153, 74.05897391,\n",
       "       73.626741  , 58.45372065, 61.3930471 , 67.70478626, 63.77897363,\n",
       "       54.57333649, 79.03661127, 70.68715408, 80.18463907, 67.4383411 ,\n",
       "       67.94972173, 78.19444801, 69.55783604, 72.91969682, 80.70132429,\n",
       "       71.94649514, 69.51699777, 71.18289783, 80.77743139, 74.56851456,\n",
       "       74.66320259, 71.89577083, 71.07805294, 79.30457559, 71.40062904,\n",
       "       80.86385566, 79.04538249, 79.99143298, 68.54418089, 73.60002544,\n",
       "       73.86661367, 69.47981584, 51.60083733, 70.68564411, 73.41915716,\n",
       "       64.14804897, 75.61821827, 70.58431382, 58.24143115, 71.46174586,\n",
       "       69.27993573, 56.06838515, 77.9677197 , 72.61538571, 75.64741187,\n",
       "       77.559149  , 74.88386328, 82.13453911, 70.23115526, 75.78323761,\n",
       "       58.49716185, 73.31719904, 72.04502969, 72.2815202 , 51.35104755,\n",
       "       64.39979245, 59.66251821, 57.14519688, 64.81261536, 53.90983558,\n",
       "       75.54407319, 75.00746444, 70.36015919, 73.46410521, 74.48758303,\n",
       "       67.94987145, 61.55362823, 72.63484236, 74.50862099, 80.28621471,\n",
       "       50.98670918, 70.66505062, 73.2938348 , 80.19713947, 67.26872152,\n",
       "       79.17362166, 74.37414762, 81.14506198, 80.22036904, 82.03810827,\n",
       "       80.48291948, 52.00772549, 57.63592816, 70.46328253, 75.94164237,\n",
       "       56.90898859, 65.14681398, 56.68355356, 60.55107548, 74.33302608,\n",
       "       78.65145736, 62.29674567, 76.94351772, 54.21111292, 74.047321  ,\n",
       "       64.67485118, 76.40490938, 66.34840497, 71.66661345, 80.12667397,\n",
       "       57.17427921, 78.17434456, 81.40454602, 75.94667317, 51.61491748,\n",
       "       75.92539162, 78.8914355 , 81.13359899, 51.43727864, 72.68475287,\n",
       "       73.15045458, 65.42136825, 77.17598098, 73.37205024, 71.87755105,\n",
       "       71.64215719, 71.86363622, 73.96997122, 59.50497877, 75.51368743,\n",
       "       55.98733601, 76.66030129, 81.92151103, 57.15181633, 77.45954763,\n",
       "       59.54139322, 68.51700961, 74.14393279])"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "72b21d60",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Adultmortality</th>\n",
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       "      <td>43.979713</td>\n",
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      "text/plain": [
       "      Adultmortality  Infantdeaths    Alcohol  Percentageexpenditure  Measles  \\\n",
       "2546           123.0           8.0   0.970000             122.652333  517.000   \n",
       "650            113.0           0.0  11.830000            1096.807347    1.000   \n",
       "1740           125.0           0.0   4.980000             678.518894    0.000   \n",
       "177              7.0           0.0   1.570000             367.255674   46.000   \n",
       "1377           428.0          55.0   1.510000               0.681686  900.625   \n",
       "...              ...           ...        ...                    ...      ...   \n",
       "859            336.0           7.0   0.970000              10.602698  128.000   \n",
       "1405            91.0           0.0   0.020000            1096.807347   23.000   \n",
       "962            262.0           3.0   4.602861               0.000000   71.000   \n",
       "2658           188.0           0.0   6.350000             103.371069    0.000   \n",
       "2272           132.0           1.0   9.540000              96.332379    2.000   \n",
       "\n",
       "       Bmi  Totalexpenditure  Hiv/aids           Gdp  Thinness1-19years  \\\n",
       "2546  48.5           3.78000      0.10   1762.246170                6.4   \n",
       "650   58.1           6.95000      0.10  11363.418450                1.8   \n",
       "1740  57.2           6.74000      0.10   5957.145693                2.1   \n",
       "177   62.9           4.98000      0.10  17837.165680                6.1   \n",
       "1377  14.4           4.68000      1.85     43.979713                9.2   \n",
       "...    ...               ...       ...           ...                ...   \n",
       "859   14.3           3.30000      1.40    297.828588                9.3   \n",
       "1405  65.9           2.76000      0.10  17837.165680                3.2   \n",
       "962   27.3           5.93819      1.70   7483.158469                7.3   \n",
       "2658  38.4           4.49000      0.60   1495.963370                6.2   \n",
       "2272  55.8           1.50000      0.10    671.773913                2.3   \n",
       "\n",
       "      actual  predicted  \n",
       "2546    73.7  71.374914  \n",
       "650     75.9  79.189078  \n",
       "1740    74.2  75.744784  \n",
       "177     76.8  77.679235  \n",
       "1377    51.9  49.033789  \n",
       "...      ...        ...  \n",
       "859     59.7  57.151816  \n",
       "1405    73.5  77.459548  \n",
       "962     61.1  59.541393  \n",
       "2658    69.6  68.517010  \n",
       "2272    74.0  74.143933  \n",
       "\n",
       "[588 rows x 12 columns]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test['actual'] = y_test\n",
    "X_test['predicted'] = y_pred\n",
    "\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "14eb7865",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "MAE is 3.0244611446679754\n",
      "MSE is 15.89435023159835\n",
      "RMSE is 3.9867719061414024\n",
      "R2 score is 0.8162693530830407\n"
     ]
    }
   ],
   "source": [
    "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
    "mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = metrics.r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"The model performance for testing set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('MAE is {}'.format(mae))\n",
    "print('MSE is {}'.format(mse))\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a534ea44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e72311c2",
   "metadata": {},
   "source": [
    "# Decision Tree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "634bf518",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>456.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000</td>\n",
       "      <td>27.1</td>\n",
       "      <td>7.13</td>\n",
       "      <td>1.85</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>9.40</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>456.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900.625</td>\n",
       "      <td>26.7</td>\n",
       "      <td>6.52</td>\n",
       "      <td>1.85</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>9.80</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>73.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>304.000</td>\n",
       "      <td>26.3</td>\n",
       "      <td>6.53</td>\n",
       "      <td>1.85</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>1.20</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>456.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>529.000</td>\n",
       "      <td>25.9</td>\n",
       "      <td>6.16</td>\n",
       "      <td>1.85</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>1.60</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>456.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900.625</td>\n",
       "      <td>25.5</td>\n",
       "      <td>7.10</td>\n",
       "      <td>1.85</td>\n",
       "      <td>547.358878</td>\n",
       "      <td>11.00</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adultmortality  Infantdeaths  Alcohol  Percentageexpenditure  Measles  \\\n",
       "0              263.0          55.0     0.01              71.279624  900.625   \n",
       "1              271.0          55.0     0.01              73.523582  492.000   \n",
       "2              268.0          55.0     0.01              73.219243  430.000   \n",
       "3              272.0          55.0     0.01              78.184215  900.625   \n",
       "4              275.0          55.0     0.01               7.097109  900.625   \n",
       "...              ...           ...      ...                    ...      ...   \n",
       "2933           456.5          27.0     4.36               0.000000   31.000   \n",
       "2934           456.5          26.0     4.06               0.000000  900.625   \n",
       "2935            73.0          25.0     4.43               0.000000  304.000   \n",
       "2936           456.5          25.0     1.72               0.000000  529.000   \n",
       "2937           456.5          24.0     1.68               0.000000  900.625   \n",
       "\n",
       "       Bmi  Totalexpenditure  Hiv/aids         Gdp  Thinness1-19years  \\\n",
       "0     19.1              8.16      0.10  584.259210              15.35   \n",
       "1     18.6              8.18      0.10  612.696514              15.35   \n",
       "2     18.1              8.13      0.10  631.744976              15.35   \n",
       "3     17.6              8.52      0.10  669.959000              15.35   \n",
       "4     17.2              7.87      0.10   63.537231              15.35   \n",
       "...    ...               ...       ...         ...                ...   \n",
       "2933  27.1              7.13      1.85  454.366654               9.40   \n",
       "2934  26.7              6.52      1.85  453.351155               9.80   \n",
       "2935  26.3              6.53      1.85   57.348340               1.20   \n",
       "2936  25.9              6.16      1.85  548.587312               1.60   \n",
       "2937  25.5              7.10      1.85  547.358878              11.00   \n",
       "\n",
       "      Lifeexpectancy  \n",
       "0               65.0  \n",
       "1               59.9  \n",
       "2               59.9  \n",
       "3               59.5  \n",
       "4               59.2  \n",
       "...              ...  \n",
       "2933            44.6  \n",
       "2934            44.6  \n",
       "2935            44.8  \n",
       "2936            45.3  \n",
       "2937            46.0  \n",
       "\n",
       "[2938 rows x 11 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "44a29e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= data [['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years']]\n",
    "\n",
    "y=data['Lifeexpectancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "7e0f0c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "d16113b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "2b4f3d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, X_test, y_test):\n",
    "    \n",
    "    y_pred_DT = reg.predict(X_test)\n",
    "\n",
    "    mse = metrics.mean_squared_error(y_test, y_pred_DT)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2 = metrics.r2_score(y_test, y_pred_DT)\n",
    "\n",
    "    print(\"The model performance for testing set\")\n",
    "    print(\"--------------------------------------\")\n",
    "    print('RMSE is {}'.format(rmse))\n",
    "    print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "e1aaa6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = DecisionTreeRegressor()\n",
    "\n",
    "reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "afbf87aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "RMSE is 3.1848666404349526\n",
      "R2 score is 0.8905567168104495\n"
     ]
    }
   ],
   "source": [
    "result = evaluate(reg, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161ae940",
   "metadata": {},
   "source": [
    "# Random Forest Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "baaca2ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regRF = RandomForestRegressor(n_estimators = 100)\n",
    "\n",
    "regRF.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "3a261880",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, X_test, y_test):\n",
    "    \n",
    "    y_pred_RF = regRF.predict(X_test)\n",
    "\n",
    "    mse = metrics.mean_squared_error(y_test, y_pred_RF)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2 = metrics.r2_score(y_test, y_pred_RF)\n",
    "\n",
    "    print(\"The model performance for testing set\")\n",
    "    print(\"--------------------------------------\")\n",
    "    print('RMSE is {}'.format(rmse))\n",
    "    print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "74a1b7b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "RMSE is 1.996178368183604\n",
      "R2 score is 0.9570062527326914\n"
     ]
    }
   ],
   "source": [
    "result = evaluate(regRF, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48999880",
   "metadata": {},
   "source": [
    "# Support Vector Machines for Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "2d533d17",
   "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>Adultmortality</th>\n",
       "      <th>Infantdeaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Percentageexpenditure</th>\n",
       "      <th>Measles</th>\n",
       "      <th>Bmi</th>\n",
       "      <th>Totalexpenditure</th>\n",
       "      <th>Hiv/aids</th>\n",
       "      <th>Gdp</th>\n",
       "      <th>Thinness1-19years</th>\n",
       "      <th>Lifeexpectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>900.625</td>\n",
       "      <td>19.1</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>15.35</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>271.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>492.000</td>\n",
       "      <td>18.6</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>268.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>430.000</td>\n",
       "      <td>18.1</td>\n",
       "      <td>8.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>272.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>900.625</td>\n",
       "      <td>17.6</td>\n",
       "      <td>8.52</td>\n",
       "      <td>0.10</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>275.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>900.625</td>\n",
       "      <td>17.2</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.10</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>456.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000</td>\n",
       "      <td>27.1</td>\n",
       "      <td>7.13</td>\n",
       "      <td>1.85</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>9.40</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>456.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900.625</td>\n",
       "      <td>26.7</td>\n",
       "      <td>6.52</td>\n",
       "      <td>1.85</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>9.80</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>73.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>304.000</td>\n",
       "      <td>26.3</td>\n",
       "      <td>6.53</td>\n",
       "      <td>1.85</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>1.20</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>456.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>529.000</td>\n",
       "      <td>25.9</td>\n",
       "      <td>6.16</td>\n",
       "      <td>1.85</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>1.60</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>456.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900.625</td>\n",
       "      <td>25.5</td>\n",
       "      <td>7.10</td>\n",
       "      <td>1.85</td>\n",
       "      <td>547.358878</td>\n",
       "      <td>11.00</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adultmortality  Infantdeaths  Alcohol  Percentageexpenditure  Measles  \\\n",
       "0              263.0          55.0     0.01              71.279624  900.625   \n",
       "1              271.0          55.0     0.01              73.523582  492.000   \n",
       "2              268.0          55.0     0.01              73.219243  430.000   \n",
       "3              272.0          55.0     0.01              78.184215  900.625   \n",
       "4              275.0          55.0     0.01               7.097109  900.625   \n",
       "...              ...           ...      ...                    ...      ...   \n",
       "2933           456.5          27.0     4.36               0.000000   31.000   \n",
       "2934           456.5          26.0     4.06               0.000000  900.625   \n",
       "2935            73.0          25.0     4.43               0.000000  304.000   \n",
       "2936           456.5          25.0     1.72               0.000000  529.000   \n",
       "2937           456.5          24.0     1.68               0.000000  900.625   \n",
       "\n",
       "       Bmi  Totalexpenditure  Hiv/aids         Gdp  Thinness1-19years  \\\n",
       "0     19.1              8.16      0.10  584.259210              15.35   \n",
       "1     18.6              8.18      0.10  612.696514              15.35   \n",
       "2     18.1              8.13      0.10  631.744976              15.35   \n",
       "3     17.6              8.52      0.10  669.959000              15.35   \n",
       "4     17.2              7.87      0.10   63.537231              15.35   \n",
       "...    ...               ...       ...         ...                ...   \n",
       "2933  27.1              7.13      1.85  454.366654               9.40   \n",
       "2934  26.7              6.52      1.85  453.351155               9.80   \n",
       "2935  26.3              6.53      1.85   57.348340               1.20   \n",
       "2936  25.9              6.16      1.85  548.587312               1.60   \n",
       "2937  25.5              7.10      1.85  547.358878              11.00   \n",
       "\n",
       "      Lifeexpectancy  \n",
       "0               65.0  \n",
       "1               59.9  \n",
       "2               59.9  \n",
       "3               59.5  \n",
       "4               59.2  \n",
       "...              ...  \n",
       "2933            44.6  \n",
       "2934            44.6  \n",
       "2935            44.8  \n",
       "2936            45.3  \n",
       "2937            46.0  \n",
       "\n",
       "[2938 rows x 11 columns]"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "addfa79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = data['Lifeexpectancy']\n",
    "inputs = data [['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "aa676da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(inputs, targets, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "a94737f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "def evaluate(model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    mae = metrics.mean_absolute_error(y_test, y_pred)\n",
    "    mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    r2 = metrics.r2_score(y_test, y_pred)\n",
    "    print('Model Performance')\n",
    "    print('Accuracy is', r2*100, '%')\n",
    "    \n",
    "    return r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9111471d",
   "metadata": {},
   "source": [
    "### SVR by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "8ec4df26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "Accuracy is 39.46769127297496 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "base_model = SVR()\n",
    "base_model.fit(x_train, y_train)\n",
    "base_accuracy = evaluate(base_model, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "e74a52e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'gamma': ['scale', 'auto'], 'C': [1, 10, 100, 1000.0, 10000.0, 100000.0, 1000000.0], 'epsilon': [0.1, 0.01, 0.001, 0.0001]}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "kernel = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "\n",
    "gamma = ['scale', 'auto'] \n",
    "\n",
    "C = [1, 10, 100, 1e3, 1e4, 1e5, 1e6]\n",
    "\n",
    "epsilon = [0.1 , 0.01, 0.001, 0.0001]\n",
    "\n",
    "random_grid = {'kernel': kernel,\n",
    "               'gamma': gamma,\n",
    "               'C': C,\n",
    "               'epsilon': epsilon}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "2a19e8bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=SVR(), n_iter=1, n_jobs=-1,\n",
       "                   param_distributions={&#x27;C&#x27;: [1, 10, 100, 1000.0, 10000.0,\n",
       "                                              100000.0, 1000000.0],\n",
       "                                        &#x27;epsilon&#x27;: [0.1, 0.01, 0.001, 0.0001],\n",
       "                                        &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
       "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;,\n",
       "                                                   &#x27;sigmoid&#x27;]},\n",
       "                   verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=3, estimator=SVR(), n_iter=1, n_jobs=-1,\n",
       "                   param_distributions={&#x27;C&#x27;: [1, 10, 100, 1000.0, 10000.0,\n",
       "                                              100000.0, 1000000.0],\n",
       "                                        &#x27;epsilon&#x27;: [0.1, 0.01, 0.001, 0.0001],\n",
       "                                        &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
       "                                        &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;,\n",
       "                                                   &#x27;sigmoid&#x27;]},\n",
       "                   verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVR(), n_iter=1, n_jobs=-1,\n",
       "                   param_distributions={'C': [1, 10, 100, 1000.0, 10000.0,\n",
       "                                              100000.0, 1000000.0],\n",
       "                                        'epsilon': [0.1, 0.01, 0.001, 0.0001],\n",
       "                                        'gamma': ['scale', 'auto'],\n",
       "                                        'kernel': ['linear', 'poly', 'rbf',\n",
       "                                                   'sigmoid']},\n",
       "                   verbose=1)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_random = RandomizedSearchCV(estimator = base_model, param_distributions = random_grid, n_iter = 1, cv = 3, verbose=1, n_jobs = -1)\n",
    "\n",
    "svr_random.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "7847eb5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kernel': 'poly', 'gamma': 'scale', 'epsilon': 0.0001, 'C': 10}"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "d7a11700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "Accuracy is 19.60968226969554 %\n"
     ]
    }
   ],
   "source": [
    "optimized_model = svr_random.best_estimator_\n",
    "optmized_accuracy = evaluate(optimized_model, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75ab8fcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a54be560",
   "metadata": {},
   "source": [
    "# Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "7fb10034",
   "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>Adultmortality</th>\n",
       "      <th>Infantdeaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Percentageexpenditure</th>\n",
       "      <th>Measles</th>\n",
       "      <th>Bmi</th>\n",
       "      <th>Totalexpenditure</th>\n",
       "      <th>Hiv/aids</th>\n",
       "      <th>Gdp</th>\n",
       "      <th>Thinness1-19years</th>\n",
       "      <th>Lifeexpectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>263.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>900.625</td>\n",
       "      <td>19.1</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.10</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>15.35</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>271.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>492.000</td>\n",
       "      <td>18.6</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>268.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>430.000</td>\n",
       "      <td>18.1</td>\n",
       "      <td>8.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>272.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>900.625</td>\n",
       "      <td>17.6</td>\n",
       "      <td>8.52</td>\n",
       "      <td>0.10</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>275.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>900.625</td>\n",
       "      <td>17.2</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.10</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>15.35</td>\n",
       "      <td>59.2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>456.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000</td>\n",
       "      <td>27.1</td>\n",
       "      <td>7.13</td>\n",
       "      <td>1.85</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>9.40</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>456.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900.625</td>\n",
       "      <td>26.7</td>\n",
       "      <td>6.52</td>\n",
       "      <td>1.85</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>9.80</td>\n",
       "      <td>44.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>73.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>304.000</td>\n",
       "      <td>26.3</td>\n",
       "      <td>6.53</td>\n",
       "      <td>1.85</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>1.20</td>\n",
       "      <td>44.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>456.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>529.000</td>\n",
       "      <td>25.9</td>\n",
       "      <td>6.16</td>\n",
       "      <td>1.85</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>1.60</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>456.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>900.625</td>\n",
       "      <td>25.5</td>\n",
       "      <td>7.10</td>\n",
       "      <td>1.85</td>\n",
       "      <td>547.358878</td>\n",
       "      <td>11.00</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2938 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adultmortality  Infantdeaths  Alcohol  Percentageexpenditure  Measles  \\\n",
       "0              263.0          55.0     0.01              71.279624  900.625   \n",
       "1              271.0          55.0     0.01              73.523582  492.000   \n",
       "2              268.0          55.0     0.01              73.219243  430.000   \n",
       "3              272.0          55.0     0.01              78.184215  900.625   \n",
       "4              275.0          55.0     0.01               7.097109  900.625   \n",
       "...              ...           ...      ...                    ...      ...   \n",
       "2933           456.5          27.0     4.36               0.000000   31.000   \n",
       "2934           456.5          26.0     4.06               0.000000  900.625   \n",
       "2935            73.0          25.0     4.43               0.000000  304.000   \n",
       "2936           456.5          25.0     1.72               0.000000  529.000   \n",
       "2937           456.5          24.0     1.68               0.000000  900.625   \n",
       "\n",
       "       Bmi  Totalexpenditure  Hiv/aids         Gdp  Thinness1-19years  \\\n",
       "0     19.1              8.16      0.10  584.259210              15.35   \n",
       "1     18.6              8.18      0.10  612.696514              15.35   \n",
       "2     18.1              8.13      0.10  631.744976              15.35   \n",
       "3     17.6              8.52      0.10  669.959000              15.35   \n",
       "4     17.2              7.87      0.10   63.537231              15.35   \n",
       "...    ...               ...       ...         ...                ...   \n",
       "2933  27.1              7.13      1.85  454.366654               9.40   \n",
       "2934  26.7              6.52      1.85  453.351155               9.80   \n",
       "2935  26.3              6.53      1.85   57.348340               1.20   \n",
       "2936  25.9              6.16      1.85  548.587312               1.60   \n",
       "2937  25.5              7.10      1.85  547.358878              11.00   \n",
       "\n",
       "      Lifeexpectancy  \n",
       "0               65.0  \n",
       "1               59.9  \n",
       "2               59.9  \n",
       "3               59.5  \n",
       "4               59.2  \n",
       "...              ...  \n",
       "2933            44.6  \n",
       "2934            44.6  \n",
       "2935            44.8  \n",
       "2936            45.3  \n",
       "2937            46.0  \n",
       "\n",
       "[2938 rows x 11 columns]"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "32b7ea22",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = data['Lifeexpectancy']\n",
    "inputs = data [['Adultmortality', \n",
    "                  'Infantdeaths', \n",
    "                  'Alcohol',\n",
    "                 'Percentageexpenditure',\n",
    "                'Measles',\n",
    "                  'Bmi',\n",
    "                 'Totalexpenditure',\n",
    "                   'Hiv/aids', \n",
    "                  'Gdp',\n",
    "                  'Thinness1-19years']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "e5be8ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(inputs, output, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "0df8e919",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: catboost in c:\\users\\user\\anaconda3\\lib\\site-packages (1.2)\n",
      "Requirement already satisfied: plotly in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (5.9.0)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (1.23.5)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (3.7.0)\n",
      "Requirement already satisfied: pandas>=0.24 in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (1.5.3)\n",
      "Requirement already satisfied: scipy in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (1.10.0)\n",
      "Requirement already satisfied: graphviz in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (0.20.1)\n",
      "Requirement already satisfied: six in c:\\users\\user\\anaconda3\\lib\\site-packages (from catboost) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=0.24->catboost) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from pandas>=0.24->catboost) (2022.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (9.4.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (3.0.9)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (1.0.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from matplotlib->catboost) (22.0)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\user\\anaconda3\\lib\\site-packages (from plotly->catboost) (8.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "0554f525",
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "e380e2cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 8.7117802\ttotal: 6.31ms\tremaining: 624ms\n",
      "10:\tlearn: 4.6907289\ttotal: 50.4ms\tremaining: 407ms\n",
      "20:\tlearn: 3.2656018\ttotal: 93.9ms\tremaining: 353ms\n",
      "30:\tlearn: 2.7794723\ttotal: 139ms\tremaining: 309ms\n",
      "40:\tlearn: 2.5477732\ttotal: 181ms\tremaining: 261ms\n",
      "50:\tlearn: 2.4291873\ttotal: 222ms\tremaining: 214ms\n",
      "60:\tlearn: 2.3105455\ttotal: 276ms\tremaining: 177ms\n",
      "70:\tlearn: 2.2357292\ttotal: 330ms\tremaining: 135ms\n",
      "80:\tlearn: 2.1671396\ttotal: 376ms\tremaining: 88.1ms\n",
      "90:\tlearn: 2.0855133\ttotal: 422ms\tremaining: 41.7ms\n",
      "99:\tlearn: 2.0266865\ttotal: 461ms\tremaining: 0us\n",
      "Mean Squared Error: 4.951845190384272\n"
     ]
    }
   ],
   "source": [
    "catboost_model = CatBoostRegressor(iterations=100, \n",
    "                                   learning_rate=0.1, \n",
    "                                   depth=6,  \n",
    "                                   loss_function='RMSE',  \n",
    "                                   random_seed=42  \n",
    "                                   )\n",
    "catboost_model.fit(X_train, y_train, verbose=10)  \n",
    "\n",
    "y_pred = catboost_model.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "488ec879",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0d158cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dd8a3c65",
   "metadata": {},
   "source": [
    "# XGB model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "f03b7c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "b9144819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 3.849784433265579\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "xgboost_model = xgb.XGBRegressor(n_estimators=100,  \n",
    "                                 learning_rate=0.1, \n",
    "                                 max_depth=6,  \n",
    "                                 objective='reg:squarederror',  \n",
    "                                 random_state=42  \n",
    "                                 )\n",
    "\n",
    "\n",
    "xgboost_model.fit(X_train, y_train, verbose=10)  \n",
    "\n",
    "y_pred = xgboost_model.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0bc79b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd2ab7f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "611d49c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6912e86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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