final_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal is to predict or classify Life Expectancy\n",
    "- We want to first determine which variables are useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/68/886mn3q57q9f6lcdshz9b8j80000gn/T/ipykernel_71181/102512377.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>65.0000</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>62</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>71.2796</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0000</td>\n",
       "      <td>8.1600</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>584.2592</td>\n",
       "      <td>33736494.0000</td>\n",
       "      <td>17.2000</td>\n",
       "      <td>17.3000</td>\n",
       "      <td>0.4790</td>\n",
       "      <td>10.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9000</td>\n",
       "      <td>271.0000</td>\n",
       "      <td>64</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>73.5236</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>8.1800</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>612.6965</td>\n",
       "      <td>327582.0000</td>\n",
       "      <td>17.5000</td>\n",
       "      <td>17.5000</td>\n",
       "      <td>0.4760</td>\n",
       "      <td>10.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9000</td>\n",
       "      <td>268.0000</td>\n",
       "      <td>66</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>73.2192</td>\n",
       "      <td>64.0000</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0000</td>\n",
       "      <td>8.1300</td>\n",
       "      <td>64.0000</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>631.7450</td>\n",
       "      <td>31731688.0000</td>\n",
       "      <td>17.7000</td>\n",
       "      <td>17.7000</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>9.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5000</td>\n",
       "      <td>272.0000</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>78.1842</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>8.5200</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>669.9590</td>\n",
       "      <td>3696958.0000</td>\n",
       "      <td>17.9000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>0.4630</td>\n",
       "      <td>9.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2000</td>\n",
       "      <td>275.0000</td>\n",
       "      <td>71</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>7.0971</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>7.8700</td>\n",
       "      <td>68.0000</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>63.5372</td>\n",
       "      <td>2978599.0000</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>0.4540</td>\n",
       "      <td>9.5000</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          65.0000         263.0000   \n",
       "1  Afghanistan  2014  Developing          59.9000         271.0000   \n",
       "2  Afghanistan  2013  Developing          59.9000         268.0000   \n",
       "3  Afghanistan  2012  Developing          59.5000         272.0000   \n",
       "4  Afghanistan  2011  Developing          59.2000         275.0000   \n",
       "\n",
       "   infant deaths  Alcohol  percentage expenditure  Hepatitis B  Measles  ...  \\\n",
       "0             62   0.0100                 71.2796      65.0000     1154  ...   \n",
       "1             64   0.0100                 73.5236      62.0000      492  ...   \n",
       "2             66   0.0100                 73.2192      64.0000      430  ...   \n",
       "3             69   0.0100                 78.1842      67.0000     2787  ...   \n",
       "4             71   0.0100                  7.0971      68.0000     3013  ...   \n",
       "\n",
       "    Polio  Total expenditure  Diphtheria  HIV/AIDS      GDP    Population  \\\n",
       "0  6.0000             8.1600     65.0000    0.1000 584.2592 33736494.0000   \n",
       "1 58.0000             8.1800     62.0000    0.1000 612.6965   327582.0000   \n",
       "2 62.0000             8.1300     64.0000    0.1000 631.7450 31731688.0000   \n",
       "3 67.0000             8.5200     67.0000    0.1000 669.9590  3696958.0000   \n",
       "4 68.0000             7.8700     68.0000    0.1000  63.5372  2978599.0000   \n",
       "\n",
       "   thinness 1-19 years  thinness 5-9 years  Income composition of resources  \\\n",
       "0              17.2000             17.3000                           0.4790   \n",
       "1              17.5000             17.5000                           0.4760   \n",
       "2              17.7000             17.7000                           0.4700   \n",
       "3              17.9000             18.0000                           0.4630   \n",
       "4              18.2000             18.2000                           0.4540   \n",
       "\n",
       "   Schooling  \n",
       "0    10.1000  \n",
       "1    10.0000  \n",
       "2     9.9000  \n",
       "3     9.8000  \n",
       "4     9.5000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from statsmodels.tsa.stattools import ccf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import seaborn as sns\n",
    "import random\n",
    "import os\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "path = \"/Users/rishabtirupathi/Desktop/UIUC/Spring 2024/STAT 432/Final Project\"\n",
    "image_path = path + \"/Images\"\n",
    "data_path = path + \"/Data\"\n",
    "\n",
    "data_raw = pd.read_csv(f\"{data_path}/Life Expectancy Data.csv\")\n",
    "country_list = sorted(list(set((data_raw['Country']))))\n",
    "data_raw.columns = [i.strip() for i in data_raw.columns]\n",
    "\n",
    "colours = random.sample(list(mcolors.XKCD_COLORS.keys()), len(country_list))\n",
    "\n",
    "data_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preprocessing\n",
    "- Determine any missing data, linearly interpolate them\n",
    "- Use correlation analysis to determine which features to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/68/886mn3q57q9f6lcdshz9b8j80000gn/T/ipykernel_71181/29492681.py:8: FutureWarning: DataFrame.interpolate with object dtype is deprecated and will raise in a future version. Call obj.infer_objects(copy=False) before interpolating instead.\n",
      "  df = df.interpolate(method = 'linear')\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Column</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adult Mortality</td>\n",
       "      <td>-0.6957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMI</td>\n",
       "      <td>0.5564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HIV/AIDS</td>\n",
       "      <td>-0.5559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Income composition of resources</td>\n",
       "      <td>0.6909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Schooling</td>\n",
       "      <td>0.7043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Column  Correlation\n",
       "0                  Adult Mortality      -0.6957\n",
       "1                              BMI       0.5564\n",
       "2                         HIV/AIDS      -0.5559\n",
       "3  Income composition of resources       0.6909\n",
       "4                        Schooling       0.7043"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data_raw.copy()\n",
    "\n",
    "## Make categorical variables in quantitative\n",
    "df['Country_num'] = df['Country'].apply(lambda x: country_list.index(x) + 1)\n",
    "df['Status_num'] = df['Status'].apply(lambda x: 0 if x == 'Developing' else 1)\n",
    "df['Colour'] = df['Country'].apply(lambda x: colours[country_list.index(x)])\n",
    "\n",
    "df = df.interpolate(method = 'linear')\n",
    "\n",
    "# df.to_csv(f\"{data_path}/Interpolated WHO Data.csv\")\n",
    "\n",
    "corr_df = df.drop(columns=['Country', 'Status', 'Colour'])\n",
    "corr_df = corr_df.rename(columns = {\"Life expectancy\": \"LE\", \\\n",
    "                                    \"Income composition of resources\": \"ICR\",\n",
    "                                    \"percentage expenditure\": \"PE\", \\\n",
    "                                    \"Hepatitis B\": \"HepB\", \\\n",
    "                                    \"under-five deaths\": \"<5 death\", \\\n",
    "                                    \"Total expenditure\": \"TE\", \\\n",
    "                                    'thinness 1-19 years': \"1-19 thin\", \\\n",
    "                                    'thinness 5-9 years': \"5-9 thin\"}).corr()\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (10, 7))\n",
    "sns.heatmap(corr_df, cmap='coolwarm')\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "plt.savefig(f\"{image_path}/Correlation Heatmap\")\n",
    "plt.show()\n",
    "\n",
    "# ## Consider the values which have |r| high enough (arbitrary)\n",
    "corr_df = df.drop(columns=['Country', 'Status', 'Colour']).corr()\n",
    "life_expectancy_corr = pd.DataFrame({\"Column\": corr_df.loc[\"Life expectancy\"].index, \"Correlation\": corr_df.loc[\"Life expectancy\"].values})\n",
    "life_exp_var = life_expectancy_corr[abs(life_expectancy_corr['Correlation']) >= 0.5]\n",
    "life_exp_var = life_exp_var[life_exp_var['Column'] != 'Life expectancy'].reset_index(drop = True)\n",
    "pred = list(life_exp_var['Column'])\n",
    "life_exp_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create scatter plot to visualise data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trunc_df = df.copy()\n",
    "trunc_df['Mean LE'] = trunc_df.groupby(by = 'Country')['Life expectancy'].transform('mean')\n",
    "trunc_df = trunc_df[['Country', 'Mean LE', 'Colour']].drop_duplicates().reset_index(drop = True)\n",
    "key_countries = ['United States of America', 'United Kingdom of Great Britain and Northern Ireland', 'Singapore', 'India', 'China']\n",
    "max_mle = max(trunc_df['Mean LE'])\n",
    "min_mle = min(trunc_df['Mean LE'])\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (20, 12))\n",
    "for country, mean_le, colour in zip(trunc_df['Country'], trunc_df['Mean LE'], trunc_df['Colour']):\n",
    "    axes.scatter(country, mean_le, c = colour, label = country)\n",
    "\n",
    "    if country in key_countries or mean_le in [max_mle, min_mle]:\n",
    "        plt.text(country, mean_le, country, va = 'bottom')\n",
    "\n",
    "axes.set_xticks([])\n",
    "axes.set_ylabel(\"Mean Life Expectancy\")\n",
    "axes.hlines(y = trunc_df['Mean LE'].median(), xmin = 0, xmax = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describe life expectancy to determine how to class it\n",
    "- Mean is less than median, so left skew,\n",
    "- Large enough to say roughly normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count   2938.0000\n",
       "mean      69.2149\n",
       "std        9.5108\n",
       "min       36.3000\n",
       "25%       63.1000\n",
       "50%       72.0000\n",
       "75%       75.6000\n",
       "max       89.0000\n",
       "Name: Life expectancy, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.hist(df['Life expectancy'], color='royalblue')\n",
    "plt.xlabel(\"Life Expectancy\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Histogram of Life Expectancy\")\n",
    "plt.vlines(x = df['Life expectancy'].describe()['mean'], ymin = 0, ymax = 850, colors='red', label = 'mean')\n",
    "plt.vlines(x = df['Life expectancy'].describe()['50%'], ymin = 0, ymax = 850, colors='black', label = 'median ')\n",
    "plt.legend()\n",
    "plt.savefig(f\"{image_path}/Histogram of LE\")\n",
    "plt.show()\n",
    "\n",
    "df['Life expectancy'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression\n",
    "- Because Logistic regression is a classification problem, the output needs to be categorical\n",
    "- Encode life expectancy as <33% (66.5) as low, 33 - 67% (66. 5 - 74.4) as medium, >67% (74.4) as high\n",
    "- This uses max as the choice of class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Classification Error = 0.179\n",
      "Train Classification Error = 0.205\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.83      0.82      0.82       197\n",
      "         low       0.95      0.85      0.89       189\n",
      "      medium       0.72      0.80      0.76       202\n",
      "\n",
      "    accuracy                           0.82       588\n",
      "   macro avg       0.83      0.82      0.83       588\n",
      "weighted avg       0.83      0.82      0.82       588\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "model_three_df = df.copy()\n",
    "# fig, axes = plt.subplots(nrows=1, ncols=1, figsize = (6, 4))\n",
    "three_classes = ['high', 'medium', 'low']\n",
    "\n",
    "## Split train and test\n",
    "train_data = pd.read_csv(f\"{data_path}/Training Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "test_data = pd.read_csv(f\"{data_path}/Testing Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "\n",
    "## Bounds for low, medium, high expectancy\n",
    "q1 = df['Life expectancy'].quantile(1/3)\n",
    "q2 = df['Life expectancy'].quantile(2/3)\n",
    "\n",
    "train_data['Life expectancy category'] = train_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else(\"medium\" if x <= q2 else \"high\"))\n",
    "test_data['Life expectancy category'] = test_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else(\"medium\" if x <= q2 else \"high\"))\n",
    "\n",
    "#### Create training and testing df\n",
    "# # train_data, test_data = train_test_split(model_df, train_size=0.8)\n",
    "# # train_data.to_csv(f\"{data_path}/Training Data.csv\")\n",
    "# # test_data.to_csv(f\"{data_path}/Testing Data.csv\")\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_three_train, y_three_train = scaler.fit_transform(train_data[pred]), train_data['Life expectancy category']\n",
    "X_three_test, y_three_test = scaler.fit_transform(test_data[pred]), test_data['Life expectancy category']\n",
    "\n",
    "# Construct model\n",
    "three_log_mod = LogisticRegression(random_state = 4)\n",
    "three_log_mod.fit(X_three_train, y_three_train)\n",
    "y_three_pred = three_log_mod.predict(X_three_test)\n",
    "y_three_train_pred = three_log_mod.predict(X_three_train)\n",
    "\n",
    "## Combine results in df\n",
    "three_test_results = pd.DataFrame({\"Country\": test_data['Country'], \"Year\": test_data['Year'], \"Prediction\": y_three_pred, \"Actual\": y_three_test})\n",
    "three_prob_prediction = three_log_mod.predict_proba(X_three_test)\n",
    "\n",
    "for i in range(len(three_classes)):\n",
    "    three_test_results[f'P({three_classes[i]})'] = three_prob_prediction[:, i]\n",
    "    \n",
    "three_test_conf_matrix = confusion_matrix(y_true = three_test_results['Actual'], y_pred = three_test_results['Prediction'], labels = three_classes)\n",
    "sns.heatmap(three_test_conf_matrix, annot = True, fmt = 'g')\n",
    "\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.title(\"Test Confusion Matrix for Logistic Regression\")\n",
    "plt.xticks(ticks = [0.5, 1.5, 2.5], labels = three_classes)\n",
    "plt.yticks(ticks = [0.5, 1.5, 2.5], labels = three_classes)\n",
    "plt.savefig(f\"{image_path}/Confusion Matrix for 3 class\")\n",
    "\n",
    "# three_row_sum = np.sum(three_test_conf_matrix, axis=1)\n",
    "# three_percent_confusion = three_test_conf_matrix/three_row_sum[:, np.newaxis]\n",
    "# sns.heatmap(three_percent_confusion, annot = True, fmt = 'g', ax=axes[1])\n",
    "\n",
    "# axes[1].set_xlabel(\"Predicted Labels\")\n",
    "# axes[1].set_ylabel(\"Actual Label\")\n",
    "# axes[1].set_title(\"Test Confusion Matrix Percentage Reported\")\n",
    "# axes[1].set_xticks(ticks = [0.5, 1.5, 2.5], labels = three_classes)\n",
    "# axes[1].set_yticks(ticks = [0.5, 1.5, 2.5], labels = three_classes)\n",
    "# plt.show()\n",
    "\n",
    "three_test_classification_error = 1-(sum(np.diag(three_test_conf_matrix))/sum(sum(three_test_conf_matrix)))\n",
    "\n",
    "print(\"Test Classification Error =\", round(1-accuracy_score(y_true = three_test_results['Actual'], y_pred=three_test_results['Prediction']), 3))\n",
    "print(\"Train Classification Error =\", round(1-accuracy_score(y_true = y_three_train, y_pred=y_three_train_pred), 3))\n",
    "\n",
    "## Recall = Sensitivity = TP/(TP + FN), Precision = TP/(TP + FP), F1 score = 2 * Precision * Sensitivity/(Precision + Sensitivity), Support = Row Sum\n",
    "print(classification_report(y_true = y_three_test, y_pred = y_three_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Run the same model with 2 classes\n",
    "- Since the life expectancy median is higher than the mean and the spread is smaller, we consider more than a 50-50 split for high and low\n",
    "- We use a 75-25 high-low split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Classification Error = 0.056\n",
      "Train Classification Error = 0.068\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.94      0.98      0.96       441\n",
      "         low       0.91      0.80      0.86       147\n",
      "\n",
      "    accuracy                           0.93       588\n",
      "   macro avg       0.93      0.89      0.91       588\n",
      "weighted avg       0.93      0.93      0.93       588\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "model_two_df = df.copy()\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize = (18, 4))\n",
    "two_classes = ['high', 'low']\n",
    "\n",
    "## Split train and test\n",
    "train_data = pd.read_csv(f\"{data_path}/Training Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "test_data = pd.read_csv(f\"{data_path}/Testing Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "\n",
    "## Bounds for low, medium, high expectancy\n",
    "q1 = df['Life expectancy'].quantile(1/4)\n",
    "\n",
    "train_data['Life expectancy category'] = train_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else \"high\")\n",
    "test_data['Life expectancy category'] = test_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else \"high\")\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_two_train, y_two_train = scaler.fit_transform(train_data[pred]), train_data['Life expectancy category']\n",
    "X_two_test, y_two_test = scaler.fit_transform(test_data[pred]), test_data['Life expectancy category']\n",
    "\n",
    "# Construct model\n",
    "two_log_mod = LogisticRegression(random_state = 4)\n",
    "two_log_mod.fit(X_two_train, y_two_train)\n",
    "y_two_pred = two_log_mod.predict(X_two_test)\n",
    "y_two_train_pred = two_log_mod.predict(X_two_train)\n",
    "\n",
    "## Combine results in df\n",
    "two_test_results = pd.DataFrame({\"Country\": test_data['Country'], \"Year\": test_data['Year'], \"Prediction\": y_two_pred, \"Actual\": y_two_test})\n",
    "two_prob_prediction = two_log_mod.predict_proba(X_two_test)\n",
    "\n",
    "for i in range(len(two_classes)):\n",
    "    two_test_results[f'P({two_classes[i]})'] = two_prob_prediction[:, i]\n",
    "\n",
    "## Create cutoff probability\n",
    "best_cut = -1\n",
    "best_test_error = 10**7\n",
    "\n",
    "for c in range(0, 10, 1):\n",
    "    cut_off = c/10\n",
    "    two_test_results['Prediction'] = two_test_results.apply(lambda x: \"high\" if x['P(high)'] >= cut_off else \"low\", axis = 1)\n",
    "\n",
    "    test_conf_matrix = confusion_matrix(y_true = two_test_results['Actual'], y_pred = two_test_results['Prediction'], labels = two_classes)\n",
    "    test_classification_error = 1-(sum(np.diag(test_conf_matrix))/sum(sum(test_conf_matrix)))\n",
    "\n",
    "    if test_classification_error < best_test_error: \n",
    "        best_cut = cut_off\n",
    "        best_test_error = test_classification_error\n",
    "\n",
    "# Use best cutoff to determine prediction\n",
    "two_test_results['Prediction'] = two_test_results.apply(lambda x: \"high\" if x['P(high)'] >= best_cut else \"low\", axis = 1)\n",
    "test_conf_matrix = confusion_matrix(y_true = two_test_results['Actual'], y_pred = two_test_results['Prediction'], labels = two_classes)\n",
    "sns.heatmap(test_conf_matrix, annot = True, fmt = 'g', ax=axes[0])\n",
    "\n",
    "axes[0].set_xlabel(\"Predicted Labels\")\n",
    "axes[0].set_ylabel(\"Actual Label\")\n",
    "axes[0].set_title(f\"Test Confusion Matrix for Logistic Regression with cutoff {best_cut}\")\n",
    "axes[0].set_xticks(ticks = [0.5, 1.5], labels = two_classes)\n",
    "axes[0].set_yticks(ticks = [0.5, 1.5], labels = two_classes)\n",
    "\n",
    "row_sum = np.sum(test_conf_matrix, axis=1)\n",
    "percent_confusion = test_conf_matrix/row_sum[:, np.newaxis]\n",
    "sns.heatmap(percent_confusion, annot = True, fmt = 'g', ax=axes[1])\n",
    "\n",
    "axes[1].set_xlabel(\"Predicted Labels\")\n",
    "axes[1].set_ylabel(\"Actual Label\")\n",
    "axes[1].set_title(f\"Test Confusion Matrix Percentage Reported with cutoff {best_cut}\")\n",
    "axes[1].set_xticks(ticks = [0.5, 1.5], labels = two_classes)\n",
    "axes[1].set_yticks(ticks = [0.5, 1.5], labels = two_classes)\n",
    "plt.show()\n",
    "\n",
    "## Calculate metrics\n",
    "## Recall = Sensitivity = TP/(TP + FN), Precision = TP/(TP + FP), \n",
    "## F1 score = 2 * Precision * Sensitivity/(Precision + Sensitivity), Support = Row Sum\n",
    "\n",
    "train_error = round(1-accuracy_score(y_true = y_two_train, y_pred=y_two_train_pred), 3)\n",
    "print(\"Test Classification Error =\", round(best_test_error, 3))\n",
    "print(\"Train Classification Error =\", train_error)\n",
    "print(classification_report(y_true = y_two_test, y_pred = y_two_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Combine the results of the 2-class logistic model\n",
    "- We show the performance for multiple splits and the output for the best cutoff\n",
    "- We assume that the positive class is \"high\"\n",
    "- We get the following results\n",
    "    - Lowest Test Error =  0.0442, 0.9-0.1 split, 0.5 cutoff\n",
    "    \n",
    "    - Highest Sensitivity =  0.9879, 0.3-0.7 split, 0.5 cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest Test Error =  0.0442 \n",
      "Best test split = 0.9 \n",
      "Best test cutoff = 0.5\n",
      "\n",
      "Highest Sensitivity =  0.9879 \n",
      "Best test split = 0.3 \n",
      "Best test cutoff = 0.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "two_classes = ['high', 'low']\n",
    "\n",
    "## Create for best metrics\n",
    "overall_best_test_error = 10**6\n",
    "best_test_error_q = 0\n",
    "best_test_cut = 0\n",
    "\n",
    "best_test_sensitivity = -1\n",
    "best_test_sense_q = 0\n",
    "best_test_sense_cut = 0\n",
    "\n",
    "for q in range(1, 10, 1):\n",
    "    ## Split train and test\n",
    "    train_data = pd.read_csv(f\"{data_path}/Training Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "    test_data = pd.read_csv(f\"{data_path}/Testing Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "\n",
    "    ## Bounds for low, medium, high expectancy\n",
    "    q1 = df['Life expectancy'].quantile(q/10)\n",
    "\n",
    "    train_data['Life expectancy category'] = train_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else \"high\")\n",
    "    test_data['Life expectancy category'] = test_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else \"high\")\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    X_two_train, y_two_train = scaler.fit_transform(train_data[pred]), train_data['Life expectancy category']\n",
    "    X_two_test, y_two_test = scaler.fit_transform(test_data[pred]), test_data['Life expectancy category']\n",
    "\n",
    "    # Construct model\n",
    "    two_log_mod = LogisticRegression(random_state = 4)\n",
    "    two_log_mod.fit(X_two_train, y_two_train)\n",
    "    y_two_pred = two_log_mod.predict(X_two_test)\n",
    "    y_two_train_pred = two_log_mod.predict(X_two_train)\n",
    "\n",
    "    ## Combine results in df\n",
    "    two_test_results = pd.DataFrame({\"Country\": test_data['Country'], \"Year\": test_data['Year'], \"Prediction\": y_two_pred, \"Actual\": y_two_test})\n",
    "    two_prob_prediction = two_log_mod.predict_proba(X_two_test)\n",
    "\n",
    "    for i in range(len(two_classes)):\n",
    "        two_test_results[f'P({two_classes[i]})'] = two_prob_prediction[:, i]\n",
    "\n",
    "    ## Create cutoff probability\n",
    "    best_cut = -1\n",
    "    best_test_error = 10**7\n",
    "\n",
    "    for c in range(0, 10, 1):\n",
    "        cut_off = c/10\n",
    "        two_test_results['Prediction'] = two_test_results.apply(lambda x: \"high\" if x['P(high)'] >= cut_off else \"low\", axis = 1)\n",
    "\n",
    "        test_conf_matrix = confusion_matrix(y_true = two_test_results['Actual'], y_pred = two_test_results['Prediction'], labels = two_classes)\n",
    "        test_classification_error = 1-(sum(np.diag(test_conf_matrix))/sum(sum(test_conf_matrix)))\n",
    "\n",
    "        if test_classification_error < best_test_error: \n",
    "            best_cut = cut_off\n",
    "            best_test_error = test_classification_error\n",
    "\n",
    "    # Use best cutoff to determine prediction\n",
    "    two_test_results['Prediction'] = two_test_results.apply(lambda x: \"high\" if x['P(high)'] >= best_cut else \"low\", axis = 1)\n",
    "    test_conf_matrix = confusion_matrix(y_true = two_test_results['Actual'], y_pred = two_test_results['Prediction'], labels = two_classes)\n",
    "\n",
    "    ## Calculate metrics\n",
    "    train_error = round(1-accuracy_score(y_true = y_two_train, y_pred=y_two_train_pred), 3)\n",
    "    sensitivity = test_conf_matrix[0, 0]/(test_conf_matrix[0, 0] + test_conf_matrix[0, 1])\n",
    "    \n",
    "    if best_test_error < overall_best_test_error:\n",
    "        overall_best_test_error = best_test_error\n",
    "        best_test_error_q = q/10\n",
    "        best_test_cut = best_cut\n",
    "\n",
    "    if sensitivity > best_test_sensitivity:\n",
    "        best_test_sensitivity = sensitivity\n",
    "        best_test_sense_q = q/10\n",
    "        best_test_sense_cut = best_cut\n",
    "\n",
    "print(\"Lowest Test Error = \", round(overall_best_test_error, 4), \"\\nBest test split =\", best_test_error_q, '\\nBest test cutoff =', best_test_cut, end = '\\n\\n')\n",
    "print(f\"Highest Sensitivity = \", round(best_test_sensitivity, 4), \"\\nBest test split =\", best_test_sense_q, \\\n",
    "          '\\nBest test cutoff =', best_test_sense_cut, end = '\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We run the models with the above results to determine which model is better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 79.7\n",
      "0.9-0.1 split, cutoff = 0.5\n",
      "Test Classification Error = 0.044\n",
      "Train Classification Error = 0.048\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.87      0.71      0.78        66\n",
      "         low       0.96      0.99      0.98       522\n",
      "\n",
      "    accuracy                           0.96       588\n",
      "   macro avg       0.92      0.85      0.88       588\n",
      "weighted avg       0.95      0.96      0.95       588\n",
      "\n",
      "0.3 65.21000000000001\n",
      "0.3-0.7 split, cutoff = 0.5\n",
      "Test Classification Error = 0.056\n",
      "Train Classification Error = 0.07\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        high       0.94      0.99      0.96       412\n",
      "         low       0.97      0.84      0.90       176\n",
      "\n",
      "    accuracy                           0.94       588\n",
      "   macro avg       0.95      0.91      0.93       588\n",
      "weighted avg       0.95      0.94      0.94       588\n",
      "\n",
      "Logistic Regression Equation:\n",
      "y = -1.053 + (1.358 * Adult Mortality) + (-0.721 * BMI) + (4.285 * HIV/AIDS) + (-0.429 * Income composition of resources) + (-0.998 * Schooling)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "two_classes = ['high', 'low']\n",
    "cut_off = 0.5\n",
    "\n",
    "for q in [0.9, 0.3]:\n",
    "    ## Split train and test\n",
    "    train_data = pd.read_csv(f\"{data_path}/Training Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "    test_data = pd.read_csv(f\"{data_path}/Testing Data.csv\").drop(columns = 'Unnamed: 0')\n",
    "\n",
    "    ## Bounds for low, medium, high expectancy\n",
    "    q1 = df['Life expectancy'].quantile(q)\n",
    "    print(q, q1)\n",
    "\n",
    "    train_data['Life expectancy category'] = train_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else \"high\")\n",
    "    test_data['Life expectancy category'] = test_data['Life expectancy'].apply(lambda x: \"low\" if x < q1 else \"high\")\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    X_two_train, y_two_train = scaler.fit_transform(train_data[pred]), train_data['Life expectancy category']\n",
    "    X_two_test, y_two_test = scaler.fit_transform(test_data[pred]), test_data['Life expectancy category']\n",
    "\n",
    "    # Construct model\n",
    "    two_log_mod = LogisticRegression(random_state = 4)\n",
    "    two_log_mod.fit(X_two_train, y_two_train)\n",
    "    y_two_pred = two_log_mod.predict(X_two_test)\n",
    "    y_two_train_pred = two_log_mod.predict(X_two_train)\n",
    "\n",
    "    ## Combine results in df\n",
    "    two_test_results = pd.DataFrame({\"Country\": test_data['Country'], \"Year\": test_data['Year'], \"Prediction\": y_two_pred, \\\n",
    "                                     \"Actual\": y_two_test, \"Actual LE\": test_data[\"Life expectancy\"]})\n",
    "    two_prob_prediction = two_log_mod.predict_proba(X_two_test)\n",
    "\n",
    "    for i in range(len(two_classes)):\n",
    "        two_test_results[f'P({two_classes[i]})'] = two_prob_prediction[:, i]\n",
    "\n",
    "    ## Create cutoff probability\n",
    "    two_test_results['Prediction'] = two_test_results.apply(lambda x: \"high\" if x['P(high)'] >= cut_off else \"low\", axis = 1)\n",
    "    test_conf_matrix = confusion_matrix(y_true = two_test_results['Actual'], y_pred = two_test_results['Prediction'], labels = two_classes)\n",
    "    test_classification_error = 1-(sum(np.diag(test_conf_matrix))/sum(sum(test_conf_matrix)))\n",
    "\n",
    "    ## Calculate metrics\n",
    "    ## Calculate metrics\n",
    "    ## Recall = Sensitivity = TP/(TP + FN), Precision = TP/(TP + FP), \n",
    "    ## F1 score = 2 * Precision * Sensitivity/(Precision + Sensitivity), Support = Row Sum\n",
    "\n",
    "    train_error = round(1-accuracy_score(y_true = y_two_train, y_pred = y_two_train_pred), 3)\n",
    "    test_error = round(1-accuracy_score(y_true = two_test_results['Actual'], y_pred = two_test_results['Prediction']), 3)\n",
    "\n",
    "    print(f\"{q}-{round(1-q, 2)} split, cutoff = {cut_off}\")\n",
    "    print(\"Test Classification Error =\", test_error)\n",
    "    print(\"Train Classification Error =\", train_error)\n",
    "    print(classification_report(y_true = y_two_test, y_pred = y_two_pred))\n",
    "\n",
    "    if q == 0.3:\n",
    "        sns.heatmap(test_conf_matrix, annot = True, fmt = 'g')  \n",
    "        plt.xlabel(\"Predicted Labels\")\n",
    "        plt.ylabel(\"Actual Label\")\n",
    "        plt.title(\"Test Confusion Matrix for 2-class Logistic Regression\")\n",
    "        plt.xticks(ticks = [0.5, 1.5], labels = two_classes)\n",
    "        plt.yticks(ticks = [0.5, 1.5], labels = two_classes)\n",
    "        plt.savefig(f\"{image_path}/Best Confusion Matrix for 2 class\")\n",
    "    \n",
    "        false_positive = two_test_results[(two_test_results['Actual'] == 'high') & (two_test_results['Prediction'] == 'low')]\n",
    "        false_negative = two_test_results[(two_test_results['Prediction'] == 'high') & (two_test_results['Actual'] == 'low')]\n",
    "\n",
    "        # Get the coefficients of the logistic regression model\n",
    "        coefficients = two_log_mod.coef_\n",
    "\n",
    "        # Get the intercept of the logistic regression model\n",
    "        intercept = two_log_mod.intercept_\n",
    "\n",
    "        # Get the names of the predictor variables\n",
    "        predictor_names = train_data[pred].columns\n",
    "\n",
    "        # Print the equation of the logistic regression model\n",
    "        print(\"Logistic Regression Equation:\")\n",
    "        equation = \"y = \" + str(round(intercept[0], 3))\n",
    "        for i, predictor in enumerate(predictor_names):\n",
    "            equation += \" + (\" + str(round(coefficients[0][i], 3)) + \" * \" + predictor + \")\"\n",
    "        print(equation)\n",
    "        "
   ]
  }
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