27-Missing-Values-Cross-Validation copy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = pd.read_csv(\"titanic-train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values = titanic_data.isnull().sum()\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.000000\n",
       "Survived       0.000000\n",
       "Pclass         0.000000\n",
       "Name           0.000000\n",
       "Sex            0.000000\n",
       "Age            0.198653\n",
       "SibSp          0.000000\n",
       "Parch          0.000000\n",
       "Ticket         0.000000\n",
       "Fare           0.000000\n",
       "Cabin          0.771044\n",
       "Embarked       0.002245\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values / titanic_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "502    2\n",
       "773    2\n",
       "517    2\n",
       "783    2\n",
       "359    2\n",
       "      ..\n",
       "659    0\n",
       "662    0\n",
       "438    0\n",
       "215    0\n",
       "445    0\n",
       "Length: 891, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum(axis=1).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Deletion (column / row)\n",
    "* Imputation:\n",
    "  * Mean/Median/Mode Imputation\n",
    "* Prediction Models\n",
    "* Assign a unique category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(titanic_data, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 12), (268, 12))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape, test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "strat_kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_data.drop('Survived', axis='columns')\n",
    "y = train_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Cabin'].fillna('Unknown', inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
      "C:\\Users\\Dell\\AppData\\Local\\Temp\\ipykernel_14544\\3030464755.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_val_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "\n",
    "for train_index, val_index in strat_kfold.split(X, y):\n",
    "  X_train_fold, X_val_fold = X.iloc[train_index], X.iloc[val_index]\n",
    "  y_train_fold, y_val_fold = y.iloc[train_index], y.iloc[val_index]\n",
    "  \n",
    "  X_train_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
    "  X_val_fold['Age'].fillna(X_train_fold['Age'].median(), inplace=True)\n",
    "  \n",
    "  X_train_fold['Cabin'].fillna('Unknown', inplace=True)\n",
    "  X_val_fold['Cabin'].fillna('Unknown', inplace=True)\n",
    "  \n",
    "  most_frequent_embarked_fold = X_train_fold['Embarked'].mode()[0]\n",
    "  X_train_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
    "  X_val_fold['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
    "  \n",
    "  X_train_fold = X_train_fold[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']]\n",
    "  X_val_fold = X_val_fold[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']]\n",
    "  \n",
    "  model = LogisticRegression(max_iter=1000)\n",
    "  model.fit(X_train_fold, y_train_fold)\n",
    "  \n",
    "  y_pred = model.predict(X_val_fold)\n",
    "  \n",
    "  accuracy = accuracy_score(y_val_fold, y_pred)\n",
    "  \n",
    "  scores.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.752, 0.696, 0.672, 0.6935483870967742, 0.7016129032258065]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7030322580645161"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_accuracy = np.mean(scores)\n",
    "average_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_data.drop('Survived', axis='columns')\n",
    "y = train_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_data.drop('Survived', axis='columns')\n",
    "y_test = test_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['Age'].fillna(X['Age'].median(), inplace=True)\n",
    "X_test['Age'].fillna(X['Age'].median(), inplace=True)\n",
    "\n",
    "X['Cabin'].fillna('Unknown', inplace=True)\n",
    "X_test['Cabin'].fillna('Unknown', inplace=True)\n",
    "\n",
    "most_frequent_embarked_fold = X['Embarked'].mode()[0]\n",
    "X['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
    "X_test['Embarked'].fillna(most_frequent_embarked_fold, inplace=True)\n",
    "\n",
    "X = X[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']]\n",
    "X_test = X_test[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "  \n",
    "accuracy = accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7276119402985075"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  }
 ],
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