data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.optimize import minimize\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Gini coefficient 1990</th>\n",
       "      <th>Gini coefficient 1991</th>\n",
       "      <th>Gini coefficient 1992</th>\n",
       "      <th>Gini coefficient 1993</th>\n",
       "      <th>Gini coefficient 1994</th>\n",
       "      <th>Gini coefficient 1995</th>\n",
       "      <th>Gini coefficient 1996</th>\n",
       "      <th>Gini coefficient 1997</th>\n",
       "      <th>Gini coefficient 1998</th>\n",
       "      <th>...</th>\n",
       "      <th>2013 Mortality</th>\n",
       "      <th>2014 Mortality</th>\n",
       "      <th>2015 Mortality</th>\n",
       "      <th>2016 Mortality</th>\n",
       "      <th>2017 Mortality</th>\n",
       "      <th>2018 Mortality</th>\n",
       "      <th>2019 Mortality</th>\n",
       "      <th>2020 Mortality</th>\n",
       "      <th>2021 Mortality</th>\n",
       "      <th>2022 Mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>0.270103</td>\n",
       "      <td>...</td>\n",
       "      <td>7.746357</td>\n",
       "      <td>7.329462</td>\n",
       "      <td>6.910799</td>\n",
       "      <td>6.520890</td>\n",
       "      <td>6.182847</td>\n",
       "      <td>5.907073</td>\n",
       "      <td>5.690909</td>\n",
       "      <td>5.515814</td>\n",
       "      <td>5.376167</td>\n",
       "      <td>5.252144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>0.353301</td>\n",
       "      <td>...</td>\n",
       "      <td>10.237530</td>\n",
       "      <td>10.161968</td>\n",
       "      <td>10.027424</td>\n",
       "      <td>9.839101</td>\n",
       "      <td>9.604716</td>\n",
       "      <td>9.322065</td>\n",
       "      <td>8.982225</td>\n",
       "      <td>8.604674</td>\n",
       "      <td>8.241613</td>\n",
       "      <td>7.953160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>0.519447</td>\n",
       "      <td>...</td>\n",
       "      <td>58.998576</td>\n",
       "      <td>56.554353</td>\n",
       "      <td>54.426747</td>\n",
       "      <td>52.619912</td>\n",
       "      <td>50.929586</td>\n",
       "      <td>49.409288</td>\n",
       "      <td>47.997828</td>\n",
       "      <td>46.713510</td>\n",
       "      <td>45.551972</td>\n",
       "      <td>44.449184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>...</td>\n",
       "      <td>6.606133</td>\n",
       "      <td>7.062106</td>\n",
       "      <td>7.966167</td>\n",
       "      <td>8.507257</td>\n",
       "      <td>8.112138</td>\n",
       "      <td>7.307098</td>\n",
       "      <td>6.660999</td>\n",
       "      <td>6.572645</td>\n",
       "      <td>7.533118</td>\n",
       "      <td>11.212091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>0.325786</td>\n",
       "      <td>...</td>\n",
       "      <td>4.572380</td>\n",
       "      <td>4.477610</td>\n",
       "      <td>4.424821</td>\n",
       "      <td>4.416563</td>\n",
       "      <td>4.448280</td>\n",
       "      <td>4.496637</td>\n",
       "      <td>4.534294</td>\n",
       "      <td>4.541600</td>\n",
       "      <td>4.505020</td>\n",
       "      <td>4.419226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 342 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Gini coefficient 1990  Gini coefficient 1991  \\\n",
       "0    Albania               0.270103               0.270103   \n",
       "1    Algeria               0.353301               0.353301   \n",
       "2     Angola               0.519447               0.519447   \n",
       "3    Armenia               0.362197               0.362197   \n",
       "4  Australia               0.325786               0.325786   \n",
       "\n",
       "   Gini coefficient 1992  Gini coefficient 1993  Gini coefficient 1994  \\\n",
       "0               0.270103               0.270103               0.270103   \n",
       "1               0.353301               0.353301               0.353301   \n",
       "2               0.519447               0.519447               0.519447   \n",
       "3               0.362197               0.362197               0.362197   \n",
       "4               0.325786               0.325786               0.325786   \n",
       "\n",
       "   Gini coefficient 1995  Gini coefficient 1996  Gini coefficient 1997  \\\n",
       "0               0.270103               0.270103               0.270103   \n",
       "1               0.353301               0.353301               0.353301   \n",
       "2               0.519447               0.519447               0.519447   \n",
       "3               0.362197               0.362197               0.362197   \n",
       "4               0.325786               0.325786               0.325786   \n",
       "\n",
       "   Gini coefficient 1998  ...  2013 Mortality  2014 Mortality  2015 Mortality  \\\n",
       "0               0.270103  ...        7.746357        7.329462        6.910799   \n",
       "1               0.353301  ...       10.237530       10.161968       10.027424   \n",
       "2               0.519447  ...       58.998576       56.554353       54.426747   \n",
       "3               0.362197  ...        6.606133        7.062106        7.966167   \n",
       "4               0.325786  ...        4.572380        4.477610        4.424821   \n",
       "\n",
       "   2016 Mortality  2017 Mortality  2018 Mortality  2019 Mortality  \\\n",
       "0        6.520890        6.182847        5.907073        5.690909   \n",
       "1        9.839101        9.604716        9.322065        8.982225   \n",
       "2       52.619912       50.929586       49.409288       47.997828   \n",
       "3        8.507257        8.112138        7.307098        6.660999   \n",
       "4        4.416563        4.448280        4.496637        4.534294   \n",
       "\n",
       "   2020 Mortality  2021 Mortality  2022 Mortality  \n",
       "0        5.515814        5.376167        5.252144  \n",
       "1        8.604674        8.241613        7.953160  \n",
       "2       46.713510       45.551972       44.449184  \n",
       "3        6.572645        7.533118       11.212091  \n",
       "4        4.541600        4.505020        4.419226  \n",
       "\n",
       "[5 rows x 342 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./datasets/combined_df_wo_infra.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_data = pd.DataFrame()\n",
    "recent_data['Country'] = df['Country']\n",
    "\n",
    "# Select the most recent non-null value for each feature\n",
    "recent_data['Gini'] = df['Gini coefficient 2022']\n",
    "recent_data['GDP_per_capita'] = df['GDP per Capita 2022']\n",
    "recent_data['Expected_School_Years'] = df['Expected Years of Schooling (2021)']\n",
    "recent_data['Mean_School_Years'] = df['Mean Years of Schooling (2021)']\n",
    "recent_data['Literacy_Rate'] = df['Literacy rate 2022']\n",
    "recent_data['Internet_Usage'] = df['Internet Usage (% of Population) 2022']\n",
    "recent_data['CO2_per_capita'] = df['2023 CO2 per capita']  # Using the first CO2 column\n",
    "recent_data['CO2_per_capita_2'] = df['2023 CO2 per capita #2']  # Using the second CO2 column\n",
    "recent_data['Energy'] = df['2021 Energy']\n",
    "recent_data['Mortality_2022'] = df['2022 Mortality']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Gini</th>\n",
       "      <th>GDP_per_capita</th>\n",
       "      <th>Expected_School_Years</th>\n",
       "      <th>Mean_School_Years</th>\n",
       "      <th>Literacy_Rate</th>\n",
       "      <th>Internet_Usage</th>\n",
       "      <th>CO2_per_capita</th>\n",
       "      <th>CO2_per_capita_2</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Mortality_2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>0.294196</td>\n",
       "      <td>5154.827498</td>\n",
       "      <td>14.448000</td>\n",
       "      <td>11.286455</td>\n",
       "      <td>98.50000</td>\n",
       "      <td>82.6137</td>\n",
       "      <td>1.830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.973814e-01</td>\n",
       "      <td>5.252144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>0.276157</td>\n",
       "      <td>4544.466881</td>\n",
       "      <td>14.626896</td>\n",
       "      <td>8.069284</td>\n",
       "      <td>81.40784</td>\n",
       "      <td>71.2432</td>\n",
       "      <td>3.859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.278755e+01</td>\n",
       "      <td>7.953160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.512640</td>\n",
       "      <td>2382.022640</td>\n",
       "      <td>12.172100</td>\n",
       "      <td>5.417391</td>\n",
       "      <td>72.40000</td>\n",
       "      <td>39.2935</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.253067e+01</td>\n",
       "      <td>44.449184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>0.279453</td>\n",
       "      <td>4791.004817</td>\n",
       "      <td>13.116760</td>\n",
       "      <td>11.330300</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>77.0277</td>\n",
       "      <td>2.585</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.214619e-09</td>\n",
       "      <td>11.212091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>0.343326</td>\n",
       "      <td>61009.807715</td>\n",
       "      <td>21.054590</td>\n",
       "      <td>12.726820</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>94.8762</td>\n",
       "      <td>14.478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.631391e+00</td>\n",
       "      <td>4.419226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0.405517</td>\n",
       "      <td>18213.695614</td>\n",
       "      <td>16.832600</td>\n",
       "      <td>8.982550</td>\n",
       "      <td>99.00000</td>\n",
       "      <td>89.8730</td>\n",
       "      <td>2.322</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.932978e-03</td>\n",
       "      <td>11.056742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>0.311848</td>\n",
       "      <td>3576.715527</td>\n",
       "      <td>12.477140</td>\n",
       "      <td>11.895590</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>83.9000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.438090e+00</td>\n",
       "      <td>10.435145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>0.323176</td>\n",
       "      <td>2631.551863</td>\n",
       "      <td>11.535315</td>\n",
       "      <td>7.064846</td>\n",
       "      <td>89.10000</td>\n",
       "      <td>69.8888</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>13.557602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.514831</td>\n",
       "      <td>1298.848050</td>\n",
       "      <td>10.928760</td>\n",
       "      <td>7.187091</td>\n",
       "      <td>87.50000</td>\n",
       "      <td>31.2342</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.307122e-02</td>\n",
       "      <td>30.978062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.502564</td>\n",
       "      <td>1361.914530</td>\n",
       "      <td>12.110969</td>\n",
       "      <td>8.710909</td>\n",
       "      <td>89.85000</td>\n",
       "      <td>32.5615</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.130469e-01</td>\n",
       "      <td>36.584246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country      Gini  GDP_per_capita  Expected_School_Years  \\\n",
       "0       Albania  0.294196     5154.827498              14.448000   \n",
       "1       Algeria  0.276157     4544.466881              14.626896   \n",
       "2        Angola  0.512640     2382.022640              12.172100   \n",
       "3       Armenia  0.279453     4791.004817              13.116760   \n",
       "4     Australia  0.343326    61009.807715              21.054590   \n",
       "..          ...       ...             ...                    ...   \n",
       "135     Uruguay  0.405517    18213.695614              16.832600   \n",
       "136  Uzbekistan  0.311848     3576.715527              12.477140   \n",
       "137     Vanuatu  0.323176     2631.551863              11.535315   \n",
       "138      Zambia  0.514831     1298.848050              10.928760   \n",
       "139    Zimbabwe  0.502564     1361.914530              12.110969   \n",
       "\n",
       "     Mean_School_Years  Literacy_Rate  Internet_Usage  CO2_per_capita  \\\n",
       "0            11.286455       98.50000         82.6137           1.830   \n",
       "1             8.069284       81.40784         71.2432           3.859   \n",
       "2             5.417391       72.40000         39.2935           0.565   \n",
       "3            11.330300      100.00000         77.0277           2.585   \n",
       "4            12.726820       99.00000         94.8762          14.478   \n",
       "..                 ...            ...             ...             ...   \n",
       "135           8.982550       99.00000         89.8730           2.322   \n",
       "136          11.895590      100.00000         83.9000           3.500   \n",
       "137           7.064846       89.10000         69.8888           0.706   \n",
       "138           7.187091       87.50000         31.2342           0.374   \n",
       "139           8.710909       89.85000         32.5615           0.683   \n",
       "\n",
       "     CO2_per_capita_2        Energy  Mortality_2022  \n",
       "0                 0.0  5.973814e-01        5.252144  \n",
       "1                 0.0  1.278755e+01        7.953160  \n",
       "2                 0.0  2.253067e+01       44.449184  \n",
       "3                 0.0  8.214619e-09       11.212091  \n",
       "4                 0.0  1.631391e+00        4.419226  \n",
       "..                ...           ...             ...  \n",
       "135               0.0  8.932978e-03       11.056742  \n",
       "136               0.0  8.438090e+00       10.435145  \n",
       "137               0.0  0.000000e+00       13.557602  \n",
       "138               0.0  2.307122e-02       30.978062  \n",
       "139               0.0  1.130469e-01       36.584246  \n",
       "\n",
       "[128 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_data = recent_data.dropna()\n",
    "recent_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = recent_data.drop(columns=['Country', 'Mortality_2022'])\n",
    "y = recent_data['Mortality_2022']\n",
    "# Split the data into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest Model Performance Metrics:\n",
      "R² Score: 0.6372\n",
      "Mean Squared Error: 59.6889\n",
      "Root Mean Squared Error: 7.7259\n",
      "Mean Absolute Error: 4.8515\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.inspection import permutation_importance\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "sort = rf_model.feature_importances_.argsort()\n",
    "plt.barh(recent_data.columns[sort], rf_model.feature_importances_[sort])\n",
    "plt.xlabel(\"Feature Importance\")\n",
    "plt.show()\n",
    "\n",
    "# Test random forest model accuracy\n",
    "y_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Calculate and print various metrics\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print(\"\\nRandom Forest Model Performance Metrics:\")\n",
    "print(f\"R² Score: {r2:.4f}\")\n",
    "print(f\"Mean Squared Error: {mse:.4f}\")\n",
    "print(f\"Root Mean Squared Error: {rmse:.4f}\")\n",
    "print(f\"Mean Absolute Error: {mae:.4f}\")\n",
    "\n",
    "# Optional: Plot predicted vs actual values\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "plt.xlabel('Actual Mortality Rate')\n",
    "plt.ylabel('Predicted Mortality Rate')\n",
    "plt.title('Random Forest: Predicted vs Actual Mortality Rates')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Contributions to Mortality Rate:\n",
      "Negative coefficients indicate features that reduce mortality\n",
      "Positive coefficients indicate features that increase mortality\n",
      "\n",
      "Ranked from strongest mortality-reducing to strongest mortality-increasing factors:\n",
      "Literacy_Rate: -6.9711 (reduces mortality)\n",
      "Internet_Usage: -4.8904 (reduces mortality)\n",
      "Expected_School_Years: -0.5371 (reduces mortality)\n",
      "GDP_per_capita: -0.1438 (reduces mortality)\n",
      "Mean_School_Years: -0.1428 (reduces mortality)\n",
      "CO2_per_capita_2: 0.0000 (reduces mortality)\n",
      "CO2_per_capita: 0.2402 (increases mortality)\n",
      "Energy: 0.9326 (increases mortality)\n",
      "Gini: 2.8063 (increases mortality)\n",
      "\n",
      "Model R² Score: 0.7983\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled = pd.DataFrame(X_scaled, columns=X.columns)\n",
    "\n",
    "# Fit linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X_scaled, y)\n",
    "\n",
    "# Create DataFrame with actual coefficients (not absolute values)\n",
    "feature_contribution = pd.DataFrame({\n",
    "    'Feature': X.columns,\n",
    "    'Coefficient': model.coef_\n",
    "})\n",
    "feature_contribution = feature_contribution.sort_values('Coefficient', ascending=True)  # Sort from most negative to most positive\n",
    "\n",
    "# Create a horizontal bar plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "colors = ['red' if x < 0 else 'green' for x in feature_contribution['Coefficient']]\n",
    "plt.barh(feature_contribution['Feature'], feature_contribution['Coefficient'], color=colors)\n",
    "plt.axvline(x=0, color='black', linestyle='-', linewidth=0.5)\n",
    "plt.title('Feature Contribution to Mortality Rate')\n",
    "plt.xlabel('Coefficient Value (Negative = Reduces Mortality, Positive = Increases Mortality)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print feature contributions\n",
    "print(\"\\nFeature Contributions to Mortality Rate:\")\n",
    "print(\"Negative coefficients indicate features that reduce mortality\")\n",
    "print(\"Positive coefficients indicate features that increase mortality\")\n",
    "print(\"\\nRanked from strongest mortality-reducing to strongest mortality-increasing factors:\")\n",
    "for idx, row in feature_contribution.iterrows():\n",
    "    direction = \"increases\" if row['Coefficient'] > 0 else \"reduces\"\n",
    "    print(f\"{row['Feature']}: {row['Coefficient']:.4f} ({direction} mortality)\")\n",
    "\n",
    "# Print model performance\n",
    "print(f\"\\nModel R² Score: {model.score(X_scaled, y):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(weights):\n",
    "    # Calculate the composite development index (CDI) by taking dot product of \n",
    "    # scaled features (X_scaled) and weights\n",
    "    cdi = np.dot(X_scaled, weights)\n",
    "    \n",
    "    # Calculate Pearson correlation coefficient between CDI and mortality rate (y)\n",
    "    # np.corrcoef returns correlation matrix, [0,1] gets correlation between variables\n",
    "    correlation = np.corrcoef(cdi, y)[0, 1]\n",
    "    \n",
    "    # Return negative absolute correlation since optimization algorithms minimize\n",
    "    # and we want to maximize correlation magnitude\n",
    "    return -abs(correlation)\n",
    "\n",
    "# Function to ensure weights sum to 1\n",
    "def constraint(weights):\n",
    "    return np.sum(weights) - 1\n",
    "\n",
    "# Initial weights (equal weighting)\n",
    "initial_weights = np.ones(len(X.columns)) / len(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled_np = X_scaled.values\n",
    "y_np = y.values\n",
    "initial_weights = np.ones(X_scaled_np.shape[1]) / X_scaled_np.shape[1]\n",
    "def jacobian(weights):\n",
    "    cdi = np.dot(X_scaled_np, weights)\n",
    "    \n",
    "    # Calculate the gradient of correlation coefficient\n",
    "    n = len(y_np)\n",
    "    mean_cdi = np.mean(cdi)\n",
    "    mean_y = np.mean(y_np)\n",
    "    \n",
    "    # Compute standard deviations\n",
    "    std_cdi = np.std(cdi)\n",
    "    std_y = np.std(y_np)\n",
    "    \n",
    "    # Gradient of correlation coefficient\n",
    "    grad = np.zeros(len(weights))\n",
    "    for i in range(len(weights)):\n",
    "        # Derivative of correlation coefficient with respect to weight i\n",
    "        dcdi_dwi = X_scaled_np[:, i]\n",
    "        \n",
    "        # Chain rule for correlation coefficient\n",
    "        grad[i] = np.sum((y_np - mean_y) * dcdi_dwi) / (n * std_cdi * std_y) - \\\n",
    "                 np.sum((cdi - mean_cdi) * (y_np - mean_y)) * np.sum(dcdi_dwi * (cdi - mean_cdi)) / \\\n",
    "                 (n * n * std_cdi**3 * std_y)\n",
    "    \n",
    "    # Negative because we're minimizing\n",
    "    return -grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Weights:\n",
      "Gini: -0.5041\n",
      "GDP_per_capita: 0.0259\n",
      "Expected_School_Years: 0.0964\n",
      "Mean_School_Years: 0.0257\n",
      "Literacy_Rate: 1.2521\n",
      "Internet_Usage: 0.8784\n",
      "CO2_per_capita: -0.0432\n",
      "CO2_per_capita_2: 0.1111\n",
      "Energy: -0.1675\n",
      "\n",
      "Final CDI: 1.8726\n",
      "Average Correlation between CDI and Mortality: -0.8935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/31/w2zt100d46vbd05gsww_hqj00000gn/T/ipykernel_98143/165252814.py:17: RuntimeWarning: Method CG cannot handle constraints.\n",
      "  result = minimize(objective, initial_weights, bounds=bounds, constraints=constraints, method='CG')\n",
      "/var/folders/31/w2zt100d46vbd05gsww_hqj00000gn/T/ipykernel_98143/165252814.py:17: RuntimeWarning: Method CG cannot handle bounds.\n",
      "  result = minimize(objective, initial_weights, bounds=bounds, constraints=constraints, method='CG')\n"
     ]
    }
   ],
   "source": [
    "constraints = ({'type': 'eq', 'fun': constraint})\n",
    "bounds = [(0, 1) for _ in range(len(X.columns))]\n",
    "\n",
    "# Perform optimization\n",
    "# Nelder-Mead = -0.8723\n",
    "# Powell = -0.8724\n",
    "# SLSQP = -0.8724\n",
    "# CG = -0.8935\n",
    "# BFGS = -0.8935\n",
    "# L-BFGS-B = -0.8724\n",
    "# TNC = -0.8724\n",
    "# trust-constr = -0.8724\n",
    "# Newton-cg = -0.7447\n",
    "\n",
    "# Perform optimization using Newton-CG with explicit Jacobian\n",
    "result = minimize(objective, initial_weights, bounds=bounds, constraints=constraints, method='CG')\n",
    "\n",
    "# Get optimal weights\n",
    "optimal_weights = result.x\n",
    "\n",
    "# Calculate final CDI\n",
    "cdi = np.dot(X_scaled, optimal_weights)\n",
    "\n",
    "# Print the optimal weights\n",
    "print(\"Optimal Weights:\")\n",
    "for feature, weight in zip(X.columns, optimal_weights):\n",
    "    print(f\"{feature}: {weight:.4f}\")\n",
    "\n",
    "# Print the final CDI\n",
    "print(f\"\\nFinal CDI: {cdi[0]:.4f}\")\n",
    "cdi \n",
    "# Average correlation between cdi and mortality\n",
    "correlation = np.corrcoef(cdi, y)[0, 1]\n",
    "print(f\"Average Correlation between CDI and Mortality: {correlation:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between CDI and Mortality: -0.8935\n"
     ]
    }
   ],
   "source": [
    "# get correlation between cdi and mortality\n",
    "correlation = np.corrcoef(cdi, y)[0, 1]\n",
    "print(f\"Correlation between CDI and Mortality: {correlation:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between HDI and Mortality: -0.8604\n"
     ]
    }
   ],
   "source": [
    "# get correlation between hdi and mortality\n",
    "correlation = np.corrcoef(df['Human Development Index (2021)'], df['2022 Mortality'])[0, 1]\n",
    "print(f\"Correlation between HDI and Mortality: {correlation:.4f}\")"
   ]
  }
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