Titanic - Logistic Regression Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading The Train Dataset Using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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",
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       "      <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 12 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         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",
       "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
       "1308                           Peter, Master. Michael J    male   NaN      1   \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  \n",
       "...     ...                 ...       ...   ...      ...  \n",
       "1304      0           A.5. 3236    8.0500   NaN        S  \n",
       "1305      0            PC 17758  108.9000  C105        C  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "1307      0              359309    8.0500   NaN        S  \n",
       "1308      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[1309 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data into a pandas dataframe\n",
    "train_titanic = pd.read_csv(\"Titanic Dataset (Complete).csv\")\n",
    "train_titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of rows and columns\n",
    "train_titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     1309 non-null   int64  \n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# 11 columns - feature columns, 1 column (survived) - target column\n",
    "# Getting information about the data\n",
    "train_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived          0\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              1\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting number of missing columns in each column\n",
    "train_titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing missing values using seaborn's heatmap\n",
    "sns.heatmap(train_titanic.isna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling The Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the missing values in the dataset\n",
    "# Majority of cabin column data is missing - so, DROP THAT COLUMN from the dataframe\n",
    "train_titanic = train_titanic.drop(columns = \"Cabin\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the age column - we can't drop this column since there are many values available\n",
    "# Replacing all missing values of age column with the mean age of all the available age values\n",
    "# inplace = true - to make the below action get saved in the original dataframe\n",
    "train_titanic['Age'].fillna(train_titanic['Age'].mean(), inplace = True)\n",
    "train_titanic['Fare'].fillna(train_titanic['Fare'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    S\n",
      "Name: Embarked, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Mode - value which is repeated most number of times\n",
    "# Finding mode value of 'Embarked' column\n",
    "print(train_titanic['Embarked'].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S\n"
     ]
    }
   ],
   "source": [
    "print(train_titanic['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing missing values in Embarked column with Mode value\n",
    "train_titanic['Embarked'].fillna(train_titanic['Embarked'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting number of missing columns in each column once again\n",
    "train_titanic.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Analysis And Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>0.377387</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.484918</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>12.883193</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.738879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>982.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId     Survived       Pclass          Age        SibSp  \\\n",
       "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean    655.000000     0.377387     2.294882    29.881138     0.498854   \n",
       "std     378.020061     0.484918     0.837836    12.883193     1.041658   \n",
       "min       1.000000     0.000000     1.000000     0.170000     0.000000   \n",
       "25%     328.000000     0.000000     2.000000    22.000000     0.000000   \n",
       "50%     655.000000     0.000000     3.000000    29.881138     0.000000   \n",
       "75%     982.000000     1.000000     3.000000    35.000000     1.000000   \n",
       "max    1309.000000     1.000000     3.000000    80.000000     8.000000   \n",
       "\n",
       "             Parch         Fare  \n",
       "count  1309.000000  1309.000000  \n",
       "mean      0.385027    33.295479  \n",
       "std       0.865560    51.738879  \n",
       "min       0.000000     0.000000  \n",
       "25%       0.000000     7.895800  \n",
       "50%       0.000000    14.454200  \n",
       "75%       0.000000    31.275000  \n",
       "max       9.000000   512.329200  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting statistical information about the data\n",
    "# Getting number of missing columns in each column\n",
    "train_titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     1309 non-null   int64  \n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1309 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1309 non-null   float64\n",
      " 10  Embarked     1309 non-null   object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 112.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    815\n",
       "1    494\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding number of people who survived and not survived\n",
    "train_titanic['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing how many survived and how many didn't\n",
    "sns.countplot(x = 'Survived', data = train_titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "male      843\n",
       "female    466\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting total number of males and females\n",
    "train_titanic['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Another countplot to check how many are male and how many are female\n",
    "sns.countplot(x = 'Sex', data = train_titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing number of survivors genderwise\n",
    "sns.countplot(x = 'Sex', hue = 'Survived', data = train_titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making countplot for pclass column\n",
    "sns.countplot(x = 'Pclass', data = train_titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Comparing number of survivors based on pclass column\n",
    "sns.countplot(x = 'Pclass', hue = 'Survived', data = train_titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encoding Categorical Columns To Numerical Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "male      843\n",
       "female    466\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping Name column, taking Sex and Embarked column and replacing their values with meaningful numeric values\n",
    "#'Sex'and 'Embarked' - categorical columns; process that will be done now is: encoding categorical columns\n",
    "train_titanic['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    916\n",
       "C    270\n",
       "Q    123\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_titanic['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>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>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>1</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>0</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 11 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         0       3   \n",
       "\n",
       "                                                   Name  Sex        Age  \\\n",
       "0                               Braund, Mr. Owen Harris    0  22.000000   \n",
       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.000000   \n",
       "2                                Heikkinen, Miss. Laina    1  26.000000   \n",
       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.000000   \n",
       "4                              Allen, Mr. William Henry    0  35.000000   \n",
       "...                                                 ...  ...        ...   \n",
       "1304                                 Spector, Mr. Woolf    0  29.881138   \n",
       "1305                       Oliva y Ocana, Dona. Fermina    1  39.000000   \n",
       "1306                       Saether, Mr. Simon Sivertsen    0  38.500000   \n",
       "1307                                Ware, Mr. Frederick    0  29.881138   \n",
       "1308                           Peter, Master. Michael J    0  29.881138   \n",
       "\n",
       "      SibSp  Parch              Ticket      Fare  Embarked  \n",
       "0         1      0           A/5 21171    7.2500         0  \n",
       "1         1      0            PC 17599   71.2833         1  \n",
       "2         0      0    STON/O2. 3101282    7.9250         0  \n",
       "3         1      0              113803   53.1000         0  \n",
       "4         0      0              373450    8.0500         0  \n",
       "...     ...    ...                 ...       ...       ...  \n",
       "1304      0      0           A.5. 3236    8.0500         0  \n",
       "1305      0      0            PC 17758  108.9000         1  \n",
       "1306      0      0  SOTON/O.Q. 3101262    7.2500         0  \n",
       "1307      0      0              359309    8.0500         0  \n",
       "1308      1      1                2668   22.3583         1  \n",
       "\n",
       "[1309 rows x 11 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_titanic.replace({'Sex': {'male':0, 'female':1}, 'Embarked': {'S':0, 'C':1, 'Q':2}}, inplace = True)\n",
    "train_titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating Features And Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating target column (survive) and the other features\n",
    "x = train_titanic.drop(columns = ['PassengerId', 'Name', 'Ticket', 'Survived'], axis = 1)\n",
    "y = train_titanic['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: \n",
      "       Pclass  Sex        Age  SibSp  Parch      Fare  Embarked\n",
      "0          3    0  22.000000      1      0    7.2500         0\n",
      "1          1    1  38.000000      1      0   71.2833         1\n",
      "2          3    1  26.000000      0      0    7.9250         0\n",
      "3          1    1  35.000000      1      0   53.1000         0\n",
      "4          3    0  35.000000      0      0    8.0500         0\n",
      "...      ...  ...        ...    ...    ...       ...       ...\n",
      "1304       3    0  29.881138      0      0    8.0500         0\n",
      "1305       1    1  39.000000      0      0  108.9000         1\n",
      "1306       3    0  38.500000      0      0    7.2500         0\n",
      "1307       3    0  29.881138      0      0    8.0500         0\n",
      "1308       3    0  29.881138      1      1   22.3583         1\n",
      "\n",
      "[1309 rows x 7 columns]\n",
      "\n",
      "Target Column: \n",
      " 0       0\n",
      "1       1\n",
      "2       1\n",
      "3       1\n",
      "4       0\n",
      "       ..\n",
      "1304    0\n",
      "1305    1\n",
      "1306    0\n",
      "1307    0\n",
      "1308    0\n",
      "Name: Survived, Length: 1309, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Features: \\n\", x)\n",
    "print(\"\\nTarget Column: \\n\", y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting Data Into Train And Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 7) (1047, 7) (262, 7)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape, x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression Model\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\FATHIMA ISMAIL\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model with training data\n",
    "model. fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy score\n",
    "# Giving just x_train data alone to the trained model, and the model will predict whether the person survived or not\n",
    "# ACCURACY ON TRAINING DATA\n",
    "x_train_pred = model.predict(x_train)\n",
    "x_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Accuracy: 0.8576886341929322\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      " [[593  62]\n",
      " [ 87 305]]\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.91      0.89       655\n",
      "           1       0.83      0.78      0.80       392\n",
      "\n",
      "    accuracy                           0.86      1047\n",
      "   macro avg       0.85      0.84      0.85      1047\n",
      "weighted avg       0.86      0.86      0.86      1047\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Training data evaluation score\n",
    "train_data_accuracy = accuracy_score(y_train, x_train_pred)\n",
    "print(\"Training Data Accuracy:\", train_data_accuracy)\n",
    "print(\"\\nConfusion Matrix:\\n\\n\", confusion_matrix(y_train, x_train_pred))\n",
    "print(\"\\nClassification Report:\\n\\n\", classification_report(y_train, x_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
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       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
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       "      dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluating model with test data\n",
    "# ACCURACY ON TEST DATA\n",
    "x_test_pred = model.predict(x_test)\n",
    "x_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Accuracy: 0.8549618320610687\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      " [[146  14]\n",
      " [ 24  78]]\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.91      0.88       160\n",
      "           1       0.85      0.76      0.80       102\n",
      "\n",
      "    accuracy                           0.85       262\n",
      "   macro avg       0.85      0.84      0.84       262\n",
      "weighted avg       0.85      0.85      0.85       262\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Test data evaluation score\n",
    "test_data_accuracy = accuracy_score(y_test, x_test_pred)\n",
    "print(\"Test Data Accuracy:\", test_data_accuracy)\n",
    "print(\"\\nConfusion Matrix:\\n\\n\", confusion_matrix(y_test, x_test_pred))\n",
    "print(\"\\nClassification Report:\\n\\n\", classification_report(y_test, x_test_pred))"
   ]
  }
 ],
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