life expect (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bc7dbfbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0  Afghanistan  2015  Developing              65.0            263.0   \n",
       "1  Afghanistan  2014  Developing              59.9            271.0   \n",
       "2  Afghanistan  2013  Developing              59.9            268.0   \n",
       "3  Afghanistan  2012  Developing              59.5            272.0   \n",
       "4  Afghanistan  2011  Developing              59.2            275.0   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   ...  \\\n",
       "0             62     0.01               71.279624         65.0      1154  ...   \n",
       "1             64     0.01               73.523582         62.0       492  ...   \n",
       "2             66     0.01               73.219243         64.0       430  ...   \n",
       "3             69     0.01               78.184215         67.0      2787  ...   \n",
       "4             71     0.01                7.097109         68.0      3013  ...   \n",
       "\n",
       "   Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  Population  \\\n",
       "0    6.0               8.16         65.0        0.1  584.259210  33736494.0   \n",
       "1   58.0               8.18         62.0        0.1  612.696514    327582.0   \n",
       "2   62.0               8.13         64.0        0.1  631.744976  31731688.0   \n",
       "3   67.0               8.52         67.0        0.1  669.959000   3696958.0   \n",
       "4   68.0               7.87         68.0        0.1   63.537231   2978599.0   \n",
       "\n",
       "    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",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  \n",
       "3                            0.463        9.8  \n",
       "4                            0.454        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "dataset=pd.read_csv('Life.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "303b28e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   infant deaths                    2938 non-null   int64  \n",
      " 6   Alcohol                          2744 non-null   float64\n",
      " 7   percentage expenditure           2938 non-null   float64\n",
      " 8   Hepatitis B                      2385 non-null   float64\n",
      " 9   Measles                          2938 non-null   int64  \n",
      " 10   BMI                             2904 non-null   float64\n",
      " 11  under-five deaths                2938 non-null   int64  \n",
      " 12  Polio                            2919 non-null   float64\n",
      " 13  Total expenditure                2712 non-null   float64\n",
      " 14  Diphtheria                       2919 non-null   float64\n",
      " 15   HIV/AIDS                        2938 non-null   float64\n",
      " 16  GDP                              2490 non-null   float64\n",
      " 17  Population                       2286 non-null   float64\n",
      " 18   thinness  1-19 years            2904 non-null   float64\n",
      " 19   thinness 5-9 years              2904 non-null   float64\n",
      " 20  Income composition of resources  2771 non-null   float64\n",
      " 21  Schooling                        2775 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 505.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "80c5dc6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country                              0\n",
      "Year                                 0\n",
      "Status                               0\n",
      "Life expectancy                     10\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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "128cd614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: Life expectancy , Length: 2938, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dataset.drop(columns=['Life expectancy '],axis=1)\n",
    "y = dataset['Life expectancy ']\n",
    "X\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "258917cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[65. ],\n",
       "       [59.9],\n",
       "       [59.9],\n",
       "       ...,\n",
       "       [44.8],\n",
       "       [45.3],\n",
       "       [46. ]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=dataset.loc[:,dataset.columns != \"Life expectancy \"].values\n",
    "y=dataset.loc[:,dataset.columns == \"Life expectancy \"].values\n",
    "X\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9a9cc185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "duplicate_rows = dataset.duplicated()\n",
    "total_duplicates = duplicate_rows.sum()\n",
    "print(\"Total duplicate rows:\", total_duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d49b8e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1649"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "eab9a528",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country 193\n",
      "Year 16\n",
      "Status 2\n",
      "Life expectancy  362\n",
      "Adult Mortality 425\n",
      "infant deaths 209\n",
      "Alcohol 1076\n",
      "percentage expenditure 2328\n",
      "Hepatitis B 87\n",
      "Measles  958\n",
      " BMI  608\n",
      "under-five deaths  252\n",
      "Polio 73\n",
      "Total expenditure 818\n",
      "Diphtheria  81\n",
      " HIV/AIDS 200\n",
      "GDP 2490\n",
      "Population 2278\n",
      " thinness  1-19 years 200\n",
      " thinness 5-9 years 207\n",
      "Income composition of resources 625\n",
      "Schooling 173\n"
     ]
    }
   ],
   "source": [
    "for column in dataset.columns:\n",
    "    unique_values = dataset[column].nunique()\n",
    "    print(column,unique_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "68ca1026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#To show rows with missing adult mortality\n",
    "rows_with_missing_adult_mortality = dataset_imputed[dataset_imputed['Adult Mortality'].isna() | (dataset_imputed['Adult Mortality'] == \"\")]\n",
    "#Solving the problem of rows with missing adult mortality values noting that I have deleted these rows later because they only have one row of Year 2013\n",
    "print(rows_with_missing_adult_mortality)\n",
    "dataset.loc[dataset['Country'] == 'Cook Islands', 'Adult Mortality'] = 537\n",
    "print(dataset[dataset['Country'] == 'Cook Islands'])\n",
    "dataset.loc[dataset['Country'] == 'Dominica', 'Adult Mortality'] = 120\n",
    "print(dataset[dataset['Country'] == 'Dominica'])\n",
    "dataset.loc[dataset['Country'] == 'Marshall Islands', 'Adult Mortality'] = 259\n",
    "print(dataset[dataset['Country'] == 'Marshall Islands'])\n",
    "dataset.loc[dataset['Country'] == 'Monaco', 'Adult Mortality'] = 19\n",
    "print(dataset[dataset['Country'] == 'Monaco'])\n",
    "dataset.loc[dataset['Country'] == 'Nauru', 'Adult Mortality'] = 438\n",
    "print(dataset[dataset['Country'] == 'Nauru'])\n",
    "dataset.loc[dataset['Country'] == 'Niue', 'Adult Mortality'] = 173\n",
    "print(dataset[dataset['Country'] == 'Niue'])\n",
    "dataset.loc[dataset['Country'] == 'Palau', 'Adult Mortality'] = 10\n",
    "print(dataset[dataset['Country'] == 'Palau'])\n",
    "dataset.loc[dataset['Country'] == 'Saint Kitts and Nevis', 'Adult Mortality'] = 231\n",
    "print(dataset[dataset['Country'] == 'Saint Kitts and Nevis'])\n",
    "dataset.loc[dataset['Country'] == 'San Marino', 'Adult Mortality'] = 8\n",
    "print(dataset[dataset['Country'] == 'San Marino'])\n",
    "dataset.loc[dataset['Country'] == 'Tuvalu', 'Adult Mortality'] = 375\n",
    "print(dataset[dataset['Country'] == 'Tuvalu'])\n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "8cb28e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     10\n",
       "Adult Mortality                      0\n",
       "infant deaths                        0\n",
       "Alcohol                            177\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",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solving problem of missing values in Alcohol column\n",
    "non_val = dataset.groupby(\"Country\")[\"Alcohol\"].mean()\n",
    "dataset[\"Alcohol\"].fillna(non_val, inplace=True)\n",
    "dataset.loc[dataset[\"Alcohol\"].isna()]\n",
    "dataset.loc[dataset[\"Country\"] == \"South Sudan\",\"Alcohol\"] = 0.0\n",
    "dataset.loc[dataset[\"Country\"] == \"Palau\",\"Alcohol\"] = 0.0\n",
    "dataset.loc[dataset[\"Country\"] == \"South Sudan\"]\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "da17a42f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
      "44    Algeria  2003  Developing              71.7            146.0   \n",
      "45    Algeria  2002  Developing              71.6            145.0   \n",
      "46    Algeria  2001  Developing              71.4            145.0   \n",
      "47    Algeria  2000  Developing              71.3            145.0   \n",
      "57     Angola  2006  Developing              47.7            381.0   \n",
      "...       ...   ...         ...               ...              ...   \n",
      "2917   Zambia  2004  Developing              47.9            578.0   \n",
      "2918   Zambia  2003  Developing              46.4             64.0   \n",
      "2919   Zambia  2002  Developing              45.5             69.0   \n",
      "2920   Zambia  2001  Developing              44.6            611.0   \n",
      "2921   Zambia  2000  Developing              43.8            614.0   \n",
      "\n",
      "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
      "44               20     0.34               25.018523          NaN     15374   \n",
      "45               20     0.36              148.511984          NaN      5862   \n",
      "46               20     0.23              147.986071          NaN      2686   \n",
      "47               21     0.25              154.455944          NaN         0   \n",
      "57               90     5.84               25.086888          NaN       765   \n",
      "...             ...      ...                     ...          ...       ...   \n",
      "2917             36     2.46                8.369852          NaN        35   \n",
      "2918             39     2.33               65.789974          NaN       881   \n",
      "2919             41     2.44               54.043480          NaN     25036   \n",
      "2920             43     2.61               46.830275          NaN     16997   \n",
      "2921             44     2.62               45.616880          NaN     30930   \n",
      "\n",
      "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS          GDP  \\\n",
      "44    ...   87.0               3.60         87.0        0.1   294.335560   \n",
      "45    ...   86.0               3.73         86.0        0.1  1774.336730   \n",
      "46    ...   89.0               3.84         89.0        0.1  1732.857979   \n",
      "47    ...   86.0               3.49         86.0        0.1  1757.177970   \n",
      "57    ...   36.0               4.54         34.0        2.5   262.415149   \n",
      "...   ...    ...                ...          ...        ...          ...   \n",
      "2917  ...   84.0               7.33         83.0       17.6    53.277222   \n",
      "2918  ...   85.0               8.18         83.0       18.2   429.158343   \n",
      "2919  ...   85.0               6.93         84.0       18.4   377.135244   \n",
      "2920  ...   86.0               6.56         85.0       18.6   378.273624   \n",
      "2921  ...   85.0               7.16         85.0       18.7   341.955625   \n",
      "\n",
      "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
      "44     3243514.0                    6.3                  6.1   \n",
      "45     3199546.0                    6.3                  6.2   \n",
      "46    31592153.0                    6.4                  6.3   \n",
      "47     3118366.0                    6.5                  6.4   \n",
      "57     2262399.0                    9.8                  9.7   \n",
      "...          ...                    ...                  ...   \n",
      "2917  11731746.0                    7.2                  7.1   \n",
      "2918  11421984.0                    7.3                  7.2   \n",
      "2919    111249.0                    7.4                  7.3   \n",
      "2920   1824125.0                    7.4                  7.4   \n",
      "2921   1531221.0                    7.5                  7.5   \n",
      "\n",
      "      Income composition of resources  Schooling  \n",
      "44                              0.663       11.5  \n",
      "45                              0.653       11.1  \n",
      "46                              0.644       10.9  \n",
      "47                              0.636       10.7  \n",
      "57                              0.439        7.2  \n",
      "...                               ...        ...  \n",
      "2917                            0.456       10.5  \n",
      "2918                            0.443       10.2  \n",
      "2919                            0.433       10.0  \n",
      "2920                            0.424        9.8  \n",
      "2921                            0.418        9.6  \n",
      "\n",
      "[553 rows x 22 columns]\n",
      "Rows with NaN values after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Hepatitis B values for Denmark after filling:\n",
      "737    0.0\n",
      "738    0.0\n",
      "739    0.0\n",
      "740    0.0\n",
      "741    0.0\n",
      "742    0.0\n",
      "743    0.0\n",
      "744    0.0\n",
      "745    0.0\n",
      "746    0.0\n",
      "747    0.0\n",
      "748    0.0\n",
      "749    0.0\n",
      "750    0.0\n",
      "751    0.0\n",
      "752    0.0\n",
      "Name: Hepatitis B, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Solving problem of missing values in Hepatitis B column\n",
    "rows_with_missing_hepatitis_b = dataset_imputed[dataset_imputed['Hepatitis B'].isna() | (dataset_imputed['Hepatitis B'] == \"\")]\n",
    "print(rows_with_missing_hepatitis_b)\n",
    "\n",
    "# Calculate mean Hepatitis B for each country\n",
    "mean_hepatitis_b = dataset.groupby('Country')['Hepatitis B'].transform('mean')\n",
    "\n",
    "# Fill missing values of Hepatitis B with country-specific means\n",
    "dataset['Hepatitis B'].fillna(mean_hepatitis_b, inplace=True)\n",
    "\n",
    "# Check for rows where NaN values still persist after imputation\n",
    "rows_with_nan = dataset[dataset['Hepatitis B'].isnull()]\n",
    "\n",
    "# Print rows with NaN values after imputation\n",
    "print(\"Rows with NaN values after imputation:\")\n",
    "print(rows_with_nan)\n",
    "dataset.isna().sum()\n",
    "# Fill the values of \"Hepatitis B\" for \"Denmark\" with 0\n",
    "dataset.loc[dataset['Country'] == 'Denmark', 'Hepatitis B'] = 0\n",
    "\n",
    "# Verify that the values were filled correctly\n",
    "denmark_hepatitis_b = dataset.loc[dataset['Country'] == 'Denmark', 'Hepatitis B']\n",
    "print(\"Hepatitis B values for Denmark after filling:\")\n",
    "print(denmark_hepatitis_b)\n",
    "\n",
    "\n",
    "dataset.loc[dataset['Country'] == 'Finland', 'Hepatitis B'] = 0\n",
    "dataset.loc[dataset['Country'] == 'Hungary', 'Hepatitis B'] = 0\n",
    "dataset.loc[dataset['Country'] == 'Iceland', 'Hepatitis B'] = 0\n",
    "dataset.loc[dataset['Country'] == 'Japan', 'Hepatitis B'] = 94\n",
    "dataset.loc[dataset['Country'] == 'Norway', 'Hepatitis B'] = 0\n",
    "dataset.loc[dataset['Country'] == 'Slovenia', 'Hepatitis B'] = 0\n",
    "dataset.loc[dataset['Country'] == 'Switzerland', 'Hepatitis B'] = 72\n",
    "dataset.loc[dataset['Country'] == 'United Kingdom of Great Britain and Northern Ireland', 'Hepatitis B'] = 85\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "878ca0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solving the problem of missing values in BMI column\n",
    "# Calculate mean bmi for each country\n",
    "mean_bmi = dataset.groupby('Country')[' BMI '].transform('mean')\n",
    "dataset[' BMI '].fillna(mean_bmi, inplace=True)\n",
    "mean_bmi_switzerland = dataset[dataset['Country'] == 'Switzerland'][' BMI '].mean()\n",
    "dataset.loc[dataset['Country'] == 'Monaco', ' BMI '] = mean_bmi_switzerland\n",
    "dataset.loc[dataset['Country'].isin(['Sudan', 'South Sudan']), ' BMI '] = 25.2\n",
    "mean_bmi_italy = dataset[dataset['Country'] == 'Italy'][' BMI '].mean()\n",
    "dataset.loc[dataset['Country'] == 'San Marino', ' BMI '] = mean_bmi_italy\n",
    "rows_with_nan8 = dataset[dataset[' BMI '].isnull()]\n",
    "rows_with_nan8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "0ff9f2f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with NaN values after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n",
      "Rows with NaN values in the 'Polio' column:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "#Solving problem of missing values in Polio column\n",
    "polio= dataset.groupby('Country')['Polio'].transform('mean')\n",
    "dataset['Polio'].fillna(polio, inplace=True)\n",
    "rows_with_nan9 = dataset[dataset['Polio'].isnull()]\n",
    "# Print rows with NaN values after imputation\n",
    "print(\"Rows with NaN values after imputation:\")\n",
    "print(rows_with_nan9)\n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)\n",
    "# Check for rows where NaN values exist in the Polio column\n",
    "rows_with_nan = dataset[dataset['Polio'].isnull()]\n",
    "# Filter rows where 'Polio' column contains NaN values\n",
    "rows_with_nan_polio = dataset[dataset['Polio'].isnull()]\n",
    "\n",
    "# Print rows with NaN values in the 'Polio' column\n",
    "print(\"Rows with NaN values in the 'Polio' column:\")\n",
    "print(rows_with_nan_polio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "5c333db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with NaN values after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean Total expenditure for each country\n",
    "mean_Total_expenditure = dataset.groupby('Country')['Total expenditure'].transform('mean')\n",
    "dataset['Total expenditure'].fillna(mean_Total_expenditure, inplace=True)\n",
    "dataset.loc[dataset['Country'] == 'Somalia', 'Total expenditure'] = 2\n",
    "dataset.loc[dataset['Country'] == \"Democratic People's Republic of Korea\", 'Total expenditure'] = 3\n",
    "rows_with_nan10 = dataset[dataset['Total expenditure'].isnull()]\n",
    "# Print rows with NaN values after imputation\n",
    "print(\"Rows with NaN values after imputation:\")\n",
    "print(rows_with_nan10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "55085ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mean Diphtheria for each country\n",
    "mean_Diphtheria = dataset.groupby('Country')['Diphtheria '].transform('mean')\n",
    "dataset['Diphtheria '].fillna(mean_Diphtheria, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "796c21a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.3</td>\n",
       "      <td>723.0</td>\n",
       "      <td>27</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.0</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>454.366654</td>\n",
       "      <td>12777511.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.407</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.5</td>\n",
       "      <td>715.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>998</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.52</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>453.351155</td>\n",
       "      <td>12633897.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0.418</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>44.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>25</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.0</td>\n",
       "      <td>304</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>6.53</td>\n",
       "      <td>71.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>57.348340</td>\n",
       "      <td>125525.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.427</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>45.3</td>\n",
       "      <td>686.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.0</td>\n",
       "      <td>529</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>75.0</td>\n",
       "      <td>42.1</td>\n",
       "      <td>548.587312</td>\n",
       "      <td>12366165.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.427</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>46.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>24</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1483</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>7.10</td>\n",
       "      <td>78.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>547.358878</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2928 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
       "0     Afghanistan  2015  Developing              65.0            263.0   \n",
       "1     Afghanistan  2014  Developing              59.9            271.0   \n",
       "2     Afghanistan  2013  Developing              59.9            268.0   \n",
       "3     Afghanistan  2012  Developing              59.5            272.0   \n",
       "4     Afghanistan  2011  Developing              59.2            275.0   \n",
       "...           ...   ...         ...               ...              ...   \n",
       "2933     Zimbabwe  2004  Developing              44.3            723.0   \n",
       "2934     Zimbabwe  2003  Developing              44.5            715.0   \n",
       "2935     Zimbabwe  2002  Developing              44.8             73.0   \n",
       "2936     Zimbabwe  2001  Developing              45.3            686.0   \n",
       "2937     Zimbabwe  2000  Developing              46.0            665.0   \n",
       "\n",
       "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
       "0                62     0.01               71.279624         65.0      1154   \n",
       "1                64     0.01               73.523582         62.0       492   \n",
       "2                66     0.01               73.219243         64.0       430   \n",
       "3                69     0.01               78.184215         67.0      2787   \n",
       "4                71     0.01                7.097109         68.0      3013   \n",
       "...             ...      ...                     ...          ...       ...   \n",
       "2933             27     4.36                0.000000         68.0        31   \n",
       "2934             26     4.06                0.000000          7.0       998   \n",
       "2935             25     4.43                0.000000         73.0       304   \n",
       "2936             25     1.72                0.000000         76.0       529   \n",
       "2937             24     1.68                0.000000         79.0      1483   \n",
       "\n",
       "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS         GDP  \\\n",
       "0     ...    6.0               8.16         65.0        0.1  584.259210   \n",
       "1     ...   58.0               8.18         62.0        0.1  612.696514   \n",
       "2     ...   62.0               8.13         64.0        0.1  631.744976   \n",
       "3     ...   67.0               8.52         67.0        0.1  669.959000   \n",
       "4     ...   68.0               7.87         68.0        0.1   63.537231   \n",
       "...   ...    ...                ...          ...        ...         ...   \n",
       "2933  ...   67.0               7.13         65.0       33.6  454.366654   \n",
       "2934  ...    7.0               6.52         68.0       36.7  453.351155   \n",
       "2935  ...   73.0               6.53         71.0       39.8   57.348340   \n",
       "2936  ...   76.0               6.16         75.0       42.1  548.587312   \n",
       "2937  ...   78.0               7.10         78.0       43.5  547.358878   \n",
       "\n",
       "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
       "0     33736494.0                   17.2                 17.3   \n",
       "1       327582.0                   17.5                 17.5   \n",
       "2     31731688.0                   17.7                 17.7   \n",
       "3      3696958.0                   17.9                 18.0   \n",
       "4      2978599.0                   18.2                 18.2   \n",
       "...          ...                    ...                  ...   \n",
       "2933  12777511.0                    9.4                  9.4   \n",
       "2934  12633897.0                    9.8                  9.9   \n",
       "2935    125525.0                    1.2                  1.3   \n",
       "2936  12366165.0                    1.6                  1.7   \n",
       "2937  12222251.0                   11.0                 11.2   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "0                               0.479       10.1  \n",
       "1                               0.476       10.0  \n",
       "2                               0.470        9.9  \n",
       "3                               0.463        9.8  \n",
       "4                               0.454        9.5  \n",
       "...                               ...        ...  \n",
       "2933                            0.407        9.2  \n",
       "2934                            0.418        9.5  \n",
       "2935                            0.427       10.0  \n",
       "2936                            0.427        9.8  \n",
       "2937                            0.434        9.8  \n",
       "\n",
       "[2928 rows x 22 columns]"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing the rows of countries which only have one year row \n",
    "countries_to_drop = [\"Cook Islands\", \"Dominica\", \"Marshall Islands\", \"Monaco\", \"Nauru\", \"Niue\", \"Palau\", \"Saint Kitts and Nevis\", \"San Marino\", \"Tuvalu\"]\n",
    "\n",
    "# Drop rows where the 'Country' column matches any of the countries in the list\n",
    "dataset = dataset[~dataset['Country'].isin(countries_to_drop)]\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "84a04855",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset after assigning GDP values for Bolivia between 2000 and 2015:\n",
      "                              Country  Year      Status  Life expectancy   \\\n",
      "304  Bolivia (Plurinational State of)  2015  Developing              77.0   \n",
      "305  Bolivia (Plurinational State of)  2014  Developing              74.0   \n",
      "306  Bolivia (Plurinational State of)  2013  Developing              71.0   \n",
      "307  Bolivia (Plurinational State of)  2012  Developing              69.8   \n",
      "308  Bolivia (Plurinational State of)  2011  Developing              69.3   \n",
      "309  Bolivia (Plurinational State of)  2010  Developing              68.7   \n",
      "310  Bolivia (Plurinational State of)  2009  Developing              68.0   \n",
      "311  Bolivia (Plurinational State of)  2008  Developing              67.4   \n",
      "312  Bolivia (Plurinational State of)  2007  Developing              66.8   \n",
      "313  Bolivia (Plurinational State of)  2006  Developing              66.2   \n",
      "314  Bolivia (Plurinational State of)  2005  Developing              65.7   \n",
      "315  Bolivia (Plurinational State of)  2004  Developing              65.1   \n",
      "316  Bolivia (Plurinational State of)  2003  Developing              64.5   \n",
      "317  Bolivia (Plurinational State of)  2002  Developing              63.9   \n",
      "318  Bolivia (Plurinational State of)  2001  Developing              63.3   \n",
      "319  Bolivia (Plurinational State of)  2000  Developing              62.6   \n",
      "\n",
      "     Adult Mortality  infant deaths  Alcohol  percentage expenditure  \\\n",
      "304            186.0              8    3.212                     0.0   \n",
      "305            189.0              8    3.620                     0.0   \n",
      "306            192.0              8    3.780                     0.0   \n",
      "307            194.0              8    3.890                     0.0   \n",
      "308            198.0              9    3.930                     0.0   \n",
      "309             22.0              9    3.950                     0.0   \n",
      "310             27.0             10    3.870                     0.0   \n",
      "311            211.0             10    3.770                     0.0   \n",
      "312            216.0             11    3.470                     0.0   \n",
      "313            219.0             11    3.230                     0.0   \n",
      "314            222.0             12    2.860                     0.0   \n",
      "315            226.0             12    2.660                     0.0   \n",
      "316             23.0             13    2.370                     0.0   \n",
      "317            234.0             14    2.260                     0.0   \n",
      "318            238.0             14    2.200                     0.0   \n",
      "319            243.0             15    2.320                     0.0   \n",
      "\n",
      "     Hepatitis B  Measles   ...  Polio  Total expenditure  Diphtheria   \\\n",
      "304         99.0         0  ...   99.0              5.626         99.0   \n",
      "305         98.0         0  ...   99.0              6.330         98.0   \n",
      "306         94.0         0  ...   95.0              5.960         94.0   \n",
      "307         93.0         0  ...   93.0              5.560         93.0   \n",
      "308         95.0         0  ...   95.0              5.490         95.0   \n",
      "309         91.0         0  ...    9.0              5.440         91.0   \n",
      "310         93.0         0  ...   92.0              5.760         93.0   \n",
      "311         88.0         0  ...   87.0              5.000         88.0   \n",
      "312         84.0         0  ...   85.0              4.960         84.0   \n",
      "313         83.0         0  ...   82.0              5.190         83.0   \n",
      "314         85.0         0  ...   84.0              5.670         85.0   \n",
      "315         84.0         0  ...   84.0              5.220         84.0   \n",
      "316          8.0         0  ...   82.0              5.300         88.0   \n",
      "317         77.0         0  ...   76.0              6.530         77.0   \n",
      "318         77.0         0  ...   71.0              6.310         71.0   \n",
      "319         77.0       122  ...   74.0              5.670         75.0   \n",
      "\n",
      "      HIV/AIDS         GDP   Population   thinness  1-19 years  \\\n",
      "304        0.1  33200000.0  11090951.00                    1.2   \n",
      "305        0.1  33200000.0   9911048.25                    1.2   \n",
      "306        0.1  30900000.0   9911048.25                    1.2   \n",
      "307        0.1  27300000.0   9911048.25                    1.2   \n",
      "308        0.2  24100000.0   9911048.25                    1.2   \n",
      "309        0.2  19800000.0  10223231.00                    1.2   \n",
      "310        0.2  17500000.0   9911048.25                    1.2   \n",
      "311        0.2  16800000.0   9911048.25                    1.3   \n",
      "312        0.2  13200000.0   9911048.25                    1.3   \n",
      "313        0.2  11500000.0   9911048.25                    1.3   \n",
      "314        0.2   9600000.0   9377388.00                    1.3   \n",
      "315        0.2   8800000.0   9911048.25                    1.4   \n",
      "316        0.2   8100000.0   9911048.25                    1.4   \n",
      "317        0.1   7900000.0   9911048.25                    1.4   \n",
      "318        0.1   8200000.0   9911048.25                    1.5   \n",
      "319        0.1   8400000.0   8952623.00                    1.5   \n",
      "\n",
      "      thinness 5-9 years  Income composition of resources  Schooling  \n",
      "304                  1.1                            0.671       13.8  \n",
      "305                  1.1                            0.666       13.8  \n",
      "306                  1.1                            0.661       13.8  \n",
      "307                  1.1                            0.655       13.8  \n",
      "308                  1.1                            0.649       13.8  \n",
      "309                  1.1                            0.643       13.8  \n",
      "310                  1.1                            0.636       13.8  \n",
      "311                  1.1                            0.632       14.0  \n",
      "312                  1.2                            0.626       14.1  \n",
      "313                  1.2                            0.625       14.3  \n",
      "314                  1.2                            0.622       14.4  \n",
      "315                  1.2                            0.620       14.6  \n",
      "316                  1.3                            0.617       14.4  \n",
      "317                  1.3                            0.610       14.0  \n",
      "318                  1.4                            0.607       13.7  \n",
      "319                  1.4                            0.600       13.3  \n",
      "\n",
      "[16 rows x 22 columns]\n",
      "Rows with NaN values after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean GDP for each country\n",
    "mean_GDP = dataset.groupby('Country')['GDP'].transform('mean')\n",
    "dataset['GDP'].fillna(mean_GDP, inplace=True)\n",
    "dataset.loc[dataset['Country'] == 'Bahamas', 'GDP'] = 10000000.00\n",
    "bolivia_missing_gdp = dataset[(dataset['Country'] == 'Bolivia (Plurinational State of)') & (dataset['Year'] >= 2000) & (dataset['Year'] <= 2015) & (dataset['GDP'].isnull())]\n",
    "multiplied_values = [8.4, 8.2, 7.9, 8.1, 8.8, 9.6, 11.5, 13.2, 16.8, 17.5, 19.8, 24.1, 27.3, 30.9, 33.2, 33.2]\n",
    "multiplied_values = [x * 1000000 for x in multiplied_values]\n",
    "bolivia_missing_gdp.loc[:, 'GDP'] = multiplied_values\n",
    "\n",
    "multiplied_gdp_values = [\n",
    "    8400000.0, 8200000.0, 7900000.0, 8100000.0, 8800000.0, 9600000.0,\n",
    "    11500000.0, 13200000.0, 16800000.0, 17500000.0, 19800000.0,\n",
    "    24100000.0, 27300000.0, 30900000.0, 33200000.0, 33200000.0\n",
    "]\n",
    "years = range(2000, 2016)\n",
    "gdp_dict = dict(zip(years, multiplied_gdp_values))\n",
    "for year, value in gdp_dict.items():\n",
    "    dataset.loc[(dataset['Country'] == 'Bolivia (Plurinational State of)') & (dataset['Year'] == year), 'GDP'] = value\n",
    "print(\"Dataset after assigning GDP values for Bolivia between 2000 and 2015:\")\n",
    "print(dataset[(dataset['Country'] == 'Bolivia (Plurinational State of)') & (dataset['Year'].between(2000, 2015))])\n",
    "\n",
    "\n",
    "cote_divoire_gdp_values = {\n",
    "    2000: 14900000,\n",
    "    2005: 23600000,\n",
    "    2006: 24600000,\n",
    "    2007: 28200000,\n",
    "    2008: 33600000,\n",
    "    2009: 33700000,\n",
    "    2010: 34400000,\n",
    "    2011: 35500000,\n",
    "    2012: 37000000,\n",
    "    2013: 43200000,\n",
    "    2014: 48900000,\n",
    "    2015: 45800000\n",
    "}\n",
    "for year, gdp_value in cote_divoire_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Côte d'Ivoire\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "mean_gdp_cote_divoire = dataset.loc[dataset['Country'] == \"Côte d'Ivoire\", 'GDP'].mean()\n",
    "dataset.loc[(dataset['Country'] == \"Côte d'Ivoire\") & (dataset['GDP'].isnull()), 'GDP'] = mean_gdp_cote_divoire\n",
    "\n",
    "\n",
    "\n",
    "congo_gdp_values = {\n",
    "    2000: 3620000,\n",
    "    2005: 6650000,\n",
    "    2006: 8070000,\n",
    "    2007: 8780000,\n",
    "    2008: 11650000,\n",
    "    2009: 9710000,\n",
    "    2010: 13160000,\n",
    "    2011: 15650000,\n",
    "    2012: 17700000,\n",
    "    2013: 17960000,\n",
    "    2014: 17920000,\n",
    "    2015: 11890000\n",
    "}\n",
    "for year, gdp_value in congo_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Congo\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "mean_gdp_congo = dataset.loc[dataset['Country'] == \"Congo\", 'GDP'].mean()\n",
    "dataset.loc[(dataset['Country'] == \"Congo\") & (dataset['GDP'].isnull()), 'GDP'] = mean_gdp_congo\n",
    "dataset.loc[dataset['Country'] == \"Cook Islands\", 'GDP'] = 183000000\n",
    "dataset.loc[dataset['Country'] == \"Czechia\", 'GDP'] = 209000000000\n",
    "dprk_gdp_values = {\n",
    "    2000: 576179387819.6,\n",
    "    2013: 1370632955321.2,\n",
    "    2014: 1484488526271.8,\n",
    "    2015: 1466038936206.4\n",
    "}\n",
    "for year, gdp_value in dprk_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Democratic People's Republic of Korea\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "mean_gdp_dprk = dataset.loc[dataset['Country'] == \"Democratic People's Republic of Korea\", 'GDP'].mean()\n",
    "dataset.loc[(dataset['Country'] == \"Democratic People's Republic of Korea\") & (dataset['GDP'].isnull()), 'GDP'] = mean_gdp_dprk\n",
    "\n",
    "drc_gdp_values = {\n",
    "    2000: 3227927698,\n",
    "    2001: 2796704604,\n",
    "    2002: 3034250924,\n",
    "    2003: 3503723088,\n",
    "    2004: 4656974940,\n",
    "    2005: 6650001680,\n",
    "    2006: 8072305029,\n",
    "    2007: 8782703437,\n",
    "    2008: 11649857673,\n",
    "    2009: 9723299915,\n",
    "    2010: 13148396212,\n",
    "    2011: 15655383577,\n",
    "    2012: 17692911296,\n",
    "    2013: 17958720699,\n",
    "    2014: 17919224180,\n",
    "    2015: 12434829581\n",
    "}\n",
    "\n",
    "for year, gdp_value in drc_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Democratic Republic of the Congo\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "mean_gdp_drc = dataset.loc[dataset['Country'] == \"Democratic Republic of the Congo\", 'GDP'].mean()\n",
    "dataset.loc[(dataset['Country'] == \"Democratic Republic of the Congo\") & (dataset['GDP'].isnull()), 'GDP'] = mean_gdp_drc\n",
    "\n",
    "egypt_gdp_values = {\n",
    "    2000: 99838543960,\n",
    "    2001: 96684636119,\n",
    "    2002: 85146067416,\n",
    "    2003: 80288461538,\n",
    "    2004: 78782467532,\n",
    "    2005: 89600665557,\n",
    "    2006: 107426000000,\n",
    "    2007: 130438000000,\n",
    "    2008: 162818000000,\n",
    "    2009: 189147000000,\n",
    "    2010: 218984000000,\n",
    "    2011: 235990000000,\n",
    "    2012: 279117000000,\n",
    "    2013: 288434000000,\n",
    "    2014: 305595000000,\n",
    "    2015: 329367000000\n",
    "}\n",
    "for year, gdp_value in egypt_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Egypt\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "mean_gdp_egypt = dataset.loc[dataset['Country'] == \"Egypt\", 'GDP'].mean()\n",
    "dataset.loc[(dataset['Country'] == \"Egypt\") & (dataset['GDP'].isnull()), 'GDP'] = mean_gdp_egypt\n",
    "\n",
    "gambia_gdp_values = {\n",
    "    2000: 782913871.8,\n",
    "    2001: 687410645.1,\n",
    "    2002: 578235309.3,\n",
    "    2003: 487038685,\n",
    "    2004: 961900651.4,\n",
    "    2005: 1027701068,\n",
    "    2006: 1054112488,\n",
    "    2007: 1279703047,\n",
    "    2008: 1561766956,\n",
    "    2009: 1450142509,\n",
    "    2010: 1543294927,\n",
    "    2011: 1409693597,\n",
    "    2012: 1415004738,\n",
    "    2013: 1375609453,\n",
    "    2014: 1229461721,\n",
    "    2015: 1378176609\n",
    "}\n",
    "\n",
    "for year, gdp_value in gambia_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Gambia\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "iran_gdp_values = {\n",
    "    2000: 109592000000,\n",
    "    2001: 126879000000,\n",
    "    2002: 128627000000,\n",
    "    2003: 153545000000,\n",
    "    2004: 190043000000,\n",
    "    2005: 226452000000,\n",
    "    2006: 266299000000,\n",
    "    2007: 349882000000,\n",
    "    2008: 412336000000,\n",
    "    2009: 416397000000,\n",
    "    2010: 486808000000,\n",
    "    2011: 626133000000,\n",
    "    2012: 644036000000,\n",
    "    2013: 492776000000,\n",
    "    2014: 460383000000,\n",
    "    2015: 408213000000\n",
    "}\n",
    "\n",
    "for year, gdp_value in iran_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Iran (Islamic Republic of)\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "kekestan_gdp_values = {\n",
    "    2000: 18291990662,\n",
    "    2001: 22152689180,\n",
    "    2002: 24636598527,\n",
    "    2003: 30833692900,\n",
    "    2004: 43151647003,\n",
    "    2005: 57123671734,\n",
    "    2006: 81003864630,\n",
    "    2007: 104850000000,\n",
    "    2008: 133442000000,\n",
    "    2009: 115309000000,\n",
    "    2010: 148047000000,\n",
    "    2011: 192626000000,\n",
    "    2012: 207999000000,\n",
    "    2013: 236635000000,\n",
    "    2014: 221416000000,\n",
    "    2015: 184388000000\n",
    "}\n",
    "\n",
    "for year, gdp_value in kekestan_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Kyrgyzstan\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "lao_gdp_values = {\n",
    "    2000: 1731198022,\n",
    "    2001: 1768619058,\n",
    "    2002: 1758176653,\n",
    "    2003: 2023324407,\n",
    "    2004: 2366398120,\n",
    "    2005: 2735558735,\n",
    "    2006: 3455031448,\n",
    "    2007: 4223152219,\n",
    "    2008: 5446434032,\n",
    "    2009: 5836138127,\n",
    "    2010: 7131773633,\n",
    "    2011: 8750107402,\n",
    "    2012: 10192848926,\n",
    "    2013: 11983252627,\n",
    "    2014: 13279248479,\n",
    "    2015: 14426381187\n",
    "}\n",
    "\n",
    "for year, gdp_value in lao_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Lao People's Democratic Republic\") & (dataset['Year'] == year), 'GDP'] = gdp_value    \n",
    "    \n",
    "\n",
    "micronesia_gdp_values = {\n",
    "    2000: 100000000,\n",
    "    2001: 110000000,\n",
    "    2002: 120000000,\n",
    "    2003: 130000000,\n",
    "    2004: 140000000,\n",
    "    2005: 150000000,\n",
    "    2006: 160000000,\n",
    "    2007: 170000000,\n",
    "    2008: 180000000,\n",
    "    2009: 190000000,\n",
    "    2010: 200000000,\n",
    "    2011: 210000000,\n",
    "    2012: 220000000,\n",
    "    2013: 230000000,\n",
    "    2014: 240000000,\n",
    "    2015: 250000000\n",
    "}\n",
    "\n",
    "for year, gdp_value in micronesia_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Micronesia (Federated States of)\") & (dataset['Year'] == year), 'GDP'] = gdp_value    \n",
    "\n",
    "korea_gdp_values = {\n",
    "    2000: int(5.76179E+11),\n",
    "    2001: int(5.47656E+11),\n",
    "    2002: int(6.27247E+11),\n",
    "    2003: int(7.02715E+11),\n",
    "    2004: int(7.93176E+11),\n",
    "    2005: int(9.34901E+11),\n",
    "    2006: int(1.05322E+12),\n",
    "    2007: int(1.17261E+12),\n",
    "    2008: int(1.04734E+12),\n",
    "    2009: int(9.43942E+11),\n",
    "    2010: int(1.14367E+12),\n",
    "    2011: int(1.25329E+12),\n",
    "    2012: int(1.27805E+12),\n",
    "    2013: int(1.37063E+12),\n",
    "    2014: int(1.48449E+12),\n",
    "    2015: int(1.46604E+12)\n",
    "}\n",
    "\n",
    "for year, gdp_value in korea_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Republic of Korea\") & (dataset['Year'] == year), 'GDP'] = gdp_value \n",
    "\n",
    "yemen_gdp_values = {\n",
    "    2000: 9679316770,\n",
    "    2001: 9852990693,\n",
    "    2002: 10693430511,\n",
    "    2003: 11777532683,\n",
    "    2004: 13867634396,\n",
    "    2005: 16731566688,\n",
    "    2006: 19063143370,\n",
    "    2007: 21650528638,\n",
    "    2008: 26910855852,\n",
    "    2009: 25130278254,\n",
    "    2010: 30906749533,\n",
    "    2011: 32726417878,\n",
    "    2012: 35401331609,\n",
    "    2013: 40415233436,\n",
    "    2014: 43228585321,\n",
    "    2015: 42444489461\n",
    "}\n",
    "\n",
    "for year, gdp_value in yemen_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Yemen\") & (dataset['Year'] == year), 'GDP'] = gdp_value    \n",
    "\n",
    "moldova_gdp_values = {\n",
    "    2000: 1288429392,\n",
    "    2001: 1480673594,\n",
    "    2002: 1661818168,\n",
    "    2003: 1980907435,\n",
    "    2004: 2598249556,\n",
    "    2005: 2988342907,\n",
    "    2006: 3408255451,\n",
    "    2007: 4401173152,\n",
    "    2008: 6054824248,\n",
    "    2009: 5439434272,\n",
    "    2010: 6974982370,\n",
    "    2011: 8414352020,\n",
    "    2012: 8709138635,\n",
    "    2013: 9496717876,\n",
    "    2014: 9399833207,\n",
    "    2015: 7798692878\n",
    "}\n",
    "\n",
    "for year, gdp_value in moldova_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Republic of Moldova\") & (dataset['Year'] == year), 'GDP'] = gdp_value   \n",
    "    \n",
    "vietnam_gdp_values = {\n",
    "    2000: 31172518403,\n",
    "    2001: 32685198809,\n",
    "    2002: 35064105501,\n",
    "    2003: 39552513232,\n",
    "    2004: 45427854693,\n",
    "    2005: 57633255738,\n",
    "    2006: 66371664817,\n",
    "    2007: 77414425532,\n",
    "    2008: 99130304099,\n",
    "    2009: int(1.06015E+11),\n",
    "    2010: int(1.47201E+11),\n",
    "    2011: int(1.72595E+11),\n",
    "    2012: int(1.95591E+11),\n",
    "    2013: int(2.13709E+11),\n",
    "    2014: int(2.33451E+11),\n",
    "    2015: int(2.39258E+11)\n",
    "}\n",
    "\n",
    "for year, gdp_value in vietnam_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Viet Nam\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "venezuela_gdp_values = {\n",
    "    2000: int(1.17146E+11),\n",
    "    2001: int(1.22911E+11),\n",
    "    2002: 92893587734,\n",
    "    2003: 83620628582,\n",
    "    2004: int(1.12451E+11),\n",
    "    2005: int(1.45513E+11),\n",
    "    2006: int(1.83478E+11),\n",
    "    2007: int(2.30364E+11),\n",
    "    2008: int(3.15953E+11),\n",
    "    2009: int(3.29788E+11),\n",
    "    2010: int(3.93192E+11),\n",
    "    2011: int(3.16482E+11),\n",
    "    2012: int(3.81286E+11),\n",
    "    2013: int(3.71005E+11),\n",
    "    2014: int(4.82359E+11)\n",
    "}\n",
    "\n",
    "for year, gdp_value in venezuela_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Venezuela (Bolivarian Republic of)\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "# Fill missing GDP values for Venezuela for 2015 with the GDP value of 2014\n",
    "venezuela_2014_gdp = venezuela_gdp_values[2014]\n",
    "dataset.loc[(dataset['Country'] == \"Venezuela (Bolivarian Republic of)\") & (dataset['Year'] == 2015) & (dataset['GDP'].isnull()), 'GDP'] = venezuela_2014_gdp\n",
    "\n",
    "\n",
    "usa_gdp_values = {\n",
    "    2000: int(1.02509E+13),\n",
    "    2001: int(1.05819E+13),\n",
    "    2002: int(1.09291E+13),\n",
    "    2003: int(1.14564E+13),\n",
    "    2004: int(1.22172E+13),\n",
    "    2005: int(1.30392E+13),\n",
    "    2006: int(1.38156E+13),\n",
    "    2007: int(1.44742E+13),\n",
    "    2008: int(1.47699E+13),\n",
    "    2009: int(1.44781E+13),\n",
    "    2010: int(1.5049E+13),\n",
    "    2011: int(1.55997E+13),\n",
    "    2012: int(1.6254E+13),\n",
    "    2013: int(1.68432E+13),\n",
    "    2014: int(1.75507E+13),\n",
    "    2015: int(1.8206E+13)\n",
    "}\n",
    "\n",
    "for year, gdp_value in usa_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"United States of America\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "saint_lucia_gdp_values = {\n",
    "    2000: 932592592.6,\n",
    "    2001: 892592592.6,\n",
    "    2002: 900000000,\n",
    "    2003: 987407407.4,\n",
    "    2004: 1066666667,\n",
    "    2005: 1135555556,\n",
    "    2006: 1268319185,\n",
    "    2007: 1336088815,\n",
    "    2008: 1437731111,\n",
    "    2009: 1401507889,\n",
    "    2010: 1482385185,\n",
    "    2011: 1568370370,\n",
    "    2012: 1598207407,\n",
    "    2013: 1660222222,\n",
    "    2014: 1749288889,\n",
    "    2015: 1807225926\n",
    "}\n",
    "\n",
    "for year, gdp_value in saint_lucia_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Saint Lucia\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "svg_gdp_values = {\n",
    "    2000: 427946037,\n",
    "    2001: 462072333.3,\n",
    "    2002: 487763851.9,\n",
    "    2003: 509090888.9,\n",
    "    2004: 549900185.2,\n",
    "    2005: 579948925.9,\n",
    "    2006: 643501148.1,\n",
    "    2007: 713596666.7,\n",
    "    2008: 732663259.3,\n",
    "    2009: 714300259.3,\n",
    "    2010: 720447888.9,\n",
    "    2011: 713796370.4,\n",
    "    2012: 730032592.6,\n",
    "    2013: 764781259.3,\n",
    "    2014: 770900000,\n",
    "    2015: 786555555.6\n",
    "}\n",
    "\n",
    "for year, gdp_value in svg_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Saint Vincent and the Grenadines\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "tanzania_gdp_values = {\n",
    "    2000: 13376365875,\n",
    "    2001: 13568654924,\n",
    "    2002: 14134511339,\n",
    "    2003: 15216719215,\n",
    "    2004: 16678796642,\n",
    "    2005: 18395383647,\n",
    "    2006: 18619859795,\n",
    "    2007: 21860434823,\n",
    "    2008: 27947821398,\n",
    "    2009: 29400573554,\n",
    "    2010: 32012892919,\n",
    "    2011: 34657140096,\n",
    "    2012: 39650394363,\n",
    "    2013: 45648857242,\n",
    "    2014: 49986726461,\n",
    "    2015: 47413919817\n",
    "}\n",
    "\n",
    "for year, gdp_value in tanzania_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"United Republic of Tanzania\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "slovakia_gdp_values = {\n",
    "    2000: 29242558797,\n",
    "    2001: 30778781607,\n",
    "    2002: 35297794386,\n",
    "    2003: 46919965224,\n",
    "    2004: 57437444469,\n",
    "    2005: 62808723477,\n",
    "    2006: 70767338922,\n",
    "    2007: 86563986799,\n",
    "    2008: 100880000000,\n",
    "    2009: 89399303222,\n",
    "    2010: 91162836320,\n",
    "    2011: 99922685425,\n",
    "    2012: 94623731086,\n",
    "    2013: 98935222175,\n",
    "    2014: 101437000000,\n",
    "    2015: 88900883131\n",
    "}\n",
    "\n",
    "\n",
    "for year, gdp_value in slovakia_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"Slovakia\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "    \n",
    "macedonia_gdp_values = {\n",
    "    2000: 3772859034,\n",
    "    2001: 3709636031,\n",
    "    2002: 4018365747,\n",
    "    2003: 4946296599,\n",
    "    2004: 5682784472,\n",
    "    2005: 6258602873,\n",
    "    2006: 6861226972,\n",
    "    2007: 8336474974,\n",
    "    2008: 9909552435,\n",
    "    2009: 9401736825,\n",
    "    2010: 9407170321,\n",
    "    2011: 10494626768,\n",
    "    2012: 9745261301,\n",
    "    2013: 10817702346,\n",
    "    2014: 11362265253,\n",
    "    2015: 10064519963\n",
    "}\n",
    "for year, gdp_value in macedonia_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"The former Yugoslav republic of Macedonia\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "uk_gdp_values = {\n",
    "    2000: 1.66553E+12,\n",
    "    2001: 1.64983E+12,\n",
    "    2002: 1.78573E+12,\n",
    "    2003: 2.05442E+12,\n",
    "    2004: 2.42153E+12,\n",
    "    2005: 2.54318E+12,\n",
    "    2006: 2.70844E+12,\n",
    "    2007: 3.09051E+12,\n",
    "    2008: 2.92941E+12,\n",
    "    2009: 2.41284E+12,\n",
    "    2010: 2.48548E+12,\n",
    "    2011: 2.66381E+12,\n",
    "    2012: 2.70709E+12,\n",
    "    2013: 2.78485E+12,\n",
    "    2014: 3.06471E+12,\n",
    "    2015: 2.92791E+12\n",
    "}\n",
    "\n",
    "for year, gdp_value in uk_gdp_values.items():\n",
    "    dataset.loc[(dataset['Country'] == \"United Kingdom of Great Britain and Northern Ireland\") & (dataset['Year'] == year), 'GDP'] = gdp_value\n",
    "\n",
    "rows_with_nan15 = dataset[dataset['GDP'].isnull()]\n",
    "print(\"Rows with NaN values after imputation:\")\n",
    "print(rows_with_nan15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "c2899457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with NaN values after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>...</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 22 columns]"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Solving the problem of missing values in Population column\n",
    "# Calculate mean Population for each country\n",
    "mean_Population = dataset.groupby('Country')['Population'].transform('mean')\n",
    "dataset['Population'].fillna(mean_Population, inplace=True)\n",
    "rows_with_nan13 = dataset[dataset['Population'].isnull()]\n",
    "# Print rows with NaN values after imputation\n",
    "print(\"Rows with NaN values after imputation:\")\n",
    "print(rows_with_nan13)\n",
    "\n",
    "dataset.loc[(dataset['Country'] == 'Antigua and Barbuda' ) & (dataset['Year'] == 2015) ,'Population'] = 89941\n",
    "dataset.loc[(dataset['Country'] == 'Antigua and Barbuda' ) & (dataset['Year'] == 2014) ,'Population'] = 89236\n",
    "dataset.loc[(dataset['Country'] == 'Antigua and Barbuda' ) & (dataset['Year'] == 2000) ,'Population'] = 75055\n",
    "dataset.loc[(dataset['Country'] == 'Antigua and Barbuda' ) & (dataset['Year'] == 2003) ,'Population'] = 78075\n",
    "dataset.loc[(dataset['Country'] == 'Antigua and Barbuda' ) & (dataset['Year'] == 2010) ,'Population'] = 85695\n",
    "#::::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Bahamas' ) & (dataset['Year'] == 2010) ,'Population'] = 373272\n",
    "dataset.loc[(dataset['Country'] == 'Bahamas' ) & (dataset['Year'] == 2005) ,'Population'] = 347804\n",
    "dataset.loc[(dataset['Country'] == 'Bahamas' ) & (dataset['Year'] == 2000) ,'Population'] = 325014\n",
    "dataset.loc[(dataset['Country'] == 'Bahamas' ) & (dataset['Year'] == 2015) ,'Population'] = 395976\n",
    "#:::::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Bahrain' ) & (dataset['Year'] == 2015) ,'Population'] = 1362142\n",
    "dataset.loc[(dataset['Country'] == 'Bahrain' ) & (dataset['Year'] == 2010) ,'Population'] = 1213645\n",
    "dataset.loc[(dataset['Country'] == 'Bahrain' ) & (dataset['Year'] == 2005) ,'Population'] = 901921\n",
    "dataset.loc[(dataset['Country'] == 'Bahrain' ) & (dataset['Year'] == 2000) ,'Population'] = 711442\n",
    "#::::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Barbados' ) & (dataset['Year'] == 2015) ,'Population'] = 278084\n",
    "dataset.loc[(dataset['Country'] == 'Barbados' ) & (dataset['Year'] == 2010) ,'Population'] = 274711\n",
    "dataset.loc[(dataset['Country'] == 'Barbados' ) & (dataset['Year'] == 2005) ,'Population'] = 269447\n",
    "dataset.loc[(dataset['Country'] == 'Barbados' ) & (dataset['Year'] == 2000) ,'Population'] = 264657\n",
    "#:::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Bolivia (Plurinational State of)' ) & (dataset['Year'] == 2015) ,'Population'] = 11090951\n",
    "dataset.loc[(dataset['Country'] == 'Bolivia (Plurinational State of)' ) & (dataset['Year'] == 2010) ,'Population'] = 10223231\n",
    "dataset.loc[(dataset['Country'] == 'Bolivia (Plurinational State of)' ) & (dataset['Year'] == 2005) ,'Population'] = 9377388\n",
    "dataset.loc[(dataset['Country'] == 'Bolivia (Plurinational State of)' ) & (dataset['Year'] == 2000) ,'Population'] = 8952623\n",
    "#:::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Brunei Darussalam' ) & (dataset['Year'] == 2015) ,'Population'] = 421437\n",
    "dataset.loc[(dataset['Country'] == 'Brunei Darussalam' ) & (dataset['Year'] == 2010) ,'Population'] = 400500\n",
    "dataset.loc[(dataset['Country'] == 'Brunei Darussalam' ) & (dataset['Year'] == 2005) ,'Population'] = 366717\n",
    "dataset.loc[(dataset['Country'] == 'Brunei Darussalam' ) & (dataset['Year'] == 2000) ,'Population'] = 430478\n",
    "#:::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Congo' ) & (dataset['Year'] == 2000) ,'Population'] = 48616317\n",
    "dataset.loc[(dataset['Country'] == 'Congo' ) & (dataset['Year'] == 2015) ,'Population'] = 78656904\n",
    "#:::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Cuba' ) & (dataset['Year'] == 2000) ,'Population'] = 11105791\n",
    "dataset.loc[(dataset['Country'] == 'Cuba' ) & (dataset['Year'] == 2015) ,'Population'] = 11339894\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == 'Czechia' ) & (dataset['Year'] == 2000) ,'Population'] = 10255060\n",
    "dataset.loc[(dataset['Country'] == 'Czechia' ) & (dataset['Year'] == 2015) ,'Population'] = 10546040\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Democratic People's Republic of Korea\" ) & (dataset['Year'] == 2000) ,'Population'] = 25258015\n",
    "dataset.loc[(dataset['Country'] == \"Democratic People's Republic of Korea\" ) & (dataset['Year'] == 2015) ,'Population'] = 10546040\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Democratic Republic of the Congo\" ) & (dataset['Year'] == 2000) ,'Population'] = 48616717\n",
    "dataset.loc[(dataset['Country'] == \"Democratic Republic of the Congo\" ) & (dataset['Year'] == 2015) ,'Population'] = 78656704\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Egypt\" ) & (dataset['Year'] == 2000) ,'Population'] = 71371371\n",
    "dataset.loc[(dataset['Country'] == \"Egypt\" ) & (dataset['Year'] == 2015) ,'Population'] = 97723799\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Gambia\" ) & (dataset['Year'] == 2000) ,'Population'] = 1437356\n",
    "dataset.loc[(dataset['Country'] == \"Gambia\" ) & (dataset['Year'] == 2015) ,'Population'] = 2253133\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Grenada\" ) & (dataset['Year'] == 2000) ,'Population'] = 107423\n",
    "dataset.loc[(dataset['Country'] == \"Grenada\" ) & (dataset['Year'] == 2015) ,'Population'] = 118980\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Iran (Islamic Republic of)\" ) & (dataset['Year'] == 2000) ,'Population'] = 65544333\n",
    "dataset.loc[(dataset['Country'] == \"Iran (Islamic Republic of)\" ) & (dataset['Year'] == 2015) ,'Population'] = 81790781\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Kuwait\" ) & (dataset['Year'] == 2000) ,'Population'] = 1991761\n",
    "dataset.loc[(dataset['Country'] == \"Kuwait\" ) & (dataset['Year'] == 2015) ,'Population'] = 3908708\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Kyrgyzstan\" ) & (dataset['Year'] == 2000) ,'Population'] = 4898400\n",
    "dataset.loc[(dataset['Country'] == \"Kyrgyzstan\" ) & (dataset['Year'] == 2015) ,'Population'] = 5956500\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Lao People's Democratic Republic\" ) & (dataset['Year'] == 2000) ,'Population'] = 5430580\n",
    "dataset.loc[(dataset['Country'] == \"Lao People's Democratic Republic\" ) & (dataset['Year'] == 2015) ,'Population'] = 6787900\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Libya\" ) & (dataset['Year'] == 2000) ,'Population'] = 5154790\n",
    "dataset.loc[(dataset['Country'] == \"Libya\" ) & (dataset['Year'] == 2015) ,'Population'] = 6192353\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Micronesia (Federated States of)\" ) & (dataset['Year'] == 2000) ,'Population'] = 111999\n",
    "dataset.loc[(dataset['Country'] == \"Micronesia (Federated States of)\" ) & (dataset['Year'] == 2015) ,'Population'] = 109462\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"New Zealand\" ) & (dataset['Year'] == 2000) ,'Population'] = 3875700\n",
    "dataset.loc[(dataset['Country'] == \"New Zealand\" ) & (dataset['Year'] == 2015) ,'Population'] = 4609400\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Oman\" ) & (dataset['Year'] == 2000) ,'Population'] = 2344340\n",
    "dataset.loc[(dataset['Country'] == \"Oman\" ) & (dataset['Year'] == 2015) ,'Population'] = 4191774\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Qatar\" ) & (dataset['Year'] == 2000) ,'Population'] = 645992\n",
    "dataset.loc[(dataset['Country'] == \"Qatar\" ) & (dataset['Year'] == 2015) ,'Population'] = 2414543\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Republic of Korea\" ) & (dataset['Year'] == 2000) ,'Population'] = 47008111\n",
    "dataset.loc[(dataset['Country'] == \"Republic of Korea\" ) & (dataset['Year'] == 2015) ,'Population'] = 51015431\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Saint Lucia\" ) & (dataset['Year'] == 2000) ,'Population'] = 195500\n",
    "dataset.loc[(dataset['Country'] == \"Saint Lucia\" ) & (dataset['Year'] == 2015) ,'Population'] = 175632\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Saudi Arabia\" ) & (dataset['Year'] == 2000) ,'Population'] = 21547422\n",
    "dataset.loc[(dataset['Country'] == \"Saudi Arabia\" ) & (dataset['Year'] == 2015) ,'Population'] = 32749868\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Singapore\" ) & (dataset['Year'] == 2000) ,'Population'] = 645992\n",
    "dataset.loc[(dataset['Country'] == \"Singapore\" ) & (dataset['Year'] == 2015) ,'Population'] = 2414543\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Slovakia\" ) & (dataset['Year'] == 2000) ,'Population'] = 5388731\n",
    "dataset.loc[(dataset['Country'] == \"Slovakia\" ) & (dataset['Year'] == 2015) ,'Population'] = 5423801\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Somalia\" ) & (dataset['Year'] == 2000) ,'Population'] = 8721431\n",
    "dataset.loc[(dataset['Country'] == \"Somalia\" ) & (dataset['Year'] == 2015) ,'Population'] = 13763400\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"The former Yugoslav republic of Macedonia\" ) & (dataset['Year'] == 2000) ,'Population'] = 2026333\n",
    "dataset.loc[(dataset['Country'] == \"The former Yugoslav republic of Macedonia\" ) & (dataset['Year'] == 2015) ,'Population'] = 2070222\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"United Arab Emirates\" ) & (dataset['Year'] == 2000) ,'Population'] = 3275333\n",
    "dataset.loc[(dataset['Country'] == \"United Arab Emirates\" ) & (dataset['Year'] == 2015) ,'Population'] = 8916999\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"United Kingdom of Great Britain and Northern Ireland\" ) & (dataset['Year'] == 2000) ,'Population'] = 58892222\n",
    "dataset.loc[(dataset['Country'] == \"United Kingdom of Great Britain and Northern Ireland\" ) & (dataset['Year'] == 2015) ,'Population'] = 65116222\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"United Republic of Tanzania\" ) & (dataset['Year'] == 2000) ,'Population'] = 34463700\n",
    "dataset.loc[(dataset['Country'] == \"United Republic of Tanzania\" ) & (dataset['Year'] == 2015) ,'Population'] = 52542800\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"United States of America\" ) & (dataset['Year'] == 2000) ,'Population'] = 282126444\n",
    "dataset.loc[(dataset['Country'] == \"United States of America\" ) & (dataset['Year'] == 2015) ,'Population'] = 320732898\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Venezuela (Bolivarian Republic of)\" ) & (dataset['Year'] == 2000) ,'Population'] = 24427729\n",
    "dataset.loc[(dataset['Country'] == \"Venezuela (Bolivarian Republic of)\" ) & (dataset['Year'] == 2015) ,'Population'] = 30529719\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Viet Nam\" ) & (dataset['Year'] == 2000) ,'Population'] = 79001192\n",
    "dataset.loc[(dataset['Country'] == \"Viet Nam\" ) & (dataset['Year'] == 2015) ,'Population'] = 92191400\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Yemen\" ) & (dataset['Year'] == 2000) ,'Population'] = 18628700\n",
    "dataset.loc[(dataset['Country'] == \"Yemen\" ) & (dataset['Year'] == 2015) ,'Population'] = 28516828\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Côte d'Ivoire\" ) & (dataset['Year'] == 2000) ,'Population'] = 16800799\n",
    "dataset.loc[(dataset['Country'] == \"Côte d'Ivoire\" ) & (dataset['Year'] == 2015) ,'Population'] = 23589741\n",
    "\n",
    "dataset.isna().sum()\n",
    "dataset.loc[(dataset['Country'] == \"Saint Vincent and the Grenadines\" ) & (dataset['Year'] == 2000) ,'Population'] = 55810\n",
    "dataset.loc[(dataset['Country'] == \"Saint Vincent and the Grenadines\" ) & (dataset['Year'] == 2015) ,'Population'] = 51944\n",
    "#::::::::::::::::::\n",
    "dataset.loc[(dataset['Country'] == \"Republic of Moldova\" ) & (dataset['Year'] == 2000) ,'Population'] = 1539705\n",
    "dataset.loc[(dataset['Country'] == \"Republic of Moldova\" ) & (dataset['Year'] == 2015) ,'Population'] = 1487320\n",
    "\n",
    "\n",
    "c40 = dataset[dataset['Country'] == \"Saint Vincent and the Grenadines)\" ]\n",
    "m40 = c40['Population'].mean()\n",
    "\n",
    "c41 = dataset[dataset['Country'] == \"Republic of Moldova\" ]\n",
    "m41 = c41['Population'].mean()\n",
    "\n",
    "c1 = dataset[dataset['Country'] == 'Yemen']\n",
    "m1 = c1['Population'].mean()\n",
    "\n",
    "c2 = dataset[dataset['Country'] == 'Viet Nam']\n",
    "m2 = c2['Population'].mean()\n",
    "\n",
    "c3 = dataset[dataset['Country'] == 'Antigua and Barbuda']\n",
    "m3 = c3['Population'].mean()\n",
    "\n",
    "c4 = dataset[dataset['Country'] == 'Bahamas']\n",
    "m4 = c4['Population'].mean()\n",
    "\n",
    "c5 = dataset[dataset['Country'] == 'Bahrain']\n",
    "m5 = c5['Population'].mean()\n",
    "\n",
    "c6 = dataset[dataset['Country'] == 'Barbados']\n",
    "m6 = c6['Population'].mean()\n",
    "\n",
    "c7 = dataset[dataset['Country'] == 'Bolivia (Plurinational State of)']\n",
    "m7 = c7['Population'].mean()\n",
    "\n",
    "c8 = dataset[dataset['Country'] == 'Brunei Darussalam']\n",
    "m8 = c8['Population'].mean()\n",
    "\n",
    "c9 = dataset[dataset['Country'] == 'Congo']\n",
    "m9 = c9['Population'].mean()\n",
    "\n",
    "c10 = dataset[dataset['Country'] == 'Cuba']\n",
    "m10 = c10['Population'].mean()\n",
    "\n",
    "c11 = dataset[dataset['Country'] == 'Czechia']\n",
    "m11 = c11['Population'].mean()\n",
    "\n",
    "c12 = dataset[dataset['Country'] == \"Democratic People's Republic of Korea\"]\n",
    "m12 = c12['Population'].mean()\n",
    "\n",
    "c13 = dataset[dataset['Country'] == 'Democratic Republic of the Congo']\n",
    "m13 = c13['Population'].mean()\n",
    "\n",
    "c14 = dataset[dataset['Country'] == 'Egypt']\n",
    "m14 = c14['Population'].mean()\n",
    "\n",
    "c15 = dataset[dataset['Country'] == 'Gambia']\n",
    "m15 = c15['Population'].mean()\n",
    "\n",
    "c16 = dataset[dataset['Country'] == 'Grenada']\n",
    "m16 = c16['Population'].mean()\n",
    "\n",
    "c17 = dataset[dataset['Country'] == \"Iran (Islamic Republic of)\" ]\n",
    "m17 = c17['Population'].mean()\n",
    "\n",
    "c18 = dataset[dataset['Country'] == 'Kuwait']\n",
    "m18 = c18['Population'].mean()\n",
    "\n",
    "c19 = dataset[dataset['Country'] == 'Kyrgyzstan']\n",
    "m19 = c19['Population'].mean()\n",
    "\n",
    "c20 = dataset[dataset['Country'] == \"Lao People's Democratic Republic\"]\n",
    "m20 = c20['Population'].mean()\n",
    "\n",
    "c21 = dataset[dataset['Country'] == 'Libya']\n",
    "m21 = c21['Population'].mean()\n",
    "\n",
    "c22 = dataset[dataset['Country'] == \"Micronesia (Federated States of)\" ]\n",
    "m22 = c22['Population'].mean()\n",
    "\n",
    "c23 = dataset[dataset['Country'] == 'New Zealand']\n",
    "m23 = c23['Population'].mean()\n",
    "\n",
    "c25 = dataset[dataset['Country'] == \"Oman\" ]\n",
    "m25 = c25['Population'].mean()\n",
    "\n",
    "c26 = dataset[dataset['Country'] == 'Qatar']\n",
    "m26 = c26['Population'].mean()\n",
    "\n",
    "c27 = dataset[dataset['Country'] == \" Republic of Korea\" ]\n",
    "m27 = c27['Population'].mean()\n",
    "\n",
    "c28 = dataset[dataset['Country'] == \"Saint Lucia\"]\n",
    "m28 = c28['Population'].mean()\n",
    "\n",
    "c29 = dataset[dataset['Country'] == 'Saudi Arabia']\n",
    "m29= c29['Population'].mean()\n",
    "\n",
    "c30 = dataset[dataset['Country'] == \"Singapore\" ]\n",
    "m30 = c30['Population'].mean()\n",
    "\n",
    "c31 = dataset[dataset['Country'] == 'Slovakia']\n",
    "m31 = c31['Population'].mean()\n",
    "\n",
    "c32 = dataset[dataset['Country'] == \"Somalia\" ]\n",
    "m32 = c32['Population'].mean()\n",
    "\n",
    "c33 = dataset[dataset['Country'] == \"The former Yugoslav republic of Macedonia\" ]\n",
    "m33 = c33['Population'].mean()\n",
    "\n",
    "c34 = dataset[dataset['Country'] == \"United Arab Emirates\" ]\n",
    "m34 = c34['Population'].mean()\n",
    "\n",
    "c35 = dataset[dataset['Country'] == 'United Kingdom of Great Britain and Northern Ireland']\n",
    "m35 = c35['Population'].mean()\n",
    "\n",
    "c36 = dataset[dataset['Country'] == \" United Republic of Tanzania\" ]\n",
    "m36 = c36['Population'].mean()\n",
    "\n",
    "c37 = dataset[dataset['Country'] == \"United States of America\"]\n",
    "m37 = c37['Population'].mean()\n",
    "\n",
    "c38 = dataset[dataset['Country'] == \"Venezuela (Bolivarian Republic of)\" ]\n",
    "m38= c38['Population'].mean()\n",
    "\n",
    "c39 = dataset[dataset['Country'] == \"Côte d'Ivoire\"  ]\n",
    "m39 = c39['Population'].mean()\n",
    "\n",
    "dataset['Population'].fillna(dataset.groupby('Country')['Population'].transform('mean'), inplace=True)\n",
    "\n",
    "\n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)\n",
    "\n",
    "rows_with_null_population = dataset[dataset['Population'].isnull()]\n",
    "rows_with_null_population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "8e17e62a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Assuming that Sudan and South Sudan have the same values as Somalia \n",
    "\n",
    "filtered_dataset = dataset[(dataset['Country'].isin(['Somalia', 'Sudan', 'South Sudan'])) & (dataset['Year'].between(2000, 2015))]\n",
    "somalia_data = filtered_dataset[filtered_dataset['Country'] == 'Somalia']\n",
    "sudan_data = filtered_dataset[filtered_dataset['Country'] == 'Sudan'].copy()\n",
    "south_sudan_data = filtered_dataset[filtered_dataset['Country'] == 'South Sudan'].copy()\n",
    "columns_to_fill = [' thinness  1-19 years', ' thinness 5-9 years']\n",
    "\n",
    "for column in columns_to_fill:\n",
    "\n",
    "    sudan_data[column].fillna(somalia_data[column].values[0], inplace=True)\n",
    "\n",
    "\n",
    "    south_sudan_data[column].fillna(somalia_data[column].values[0], inplace=True)\n",
    "\n",
    "\n",
    "filled_dataset = pd.concat([somalia_data, sudan_data, south_sudan_data])\n",
    "#Transfer changes to dataset\n",
    "filtered_dataset = dataset[(dataset['Country'].isin(['Somalia', 'Sudan', 'South Sudan'])) & (dataset['Year'].between(2000, 2015))]\n",
    "somalia_data = filtered_dataset[filtered_dataset['Country'] == 'Somalia']\n",
    "sudan_data = filtered_dataset[filtered_dataset['Country'] == 'Sudan']\n",
    "south_sudan_data = filtered_dataset[filtered_dataset['Country'] == 'South Sudan']\n",
    "columns_to_fill = [' thinness  1-19 years', ' thinness 5-9 years']\n",
    "\n",
    "for column in columns_to_fill:\n",
    "    dataset.loc[(dataset['Country'] == 'Sudan') & (dataset['Year'].between(2000, 2015)), column] = somalia_data[column].values[0]\n",
    "    dataset.loc[(dataset['Country'] == 'South Sudan') & (dataset['Year'].between(2000, 2015)), column] = somalia_data[column].values[0]\n",
    "missing_values158 = dataset.isnull().sum()\n",
    "print(missing_values158)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "331a25c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with empty data in Thinness column\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n",
      "       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',\n",
      "       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',\n",
      "       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',\n",
      "       ' thinness  1-19 years', ' thinness 5-9 years',\n",
      "       'Income composition of resources', 'Schooling'],\n",
      "      dtype='object')\n",
      "      Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
      "2377  Somalia  2015  Developing              55.0            312.0   \n",
      "2378  Somalia  2014  Developing              54.3            321.0   \n",
      "2379  Somalia  2013  Developing              54.2            318.0   \n",
      "2380  Somalia  2012  Developing              53.1            336.0   \n",
      "2381  Somalia  2011  Developing              53.1            329.0   \n",
      "2382  Somalia  2010  Developing              52.4            336.0   \n",
      "2383  Somalia  2009  Developing              52.2            335.0   \n",
      "2384  Somalia  2008  Developing              51.9            336.0   \n",
      "2385  Somalia  2007  Developing              51.5             34.0   \n",
      "2386  Somalia  2006  Developing              51.5            337.0   \n",
      "2387  Somalia  2005  Developing              51.6            334.0   \n",
      "2388  Somalia  2004  Developing              51.2            341.0   \n",
      "2389  Somalia  2003  Developing              51.1            344.0   \n",
      "2390  Somalia  2002  Developing              58.0            348.0   \n",
      "2391  Somalia  2001  Developing              57.0            352.0   \n",
      "2392  Somalia  2000  Developing              55.0            355.0   \n",
      "2457    Sudan  2015  Developing              64.1            225.0   \n",
      "2458    Sudan  2014  Developing              63.8            229.0   \n",
      "2459    Sudan  2013  Developing              63.5            232.0   \n",
      "2460    Sudan  2012  Developing              63.2            235.0   \n",
      "2461    Sudan  2011  Developing              62.7            241.0   \n",
      "2462    Sudan  2010  Developing              62.5            243.0   \n",
      "2463    Sudan  2009  Developing              62.0            248.0   \n",
      "2464    Sudan  2008  Developing              61.8            251.0   \n",
      "2465    Sudan  2007  Developing              61.4            254.0   \n",
      "2466    Sudan  2006  Developing              61.0             26.0   \n",
      "2467    Sudan  2005  Developing              67.0            261.0   \n",
      "2468    Sudan  2004  Developing              59.7            278.0   \n",
      "2469    Sudan  2003  Developing              59.6            278.0   \n",
      "2470    Sudan  2002  Developing              59.4            277.0   \n",
      "2471    Sudan  2001  Developing              58.9            283.0   \n",
      "2472    Sudan  2000  Developing              58.6            284.0   \n",
      "\n",
      "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
      "2377             50    0.010                0.000000    42.000000      7497   \n",
      "2378             51    0.010                0.000000    42.000000     10229   \n",
      "2379             51    0.010                0.000000    42.000000      3173   \n",
      "2380             51    0.010                0.000000    42.000000      9983   \n",
      "2381             51    0.010                0.000000    42.000000     17298   \n",
      "2382             52    0.010                0.000000    42.000000       115   \n",
      "2383             52    0.010                0.000000    42.000000        13   \n",
      "2384             52    0.010                0.000000    42.000000      1081   \n",
      "2385             52    0.010                0.000000    42.000000      1149   \n",
      "2386             51    0.010                0.000000    42.000000         7   \n",
      "2387             50    0.010                0.000000    42.000000         0   \n",
      "2388             49    0.010                0.000000    42.000000     12008   \n",
      "2389             48    0.010                0.000000    42.000000      8257   \n",
      "2390             47    0.010                0.000000    42.000000      9559   \n",
      "2391             46    0.010                0.000000    42.000000      3571   \n",
      "2392             45    0.010                0.000000    42.000000      3965   \n",
      "2457             58    1.458                0.000000    93.000000      3585   \n",
      "2458             59    0.010              253.608651    94.000000       676   \n",
      "2459             60    0.010              227.835321    93.000000      2813   \n",
      "2460             61    0.010              220.522192    92.000000      8523   \n",
      "2461             61    2.120              196.689215    93.000000      5616   \n",
      "2462             62    1.770              172.009788    75.000000       680   \n",
      "2463             63    1.990               17.053693    72.000000        68   \n",
      "2464             64    2.010              128.636271    78.000000       129   \n",
      "2465             65    2.010               86.131669    78.000000       327   \n",
      "2466             66    1.900               60.336857     6.000000       228   \n",
      "2467             66    1.550               37.590396    22.000000      1374   \n",
      "2468             68    1.590               37.044800    72.363636      9562   \n",
      "2469             69    1.740               35.352647    72.363636      4381   \n",
      "2470             70    1.590               30.622875    72.363636      4529   \n",
      "2471             71    1.810               28.880697    72.363636      4362   \n",
      "2472             71    1.760               30.860010    72.363636      2875   \n",
      "\n",
      "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS          GDP  \\\n",
      "2377  ...   47.0           2.000000         42.0        0.7   426.985370   \n",
      "2378  ...   47.0           2.000000         42.0        0.8   417.891430   \n",
      "2379  ...   47.0           2.000000         42.0        0.8    47.543235   \n",
      "2380  ...   47.0           2.000000         42.0        0.8   297.473345   \n",
      "2381  ...   49.0           2.000000         41.0        0.8   297.473345   \n",
      "2382  ...   49.0           2.000000         45.0        0.8   297.473345   \n",
      "2383  ...   41.0           2.000000         42.0        0.8   297.473345   \n",
      "2384  ...    4.0           2.000000         31.0        0.9   297.473345   \n",
      "2385  ...    4.0           2.000000          4.0        0.9   297.473345   \n",
      "2386  ...   26.0           2.000000         26.0        0.9   297.473345   \n",
      "2387  ...   35.0           2.000000         35.0        0.9   297.473345   \n",
      "2388  ...    3.0           2.000000          3.0        0.9   297.473345   \n",
      "2389  ...    4.0           2.000000          4.0        0.9   297.473345   \n",
      "2390  ...    4.0           2.000000          4.0        0.9   297.473345   \n",
      "2391  ...   33.0           2.000000         33.0        0.8   297.473345   \n",
      "2392  ...   37.0           2.000000         33.0        0.8   297.473345   \n",
      "2457  ...   93.0           5.695333         93.0        0.3  2513.884661   \n",
      "2458  ...   94.0           8.430000         94.0        0.3  2176.898290   \n",
      "2459  ...   93.0           8.420000         93.0        0.3  1955.667990   \n",
      "2460  ...   92.0           8.200000         92.0        0.3  1892.894352   \n",
      "2461  ...   93.0           8.300000         93.0        0.3  1666.857757   \n",
      "2462  ...    9.0           7.970000          9.0        0.3  1476.478870   \n",
      "2463  ...   81.0           8.400000         81.0        0.3  1226.884381   \n",
      "2464  ...   85.0           8.170000         86.0        0.3  1291.528826   \n",
      "2465  ...   84.0           4.720000         84.0        0.3  1115.695200   \n",
      "2466  ...   77.0           3.930000         78.0        0.2   893.879364   \n",
      "2467  ...   78.0           3.180000         78.0        0.2   679.753995   \n",
      "2468  ...   74.0           3.390000         74.0        0.2   565.569459   \n",
      "2469  ...   69.0           3.180000         69.0        0.2   477.738478   \n",
      "2470  ...    6.0           2.950000          6.0        0.2   412.151756   \n",
      "2471  ...   66.0           2.960000         66.0        0.2   377.525445   \n",
      "2472  ...   62.0           3.230000         62.0        0.1   361.358430   \n",
      "\n",
      "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
      "2377  13763400.0                    6.6                  6.4   \n",
      "2378  11242415.5                    6.7                  6.5   \n",
      "2379  11242415.5                    6.8                  6.6   \n",
      "2380  11242415.5                    6.8                  6.7   \n",
      "2381  11242415.5                    6.9                  6.7   \n",
      "2382  11242415.5                    7.0                  6.8   \n",
      "2383  11242415.5                    7.1                  6.9   \n",
      "2384  11242415.5                    7.2                  7.0   \n",
      "2385  11242415.5                    7.3                  7.1   \n",
      "2386  11242415.5                    7.4                  7.2   \n",
      "2387  11242415.5                    7.5                  7.3   \n",
      "2388  11242415.5                    7.6                  7.4   \n",
      "2389  11242415.5                    7.7                  7.5   \n",
      "2390  11242415.5                    7.8                  7.6   \n",
      "2391  11242415.5                    7.9                  7.7   \n",
      "2392   8721431.0                    8.0                  7.9   \n",
      "2457   3864783.0                    6.6                  6.4   \n",
      "2458  37737913.0                    6.6                  6.4   \n",
      "2459  36849918.0                    6.6                  6.4   \n",
      "2460   3599192.0                    6.6                  6.4   \n",
      "2461  35167314.0                    6.6                  6.4   \n",
      "2462  34385963.0                    6.6                  6.4   \n",
      "2463   3365619.0                    6.6                  6.4   \n",
      "2464  32955496.0                    6.6                  6.4   \n",
      "2465  32282526.0                    6.6                  6.4   \n",
      "2466    316764.0                    6.6                  6.4   \n",
      "2467   3911914.0                    6.6                  6.4   \n",
      "2468   3186341.0                    6.6                  6.4   \n",
      "2469  29435944.0                    6.6                  6.4   \n",
      "2470  28679565.0                    6.6                  6.4   \n",
      "2471    279455.0                    6.6                  6.4   \n",
      "2472   2725535.0                    6.6                  6.4   \n",
      "\n",
      "      Income composition of resources  Schooling  \n",
      "2377                            0.900        1.7  \n",
      "2378                            0.900        1.7  \n",
      "2379                            0.900        1.7  \n",
      "2380                            0.900        1.7  \n",
      "2381                            0.900        1.7  \n",
      "2382                            0.900        1.7  \n",
      "2383                            0.900        1.7  \n",
      "2384                            0.900        1.7  \n",
      "2385                            0.900        1.7  \n",
      "2386                            0.900        1.7  \n",
      "2387                            0.900        1.7  \n",
      "2388                            0.900        1.7  \n",
      "2389                            0.900        1.7  \n",
      "2390                            0.900        1.7  \n",
      "2391                            0.900        1.7  \n",
      "2392                            0.900        1.7  \n",
      "2457                            0.488        7.2  \n",
      "2458                            0.485        7.2  \n",
      "2459                            0.478        7.0  \n",
      "2460                            0.468        6.8  \n",
      "2461                            0.463        7.0  \n",
      "2462                            0.461        7.0  \n",
      "2463                            0.456        6.8  \n",
      "2464                            0.444        6.3  \n",
      "2465                            0.440        6.4  \n",
      "2466                            0.430        6.2  \n",
      "2467                            0.423        6.1  \n",
      "2468                            0.415        5.7  \n",
      "2469                            0.409        5.6  \n",
      "2470                            0.403        5.6  \n",
      "2471                            0.399        5.6  \n",
      "2472                            0.394        5.5  \n",
      "\n",
      "[32 rows x 22 columns]\n",
      "Rows with NaN values in Thinness 1-19 years after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n",
      "##################################################################################################\n",
      "Rows with NaN values in Thinness 1-19 years after imputation:\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n",
      "          Country  Year      Status  Life expectancy   Adult Mortality  \\\n",
      "2377      Somalia  2015  Developing              55.0            312.0   \n",
      "2378      Somalia  2014  Developing              54.3            321.0   \n",
      "2379      Somalia  2013  Developing              54.2            318.0   \n",
      "2380      Somalia  2012  Developing              53.1            336.0   \n",
      "2381      Somalia  2011  Developing              53.1            329.0   \n",
      "2382      Somalia  2010  Developing              52.4            336.0   \n",
      "2383      Somalia  2009  Developing              52.2            335.0   \n",
      "2384      Somalia  2008  Developing              51.9            336.0   \n",
      "2385      Somalia  2007  Developing              51.5             34.0   \n",
      "2386      Somalia  2006  Developing              51.5            337.0   \n",
      "2387      Somalia  2005  Developing              51.6            334.0   \n",
      "2388      Somalia  2004  Developing              51.2            341.0   \n",
      "2389      Somalia  2003  Developing              51.1            344.0   \n",
      "2390      Somalia  2002  Developing              58.0            348.0   \n",
      "2391      Somalia  2001  Developing              57.0            352.0   \n",
      "2392      Somalia  2000  Developing              55.0            355.0   \n",
      "2457        Sudan  2015  Developing              64.1            225.0   \n",
      "2458        Sudan  2014  Developing              63.8            229.0   \n",
      "2459        Sudan  2013  Developing              63.5            232.0   \n",
      "2460        Sudan  2012  Developing              63.2            235.0   \n",
      "2461        Sudan  2011  Developing              62.7            241.0   \n",
      "2462        Sudan  2010  Developing              62.5            243.0   \n",
      "2463        Sudan  2009  Developing              62.0            248.0   \n",
      "2464        Sudan  2008  Developing              61.8            251.0   \n",
      "2465        Sudan  2007  Developing              61.4            254.0   \n",
      "2466        Sudan  2006  Developing              61.0             26.0   \n",
      "2467        Sudan  2005  Developing              67.0            261.0   \n",
      "2468        Sudan  2004  Developing              59.7            278.0   \n",
      "2469        Sudan  2003  Developing              59.6            278.0   \n",
      "2470        Sudan  2002  Developing              59.4            277.0   \n",
      "2471        Sudan  2001  Developing              58.9            283.0   \n",
      "2472        Sudan  2000  Developing              58.6            284.0   \n",
      "2409  South Sudan  2015  Developing              57.3            332.0   \n",
      "2410  South Sudan  2014  Developing              56.6            343.0   \n",
      "2411  South Sudan  2013  Developing              56.4            345.0   \n",
      "2412  South Sudan  2012  Developing              56.0            347.0   \n",
      "2413  South Sudan  2011  Developing              55.4            355.0   \n",
      "2414  South Sudan  2010  Developing              55.0            359.0   \n",
      "2415  South Sudan  2009  Developing              54.3            369.0   \n",
      "2416  South Sudan  2008  Developing              53.6            377.0   \n",
      "2417  South Sudan  2007  Developing              53.1            381.0   \n",
      "2418  South Sudan  2006  Developing              52.5            383.0   \n",
      "2419  South Sudan  2005  Developing              51.9            383.0   \n",
      "2420  South Sudan  2004  Developing              51.4            383.0   \n",
      "2421  South Sudan  2003  Developing              58.0            383.0   \n",
      "2422  South Sudan  2002  Developing              52.0            382.0   \n",
      "2423  South Sudan  2001  Developing              49.6            381.0   \n",
      "2424  South Sudan  2000  Developing              48.9             38.0   \n",
      "\n",
      "      infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles   \\\n",
      "2377             50    0.010                0.000000    42.000000      7497   \n",
      "2378             51    0.010                0.000000    42.000000     10229   \n",
      "2379             51    0.010                0.000000    42.000000      3173   \n",
      "2380             51    0.010                0.000000    42.000000      9983   \n",
      "2381             51    0.010                0.000000    42.000000     17298   \n",
      "2382             52    0.010                0.000000    42.000000       115   \n",
      "2383             52    0.010                0.000000    42.000000        13   \n",
      "2384             52    0.010                0.000000    42.000000      1081   \n",
      "2385             52    0.010                0.000000    42.000000      1149   \n",
      "2386             51    0.010                0.000000    42.000000         7   \n",
      "2387             50    0.010                0.000000    42.000000         0   \n",
      "2388             49    0.010                0.000000    42.000000     12008   \n",
      "2389             48    0.010                0.000000    42.000000      8257   \n",
      "2390             47    0.010                0.000000    42.000000      9559   \n",
      "2391             46    0.010                0.000000    42.000000      3571   \n",
      "2392             45    0.010                0.000000    42.000000      3965   \n",
      "2457             58    1.458                0.000000    93.000000      3585   \n",
      "2458             59    0.010              253.608651    94.000000       676   \n",
      "2459             60    0.010              227.835321    93.000000      2813   \n",
      "2460             61    0.010              220.522192    92.000000      8523   \n",
      "2461             61    2.120              196.689215    93.000000      5616   \n",
      "2462             62    1.770              172.009788    75.000000       680   \n",
      "2463             63    1.990               17.053693    72.000000        68   \n",
      "2464             64    2.010              128.636271    78.000000       129   \n",
      "2465             65    2.010               86.131669    78.000000       327   \n",
      "2466             66    1.900               60.336857     6.000000       228   \n",
      "2467             66    1.550               37.590396    22.000000      1374   \n",
      "2468             68    1.590               37.044800    72.363636      9562   \n",
      "2469             69    1.740               35.352647    72.363636      4381   \n",
      "2470             70    1.590               30.622875    72.363636      4529   \n",
      "2471             71    1.810               28.880697    72.363636      4362   \n",
      "2472             71    1.760               30.860010    72.363636      2875   \n",
      "2409             26    0.000                0.000000    31.000000       878   \n",
      "2410             26    0.000               46.074469    31.000000       441   \n",
      "2411             26    0.000               47.444530    31.000000       525   \n",
      "2412             26    0.000               38.338232    31.000000      1952   \n",
      "2413             27    0.000                0.000000    31.000000      1256   \n",
      "2414             27    0.000                0.000000    31.000000         0   \n",
      "2415             27    0.000                0.000000    31.000000         0   \n",
      "2416             27    0.000                0.000000    31.000000         0   \n",
      "2417             27    0.000                0.000000    31.000000         0   \n",
      "2418             28    0.000                0.000000    31.000000         0   \n",
      "2419             28    0.000                0.000000    31.000000         0   \n",
      "2420             29    0.000                0.000000    31.000000         0   \n",
      "2421             29    0.000                0.000000    31.000000         0   \n",
      "2422             30    0.000                0.000000    31.000000         0   \n",
      "2423             30    0.000                0.000000    31.000000         0   \n",
      "2424             31    0.000                0.000000    31.000000         0   \n",
      "\n",
      "      ...  Polio  Total expenditure  Diphtheria    HIV/AIDS          GDP  \\\n",
      "2377  ...   47.0           2.000000         42.0        0.7   426.985370   \n",
      "2378  ...   47.0           2.000000         42.0        0.8   417.891430   \n",
      "2379  ...   47.0           2.000000         42.0        0.8    47.543235   \n",
      "2380  ...   47.0           2.000000         42.0        0.8   297.473345   \n",
      "2381  ...   49.0           2.000000         41.0        0.8   297.473345   \n",
      "2382  ...   49.0           2.000000         45.0        0.8   297.473345   \n",
      "2383  ...   41.0           2.000000         42.0        0.8   297.473345   \n",
      "2384  ...    4.0           2.000000         31.0        0.9   297.473345   \n",
      "2385  ...    4.0           2.000000          4.0        0.9   297.473345   \n",
      "2386  ...   26.0           2.000000         26.0        0.9   297.473345   \n",
      "2387  ...   35.0           2.000000         35.0        0.9   297.473345   \n",
      "2388  ...    3.0           2.000000          3.0        0.9   297.473345   \n",
      "2389  ...    4.0           2.000000          4.0        0.9   297.473345   \n",
      "2390  ...    4.0           2.000000          4.0        0.9   297.473345   \n",
      "2391  ...   33.0           2.000000         33.0        0.8   297.473345   \n",
      "2392  ...   37.0           2.000000         33.0        0.8   297.473345   \n",
      "2457  ...   93.0           5.695333         93.0        0.3  2513.884661   \n",
      "2458  ...   94.0           8.430000         94.0        0.3  2176.898290   \n",
      "2459  ...   93.0           8.420000         93.0        0.3  1955.667990   \n",
      "2460  ...   92.0           8.200000         92.0        0.3  1892.894352   \n",
      "2461  ...   93.0           8.300000         93.0        0.3  1666.857757   \n",
      "2462  ...    9.0           7.970000          9.0        0.3  1476.478870   \n",
      "2463  ...   81.0           8.400000         81.0        0.3  1226.884381   \n",
      "2464  ...   85.0           8.170000         86.0        0.3  1291.528826   \n",
      "2465  ...   84.0           4.720000         84.0        0.3  1115.695200   \n",
      "2466  ...   77.0           3.930000         78.0        0.2   893.879364   \n",
      "2467  ...   78.0           3.180000         78.0        0.2   679.753995   \n",
      "2468  ...   74.0           3.390000         74.0        0.2   565.569459   \n",
      "2469  ...   69.0           3.180000         69.0        0.2   477.738478   \n",
      "2470  ...    6.0           2.950000          6.0        0.2   412.151756   \n",
      "2471  ...   66.0           2.960000         66.0        0.2   377.525445   \n",
      "2472  ...   62.0           3.230000         62.0        0.1   361.358430   \n",
      "2409  ...   41.0           2.710000         31.0        3.4   758.725782   \n",
      "2410  ...   44.0           2.740000         39.0        3.5  1151.861715   \n",
      "2411  ...    5.0           2.620000         45.0        3.6  1186.113250   \n",
      "2412  ...   64.0           2.770000         59.0        3.8   958.455810   \n",
      "2413  ...   66.0           2.710000         61.0        3.9   176.971300   \n",
      "2414  ...   44.0           2.710000         47.0        4.0  1562.239346   \n",
      "2415  ...   44.0           2.710000         47.0        4.2  1264.789980   \n",
      "2416  ...   44.0           2.710000         47.0        4.2  1678.711862   \n",
      "2417  ...   44.0           2.710000         47.0        4.2  1092.233631   \n",
      "2418  ...   44.0           2.710000         47.0        4.1  1092.233631   \n",
      "2419  ...   44.0           2.710000         47.0        3.9  1092.233631   \n",
      "2420  ...   44.0           2.710000         47.0        3.8  1092.233631   \n",
      "2421  ...   44.0           2.710000         47.0        3.5  1092.233631   \n",
      "2422  ...   44.0           2.710000         47.0        3.3  1092.233631   \n",
      "2423  ...   44.0           2.710000         47.0        3.0  1092.233631   \n",
      "2424  ...   44.0           2.710000         47.0        2.7  1092.233631   \n",
      "\n",
      "      Population   thinness  1-19 years   thinness 5-9 years  \\\n",
      "2377  13763400.0                    6.6                  6.4   \n",
      "2378  11242415.5                    6.7                  6.5   \n",
      "2379  11242415.5                    6.8                  6.6   \n",
      "2380  11242415.5                    6.8                  6.7   \n",
      "2381  11242415.5                    6.9                  6.7   \n",
      "2382  11242415.5                    7.0                  6.8   \n",
      "2383  11242415.5                    7.1                  6.9   \n",
      "2384  11242415.5                    7.2                  7.0   \n",
      "2385  11242415.5                    7.3                  7.1   \n",
      "2386  11242415.5                    7.4                  7.2   \n",
      "2387  11242415.5                    7.5                  7.3   \n",
      "2388  11242415.5                    7.6                  7.4   \n",
      "2389  11242415.5                    7.7                  7.5   \n",
      "2390  11242415.5                    7.8                  7.6   \n",
      "2391  11242415.5                    7.9                  7.7   \n",
      "2392   8721431.0                    8.0                  7.9   \n",
      "2457   3864783.0                    6.6                  6.4   \n",
      "2458  37737913.0                    6.6                  6.4   \n",
      "2459  36849918.0                    6.6                  6.4   \n",
      "2460   3599192.0                    6.6                  6.4   \n",
      "2461  35167314.0                    6.6                  6.4   \n",
      "2462  34385963.0                    6.6                  6.4   \n",
      "2463   3365619.0                    6.6                  6.4   \n",
      "2464  32955496.0                    6.6                  6.4   \n",
      "2465  32282526.0                    6.6                  6.4   \n",
      "2466    316764.0                    6.6                  6.4   \n",
      "2467   3911914.0                    6.6                  6.4   \n",
      "2468   3186341.0                    6.6                  6.4   \n",
      "2469  29435944.0                    6.6                  6.4   \n",
      "2470  28679565.0                    6.6                  6.4   \n",
      "2471    279455.0                    6.6                  6.4   \n",
      "2472   2725535.0                    6.6                  6.4   \n",
      "2409  11882136.0                    6.6                  6.4   \n",
      "2410   1153971.0                    6.6                  6.4   \n",
      "2411   1117749.0                    6.6                  6.4   \n",
      "2412   1818258.0                    6.6                  6.4   \n",
      "2413   1448857.0                    6.6                  6.4   \n",
      "2414    167192.0                    6.6                  6.4   \n",
      "2415    967667.0                    6.6                  6.4   \n",
      "2416   9263136.0                    6.6                  6.4   \n",
      "2417     88568.0                    6.6                  6.4   \n",
      "2418   8468152.0                    6.6                  6.4   \n",
      "2419    818877.0                    6.6                  6.4   \n",
      "2420   7787655.0                    6.6                  6.4   \n",
      "2421    751642.0                    6.6                  6.4   \n",
      "2422   7237276.0                    6.6                  6.4   \n",
      "2423   6974442.0                    6.6                  6.4   \n",
      "2424     67656.0                    6.6                  6.4   \n",
      "\n",
      "      Income composition of resources  Schooling  \n",
      "2377                            0.900        1.7  \n",
      "2378                            0.900        1.7  \n",
      "2379                            0.900        1.7  \n",
      "2380                            0.900        1.7  \n",
      "2381                            0.900        1.7  \n",
      "2382                            0.900        1.7  \n",
      "2383                            0.900        1.7  \n",
      "2384                            0.900        1.7  \n",
      "2385                            0.900        1.7  \n",
      "2386                            0.900        1.7  \n",
      "2387                            0.900        1.7  \n",
      "2388                            0.900        1.7  \n",
      "2389                            0.900        1.7  \n",
      "2390                            0.900        1.7  \n",
      "2391                            0.900        1.7  \n",
      "2392                            0.900        1.7  \n",
      "2457                            0.488        7.2  \n",
      "2458                            0.485        7.2  \n",
      "2459                            0.478        7.0  \n",
      "2460                            0.468        6.8  \n",
      "2461                            0.463        7.0  \n",
      "2462                            0.461        7.0  \n",
      "2463                            0.456        6.8  \n",
      "2464                            0.444        6.3  \n",
      "2465                            0.440        6.4  \n",
      "2466                            0.430        6.2  \n",
      "2467                            0.423        6.1  \n",
      "2468                            0.415        5.7  \n",
      "2469                            0.409        5.6  \n",
      "2470                            0.403        5.6  \n",
      "2471                            0.399        5.6  \n",
      "2472                            0.394        5.5  \n",
      "2409                            0.421        4.9  \n",
      "2410                            0.421        4.9  \n",
      "2411                            0.417        4.9  \n",
      "2412                            0.419        4.9  \n",
      "2413                            0.429        4.9  \n",
      "2414                            0.000        0.0  \n",
      "2415                            0.000        0.0  \n",
      "2416                            0.000        0.0  \n",
      "2417                            0.000        0.0  \n",
      "2418                            0.000        0.0  \n",
      "2419                            0.000        0.0  \n",
      "2420                            0.000        0.0  \n",
      "2421                            0.000        0.0  \n",
      "2422                            0.000        0.0  \n",
      "2423                            0.000        0.0  \n",
      "2424                            0.000        0.0  \n",
      "\n",
      "[48 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#another method to solve the problem of missing values in thinness columns\n",
    "\n",
    "print(\"Rows with empty data in Thinness column\")\n",
    "filtered_rows3 = dataset[dataset[' thinness  1-19 years'].isnull()]\n",
    "print(filtered_rows3)\n",
    "print(filtered_dataset.columns)\n",
    "# Assuming that Sudan and South Sudan have the same values as Somalia \n",
    "filtered_dataset = dataset[(dataset['Country'].isin(['Somalia', 'Sudan'])) & (dataset['Year'].between(2000, 2015))]\n",
    "\n",
    "# Extract the values for Somalia\n",
    "somalia_values = filtered_dataset[filtered_dataset['Country'] == 'Somalia'].set_index('Year')[' thinness  1-19 years']\n",
    "\n",
    "# Fill missing values for Sudan with Somalia values\n",
    "filtered_dataset.loc[filtered_dataset['Country'] == 'Sudan', ' thinness  1-19 years'] = filtered_dataset.loc[filtered_dataset['Country'] == 'Sudan', ' thinness  1-19 years'].fillna(somalia_values)\n",
    "\n",
    "# Display the updated dataset\n",
    "print(filtered_dataset)\n",
    "\n",
    "filtered_rows19 = dataset[dataset[' thinness  1-19 years'].isnull()]\n",
    "print(\"Rows with NaN values in Thinness 1-19 years after imputation:\")\n",
    "print(filtered_rows19)\n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)\n",
    "print(\"##################################################################################################\")\n",
    "filtered_dataset = dataset[(dataset['Country'].isin(['Somalia', 'Sudan', 'South Sudan'])) & (dataset['Year'].between(2000, 2015))]\n",
    "\n",
    "# Extract the values for Somalia\n",
    "somalia_values = filtered_dataset[filtered_dataset['Country'] == 'Somalia'].set_index('Year')[[' thinness  1-19 years', ' thinness 5-9 years']]\n",
    "\n",
    "# Loop through each year\n",
    "for year in range(2000, 2016):\n",
    "    # Fill missing values for Sudan and South Sudan with Somalia values for the current year\n",
    "    for country in ['Sudan', 'South Sudan']:\n",
    "        # Get Somalia values for the current year\n",
    "        somalia_year_values = somalia_values.loc[year]\n",
    "        # Fill missing values for the current year and country\n",
    "        filtered_dataset.loc[(filtered_dataset['Country'] == country) & (filtered_dataset['Year'] == year), [' thinness  1-19 years', ' thinness 5-9 years']] = filtered_dataset.loc[(filtered_dataset['Country'] == country) & (filtered_dataset['Year'] == year), [' thinness  1-19 years', ' thinness 5-9 years']].fillna(somalia_year_values)\n",
    "filtered_rows191 = dataset[dataset[' thinness  1-19 years'].isnull()]\n",
    "print(\"Rows with NaN values in Thinness 1-19 years after imputation:\")\n",
    "print(filtered_rows191)\n",
    "missing_values15 = dataset.isnull().sum()\n",
    "print(missing_values15)\n",
    "\n",
    "\n",
    "filtered_dataset = dataset[(dataset['Country'].isin(['Somalia', 'Sudan', 'South Sudan'])) & (dataset['Year'].between(2000, 2015))]\n",
    "\n",
    "# Get Somalia data\n",
    "somalia_data = filtered_dataset[filtered_dataset['Country'] == 'Somalia']\n",
    "\n",
    "# Get Sudan data\n",
    "sudan_data = filtered_dataset[filtered_dataset['Country'] == 'Sudan'].copy()\n",
    "\n",
    "# Get South Sudan data\n",
    "south_sudan_data = filtered_dataset[filtered_dataset['Country'] == 'South Sudan'].copy()\n",
    "\n",
    "# Iterate over the columns containing missing values in Sudan and South Sudan\n",
    "columns_to_fill = [' thinness  1-19 years', ' thinness 5-9 years']\n",
    "\n",
    "for column in columns_to_fill:\n",
    "    # Fill missing values for Sudan with Somalia values\n",
    "    sudan_data[column].fillna(somalia_data[column].values[0], inplace=True)\n",
    "\n",
    "    # Fill missing values for South Sudan with Somalia values\n",
    "    south_sudan_data[column].fillna(somalia_data[column].values[0], inplace=True)\n",
    "\n",
    "# Combine the data back together\n",
    "filled_dataset = pd.concat([somalia_data, sudan_data, south_sudan_data])\n",
    "\n",
    "# Display the updated dataset\n",
    "print(filled_dataset)\n",
    "missing_values156 = dataset.isnull().sum()\n",
    "print(missing_values156)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "e2e958d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with empty data in Income composition of resources column\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean of \"Income composition of resources\" column for each country\n",
    "mean_income_composition = dataset.groupby('Country')['Income composition of resources'].mean()\n",
    "for country, mean_value in mean_income_composition.items():\n",
    "    dataset.loc[dataset['Country'] == country, 'Income composition of resources'] = dataset.loc[dataset['Country'] == country, 'Income composition of resources'].fillna(mean_value)\n",
    "print(\"Rows with empty data in Income composition of resources column\")\n",
    "filtered_rows1 = dataset[dataset['Income composition of resources'].isnull()]\n",
    "print(filtered_rows1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "158c4a52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with empty data in Schooling column\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n",
      "Rows with empty data in Schooling column\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean of \"Schooling\" column for each country\n",
    "mean_schooling = dataset.groupby('Country')['Schooling'].mean()\n",
    "for country, mean_value in mean_schooling.items():\n",
    "    dataset.loc[dataset['Country'] == country, 'Schooling'] = dataset.loc[dataset['Country'] == country, 'Schooling'].fillna(mean_value)   \n",
    "print(\"Rows with empty data in Schooling column\")\n",
    "filtered_rows2 = dataset[dataset['Schooling'].isnull()]\n",
    "print(filtered_rows2)  \n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)\n",
    "mean_schooling = dataset.groupby('Country')['Schooling'].mean()\n",
    "for country, mean_value in mean_schooling.items():\n",
    "    dataset.loc[dataset['Country'] == country, 'Schooling'] = dataset.loc[dataset['Country'] == country, 'Schooling'].fillna(mean_value)   \n",
    "dataset.loc[dataset['Country'] == \"Côte d'Ivoire\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Côte d'Ivoire\", 'Schooling'].fillna(8)\n",
    "dataset.loc[dataset['Country'] == \"Czechia\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Czechia\", 'Schooling'].fillna(13)\n",
    "dataset.loc[dataset['Country'] == \"Democratic People's Republic of Korea\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Democratic People's Republic of Korea\", 'Schooling'].fillna(13)\n",
    "dataset.loc[dataset['Country'] == \"United States of America\", 'Schooling'] = dataset.loc[dataset['Country'] == \"United States of America\", 'Schooling'].fillna(14)\n",
    "dataset.loc[dataset['Country'] == \"Democratic Republic of the Congo\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Democratic Republic of the Congo\", 'Schooling'].fillna(9)\n",
    "dataset.loc[dataset['Country'] == \"United Republic of Tanzania\", 'Schooling'] = dataset.loc[dataset['Country'] == \"United Republic of Tanzania\", 'Schooling'].fillna(7)\n",
    "dataset.loc[dataset['Country'] == \"Republic of Korea\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Republic of Korea\", 'Schooling'].fillna(17)\n",
    "dataset.loc[dataset['Country'] == \"United Kingdom of Great Britain and Northern Ireland\", 'Schooling'] = dataset.loc[dataset['Country'] == \"United Kingdom of Great Britain and Northern Ireland\", 'Schooling'].fillna(14)\n",
    "dataset.loc[dataset['Country'] == \"Republic of Moldova\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Republic of Moldova\", 'Schooling'].fillna(11.8)\n",
    "dataset.loc[dataset['Country'] == \"Somalia\", 'Schooling'] = dataset.loc[dataset['Country'] == \"Somalia\", 'Schooling'].fillna(1.7)\n",
    "print(\"Rows with empty data in Schooling column\")\n",
    "\n",
    "filtered_rows2 = dataset[dataset['Schooling'].isnull()]\n",
    "\n",
    "print(filtered_rows2)  \n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "9986e69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with empty data in Income composition of resources column\n",
      "Empty DataFrame\n",
      "Columns: [Country, Year, Status, Life expectancy , Adult Mortality, infant deaths, Alcohol, percentage expenditure, Hepatitis B, Measles ,  BMI , under-five deaths , Polio, Total expenditure, Diphtheria ,  HIV/AIDS, GDP, Population,  thinness  1-19 years,  thinness 5-9 years, Income composition of resources, Schooling]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 22 columns]\n",
      "Country                              0\n",
      "Year                                 0\n",
      "Status                               0\n",
      "Life expectancy                      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                         640\n",
      " thinness  1-19 years                0\n",
      " thinness 5-9 years                  0\n",
      "Income composition of resources      0\n",
      "Schooling                          160\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean of \"Income composition of resources\" column for each country\n",
    "mean_income_composition = dataset.groupby('Country')['Income composition of resources'].transform('mean')\n",
    "\n",
    "# Fill missing values with the corresponding mean value for each country\n",
    "dataset['Income composition of resources'] = dataset['Income composition of resources'].fillna(mean_income_composition)\n",
    "dataset.loc[dataset['Country'] == \"Côte d'Ivoire\", 'Income composition of resources'] = dataset.loc[dataset['Country'] == \"Côte d'Ivoire\", 'Income composition of resources'].fillna(0.4)\n",
    "dataset.loc[dataset['Country'] == \"Czechia\", 'Income composition of resources'] = dataset.loc[dataset['Country'] == \"Czechia\", 'Income composition of resources'].fillna(0.8)\n",
    "dataset['Income composition of resources'] = dataset['Income composition of resources'].fillna(0.9)\n",
    "# Print rows with empty data in \"Income composition of resources\" column\n",
    "print(\"Rows with empty data in Income composition of resources column\")\n",
    "filtered_rows1 = dataset[dataset['Income composition of resources'].isnull()]\n",
    "print(filtered_rows1)\n",
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "4a74a7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country                            0\n",
      "Year                               0\n",
      "Status                             0\n",
      "Life expectancy                    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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values = dataset.isnull().sum()\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "94ec0ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n#Method 2: (Target Encodeing)\\nfrom category_encoders import TargetEncoder\\n \\ntargetEncoder = TargetEncoder()\\n \\nX.iloc[:,0] = targetEncoder.fit_transform(X.iloc[:,0], y)\\n \\nX.loc[:,\"Country\"] = labelEncoder.fit_transform(X.loc[:,\"Country\"])\\nX\\n'"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "'''\n",
    "X=dataset.loc[:,dataset.columns != \"Life expectancy \"].values\n",
    "y=dataset.loc[:,dataset.columns == \"Life expectancy \"].values\n",
    "'''\n",
    "X = dataset.drop(columns=['Life expectancy '],axis=1)\n",
    "y = dataset['Life expectancy ']\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "labelEncoder =LabelEncoder()\n",
    "labelEncoderYear =LabelEncoder()\n",
    "\n",
    "X.iloc[:,2] = labelEncoder.fit_transform(X.iloc[:,2])\n",
    "X.iloc[:,1] = labelEncoderYear.fit_transform(X.iloc[:,1])\n",
    "\n",
    "#Method 2: (Target Encodeing)\n",
    "from category_encoders import TargetEncoder\n",
    " \n",
    "targetEncoder = TargetEncoder()\n",
    " \n",
    "X.iloc[:,0] = targetEncoder.fit_transform(X.iloc[:,0], y)\n",
    " \n",
    "X.loc[:,\"Country\"] = labelEncoder.fit_transform(X.loc[:,\"Country\"])\n",
    "X\n",
    "\n",
    "\n",
    "#Spliting data to 4, train, test\n",
    "#X_train,X_test,y_train,y_test = train_test_split(X,y, test_size= .2, random_state=1)\n",
    "X.iloc[:,1]\n",
    "X.nunique()\n",
    "X\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X , y ,test_size=0.2 , random_state=1) #random state takes 0 ,1 or 42 but they say 42 give best results we use random state to force code to take the same random sample each time\n",
    "#We test models with X_test values so when we run the code we will have y as a result\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fa1e4d95",
   "metadata": {},
   "source": [
    "Resources:\n",
    "https://en.wikipedia.org/wiki/Health_in_North_Korea#:~:text=The%20three%20major%20causes%20of,of%20fifteen%20cigarettes%20per%20day.\n",
    "https://tradingeconomics.com/denmark/immunization-pol3-percent-of-one-year-old-children-wb-data.html\n",
    "https://fr.wikipedia.org/wiki/Saint-Marin\n",
    "https://en.wikipedia.org/wiki/List_of_sovereign_states_by_body_mass_index\n",
    "https://www.theglobaleconomy.com/Japan/hepatitis_B_immunization_rate/\n",
    "https://www.indexmundi.com/san_marino/demographics_profile.html\n",
    "https://en.wikipedia.org/wiki/Demographics_of_Palau\n",
    "https://data.worldbank.org/indicator/SP.DYN.AMRT.MAlocations=PW\n",
    "https://getinthepicture.org/sites/default/files/resources/Niue_2012_2016_Vital_Statistics_Report.pdf\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}