model_training.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "import numpy as np # linear algebra\n",
    "import os # accessing directory structure\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):\n",
    "    nunique = df.nunique()\n",
    "    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
    "    nRow, nCol = df.shape\n",
    "    columnNames = list(df)\n",
    "    nGraphRow = (int)((nCol + nGraphPerRow - 1) / nGraphPerRow)\n",
    "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
    "    for i in range(min(nCol, nGraphShown)):\n",
    "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
    "        columnDf = df.iloc[:, i]\n",
    "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
    "            valueCounts = columnDf.value_counts()\n",
    "            valueCounts.plot.bar()\n",
    "        else:\n",
    "            columnDf.hist()\n",
    "        plt.ylabel('counts')\n",
    "        plt.xticks(rotation = 90)\n",
    "        plt.title(f'{columnNames[i]} (column {i})')\n",
    "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation matrix\n",
    "def plotCorrelationMatrix(df, graphWidth):\n",
    "    df = df.dropna(axis='columns')  # drop columns with NaN\n",
    "    df = df.select_dtypes(include=[np.number])  # keep only numeric columns\n",
    "    if df.shape[1] < 2:\n",
    "        print(f'No correlation plots shown: The number of numeric columns ({df.shape[1]}) is less than 2')\n",
    "        return\n",
    "    corr = df.corr()\n",
    "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
    "    corrMat = plt.matshow(corr, fignum = 1)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
    "    plt.gca().xaxis.tick_bottom()\n",
    "    plt.colorbar(corrMat)\n",
    "    plt.title(f'Correlation Matrix for {df}', fontsize=15)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scatter and density plots\n",
    "def plotScatterMatrix(df, plotSize, textSize):\n",
    "    df = df.select_dtypes(include=[np.number]) # keep only numerical columns\n",
    "    # Remove rows and columns that would lead to df being singular\n",
    "    df = df.dropna(axis='columns')\n",
    "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
    "    columnNames = list(df)\n",
    "    if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots\n",
    "        columnNames = columnNames[:10]\n",
    "    df = df[columnNames]\n",
    "    ax = pd.plotting.scatter_matrix(df, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')\n",
    "    corrs = df.corr().values\n",
    "    for i, j in zip(*plt.np.triu_indices_from(ax, k=1)):\n",
    "        ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)\n",
    "    plt.suptitle('Scatter and Density Plot')\n",
    "    plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 200 rows and 14 columns\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>zombieid</th>\n",
       "      <th>zombie</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>rurality</th>\n",
       "      <th>household</th>\n",
       "      <th>water</th>\n",
       "      <th>food</th>\n",
       "      <th>medication</th>\n",
       "      <th>tools</th>\n",
       "      <th>firstaid</th>\n",
       "      <th>sanitation</th>\n",
       "      <th>clothing</th>\n",
       "      <th>documents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Human</td>\n",
       "      <td>18</td>\n",
       "      <td>Female</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>No tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Human</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rural</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>Food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Human</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rural</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>Food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>No tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Human</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Rural</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>tools</td>\n",
       "      <td>No first aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Human</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>No tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Human</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Human</td>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No food</td>\n",
       "      <td>Medication</td>\n",
       "      <td>No tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Human</td>\n",
       "      <td>20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Rural</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Food</td>\n",
       "      <td>No medication</td>\n",
       "      <td>No tools</td>\n",
       "      <td>No first aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Human</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>No food</td>\n",
       "      <td>No medication</td>\n",
       "      <td>tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Documents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Human</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>Rural</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>No food</td>\n",
       "      <td>No medication</td>\n",
       "      <td>tools</td>\n",
       "      <td>First aid supplies</td>\n",
       "      <td>Sanitation</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Documents</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zombieid zombie  age     sex  rurality  household  water     food   \n",
       "0         1  Human   18  Female     Rural          1      0     Food  \\\n",
       "1         2  Human   18    Male     Rural          3     24     Food   \n",
       "2         3  Human   18    Male     Rural          4     16     Food   \n",
       "3         4  Human   19    Male     Rural          1      0     Food   \n",
       "4         5  Human   19    Male     Urban          1      0     Food   \n",
       "5         6  Human   19  Female     Urban          1      0     Food   \n",
       "6         7  Human   20  Female  Suburban          2      0  No food   \n",
       "7         8  Human   20  Female     Rural          2      0     Food   \n",
       "8         9  Human   21  Female     Urban          1      8  No food   \n",
       "9        10  Human   21  Female     Rural          2      8  No food   \n",
       "\n",
       "      medication     tools               firstaid  sanitation  clothing   \n",
       "0     Medication  No tools     First aid supplies  Sanitation  Clothing  \\\n",
       "1     Medication     tools     First aid supplies  Sanitation  Clothing   \n",
       "2     Medication  No tools     First aid supplies  Sanitation  Clothing   \n",
       "3     Medication     tools  No first aid supplies  Sanitation  Clothing   \n",
       "4     Medication  No tools     First aid supplies  Sanitation       NaN   \n",
       "5     Medication     tools     First aid supplies  Sanitation  Clothing   \n",
       "6     Medication  No tools     First aid supplies  Sanitation  Clothing   \n",
       "7  No medication  No tools  No first aid supplies  Sanitation  Clothing   \n",
       "8  No medication     tools     First aid supplies  Sanitation  Clothing   \n",
       "9  No medication     tools     First aid supplies  Sanitation  Clothing   \n",
       "\n",
       "   documents  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "5        NaN  \n",
       "6        NaN  \n",
       "7        NaN  \n",
       "8  Documents  \n",
       "9  Documents  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset/zombies.csv' , delimiter=',', nrows = 1000)\n",
    "nRow, nCol = df.shape\n",
    "print(f'There are {nRow} rows and {nCol} columns')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1280 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(df, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCorrelationMatrix(df, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotScatterMatrix(df, 12, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>zombieid</th>\n",
       "      <th>age</th>\n",
       "      <th>household</th>\n",
       "      <th>water</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>44.415000</td>\n",
       "      <td>2.680000</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>17.371865</td>\n",
       "      <td>1.263162</td>\n",
       "      <td>12.069626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         zombieid         age   household       water\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   100.500000   44.415000    2.680000    8.750000\n",
       "std     57.879185   17.371865    1.263162   12.069626\n",
       "min      1.000000   18.000000    1.000000    0.000000\n",
       "25%     50.750000   29.000000    2.000000    0.000000\n",
       "50%    100.500000   42.000000    2.500000    8.000000\n",
       "75%    150.250000   58.000000    4.000000    8.000000\n",
       "max    200.000000   85.000000    6.000000   40.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   zombieid    200 non-null    int64 \n",
      " 1   zombie      200 non-null    object\n",
      " 2   age         200 non-null    int64 \n",
      " 3   sex         200 non-null    object\n",
      " 4   rurality    200 non-null    object\n",
      " 5   household   200 non-null    int64 \n",
      " 6   water       200 non-null    int64 \n",
      " 7   food        200 non-null    object\n",
      " 8   medication  200 non-null    object\n",
      " 9   tools       200 non-null    object\n",
      " 10  firstaid    200 non-null    object\n",
      " 11  sanitation  200 non-null    object\n",
      " 12  clothing    126 non-null    object\n",
      " 13  documents   66 non-null     object\n",
      "dtypes: int64(4), object(10)\n",
      "memory usage: 22.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixing the null valued columns by categorizing into 1 & 0 for thrier respective columns --> `Clothing` & `Documents`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values :  ['Clothing' nan]\n",
      "Unique values :  [nan 'Documents']\n"
     ]
    }
   ],
   "source": [
    "print('Unique values : ',df['clothing'].unique())\n",
    "print('Unique values : ',df['documents'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['documents'] = df['documents'].fillna(0)\n",
    "df['documents'] = df['documents'].replace('Documents', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clothing'] = df['clothing'].fillna(0)\n",
    "df['clothing'] = df['clothing'].replace('Clothing', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 14 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   zombieid    200 non-null    int64 \n",
      " 1   zombie      200 non-null    object\n",
      " 2   age         200 non-null    int64 \n",
      " 3   sex         200 non-null    object\n",
      " 4   rurality    200 non-null    object\n",
      " 5   household   200 non-null    int64 \n",
      " 6   water       200 non-null    int64 \n",
      " 7   food        200 non-null    object\n",
      " 8   medication  200 non-null    object\n",
      " 9   tools       200 non-null    object\n",
      " 10  firstaid    200 non-null    object\n",
      " 11  sanitation  200 non-null    object\n",
      " 12  clothing    200 non-null    int64 \n",
      " 13  documents   200 non-null    int64 \n",
      "dtypes: int64(6), object(8)\n",
      "memory usage: 22.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping column not required "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('zombieid', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the columns to be label encoded\n",
    "columns_to_encode = ['zombie','sex', 'rurality', 'household', 'food', 'medication', 'tools','firstaid','sanitation','clothing','documents']\n",
    "\n",
    "# Create an instance of LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Iterate over the columns and apply label encoding\n",
    "for column in columns_to_encode:\n",
    "    df[column] = label_encoder.fit_transform(df[column])\n",
    "df.medication = df.medication.replace({1: 0, 0: 1})\n",
    "df.firstaid = df.firstaid.replace({1: 0, 0: 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>zombie</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>rurality</th>\n",
       "      <th>household</th>\n",
       "      <th>water</th>\n",
       "      <th>food</th>\n",
       "      <th>medication</th>\n",
       "      <th>tools</th>\n",
       "      <th>firstaid</th>\n",
       "      <th>sanitation</th>\n",
       "      <th>clothing</th>\n",
       "      <th>documents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.00000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.395000</td>\n",
       "      <td>44.415000</td>\n",
       "      <td>0.50500</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.49500</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.490077</td>\n",
       "      <td>17.371865</td>\n",
       "      <td>0.50123</td>\n",
       "      <td>0.845681</td>\n",
       "      <td>1.263162</td>\n",
       "      <td>12.069626</td>\n",
       "      <td>0.498742</td>\n",
       "      <td>0.500352</td>\n",
       "      <td>0.50123</td>\n",
       "      <td>0.500352</td>\n",
       "      <td>0.501154</td>\n",
       "      <td>0.484016</td>\n",
       "      <td>0.471393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           zombie         age        sex    rurality   household       water   \n",
       "count  200.000000  200.000000  200.00000  200.000000  200.000000  200.000000  \\\n",
       "mean     0.395000   44.415000    0.50500    0.780000    1.680000    8.750000   \n",
       "std      0.490077   17.371865    0.50123    0.845681    1.263162   12.069626   \n",
       "min      0.000000   18.000000    0.00000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000   29.000000    0.00000    0.000000    1.000000    0.000000   \n",
       "50%      0.000000   42.000000    1.00000    1.000000    1.500000    8.000000   \n",
       "75%      1.000000   58.000000    1.00000    2.000000    3.000000    8.000000   \n",
       "max      1.000000   85.000000    1.00000    2.000000    5.000000   40.000000   \n",
       "\n",
       "             food  medication      tools    firstaid  sanitation    clothing   \n",
       "count  200.000000  200.000000  200.00000  200.000000  200.000000  200.000000  \\\n",
       "mean     0.450000    0.470000    0.49500    0.530000    0.490000    0.630000   \n",
       "std      0.498742    0.500352    0.50123    0.500352    0.501154    0.484016   \n",
       "min      0.000000    0.000000    0.00000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.00000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.00000    1.000000    0.000000    1.000000   \n",
       "75%      1.000000    1.000000    1.00000    1.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.00000    1.000000    1.000000    1.000000   \n",
       "\n",
       "        documents  \n",
       "count  200.000000  \n",
       "mean     0.330000  \n",
       "std      0.471393  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 13 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   zombie      200 non-null    int32\n",
      " 1   age         200 non-null    int64\n",
      " 2   sex         200 non-null    int32\n",
      " 3   rurality    200 non-null    int32\n",
      " 4   household   200 non-null    int64\n",
      " 5   water       200 non-null    int64\n",
      " 6   food        200 non-null    int32\n",
      " 7   medication  200 non-null    int32\n",
      " 8   tools       200 non-null    int32\n",
      " 9   firstaid    200 non-null    int32\n",
      " 10  sanitation  200 non-null    int32\n",
      " 11  clothing    200 non-null    int64\n",
      " 12  documents   200 non-null    int64\n",
      "dtypes: int32(8), int64(5)\n",
      "memory usage: 14.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   zombie  age  sex  rurality  household  water  food  medication  tools   \n",
       "0       0   18    0         0          0      0     0           1      0  \\\n",
       "1       0   18    1         0          2     24     0           1      1   \n",
       "2       0   18    1         0          3     16     0           1      0   \n",
       "3       0   19    1         0          0      0     0           1      1   \n",
       "4       0   19    1         2          0      0     0           1      0   \n",
       "5       0   19    0         2          0      0     0           1      1   \n",
       "6       0   20    0         1          1      0     1           1      0   \n",
       "7       0   20    0         0          1      0     0           0      0   \n",
       "8       0   21    0         2          0      8     1           0      1   \n",
       "9       0   21    0         0          1      8     1           0      1   \n",
       "\n",
       "   firstaid  sanitation  clothing  documents  \n",
       "0         1           1         1          0  \n",
       "1         1           1         1          0  \n",
       "2         1           1         1          0  \n",
       "3         0           1         1          0  \n",
       "4         1           1         0          0  \n",
       "5         1           1         1          0  \n",
       "6         1           1         1          0  \n",
       "7         0           1         1          0  \n",
       "8         1           1         1          1  \n",
       "9         1           1         1          1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into dependent and independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>rurality</th>\n",
       "      <th>household</th>\n",
       "      <th>water</th>\n",
       "      <th>food</th>\n",
       "      <th>medication</th>\n",
       "      <th>tools</th>\n",
       "      <th>firstaid</th>\n",
       "      <th>sanitation</th>\n",
       "      <th>clothing</th>\n",
       "      <th>documents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  rurality  household  water  food  medication  tools  firstaid   \n",
       "0   18    0         0          0      0     0           1      0         1  \\\n",
       "1   18    1         0          2     24     0           1      1         1   \n",
       "2   18    1         0          3     16     0           1      0         1   \n",
       "3   19    1         0          0      0     0           1      1         0   \n",
       "4   19    1         2          0      0     0           1      0         1   \n",
       "\n",
       "   sanitation  clothing  documents  \n",
       "0           1         1          0  \n",
       "1           1         1          0  \n",
       "2           1         1          0  \n",
       "3           1         1          0  \n",
       "4           1         0          0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Independent Variable DataFrame\n",
    "x = df.iloc[:,1:13]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: zombie, dtype: int32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating Dependent Variable DataFrame\n",
    "y = df.iloc[:,0]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "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=0.1,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the model we are using\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# Instantiate model with 1000 decision trees\n",
    "rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=1000, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=1000, random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model on training data\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "# Assuming you have your model predictions and true labels\n",
    "\n",
    "y_pred = [round(pred) for pred in rf.predict(X_test)]\n",
    "y_true = y_test\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "\n",
    "# Print the accuracy\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "trained_model = rf\n",
    "\n",
    "# Save the trained model to a pickle file\n",
    "with open('model.pkl', 'wb') as file:\n",
    "    pickle.dump(trained_model, file)\n"
   ]
  }
 ],
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