Task 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13bde5f9-88eb-480d-b08d-47a822760ba6",
   "metadata": {},
   "source": [
    "# Task 1\n",
    "## Titanic Survival Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0dd599-d9f8-4c3b-bed1-aa744cde3076",
   "metadata": {},
   "source": [
    "# Import required dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1dfad965-054b-4ce7-bb5c-6cfb92968aa4",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05646452-869e-48ed-9741-aca0e18b5fe4",
   "metadata": {},
   "source": [
    "# load the data frame from csv file to pandas datframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a276a9a-9e1c-4a29-8e32-e39edeb69799",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data = pd.read_csv('tested.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d03548b9-3e38-4a20-8942-9d269b40df9d",
   "metadata": {},
   "outputs": [
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       "  <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>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
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       "      <td>893</td>\n",
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       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</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",
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       "      <th>413</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",
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       "    <tr>\n",
       "      <th>414</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>415</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>416</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",
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       "    <tr>\n",
       "      <th>417</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",
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       "<p>418 rows × 12 columns</p>\n",
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       "     PassengerId  Survived  Pclass  \\\n",
       "0            892         0       3   \n",
       "1            893         1       3   \n",
       "2            894         0       2   \n",
       "3            895         0       3   \n",
       "4            896         1       3   \n",
       "..           ...       ...     ...   \n",
       "413         1305         0       3   \n",
       "414         1306         1       1   \n",
       "415         1307         0       3   \n",
       "416         1308         0       3   \n",
       "417         1309         0       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                Kelly, Mr. James    male  34.5      0      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                       Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                                Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "..                                            ...     ...   ...    ...    ...   \n",
       "413                            Spector, Mr. Woolf    male   NaN      0      0   \n",
       "414                  Oliva y Ocana, Dona. Fermina  female  39.0      0      0   \n",
       "415                  Saether, Mr. Simon Sivertsen    male  38.5      0      0   \n",
       "416                           Ware, Mr. Frederick    male   NaN      0      0   \n",
       "417                      Peter, Master. Michael J    male   NaN      1      1   \n",
       "\n",
       "                 Ticket      Fare Cabin Embarked  \n",
       "0                330911    7.8292   NaN        Q  \n",
       "1                363272    7.0000   NaN        S  \n",
       "2                240276    9.6875   NaN        Q  \n",
       "3                315154    8.6625   NaN        S  \n",
       "4               3101298   12.2875   NaN        S  \n",
       "..                  ...       ...   ...      ...  \n",
       "413           A.5. 3236    8.0500   NaN        S  \n",
       "414            PC 17758  108.9000  C105        C  \n",
       "415  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416              359309    8.0500   NaN        S  \n",
       "417                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 12 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "t_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a19b92df-e8b9-4b47-8786-b749d8ce6aba",
   "metadata": {},
   "source": [
    "# learning about the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c4295942-c286-4488-b25d-d76234d4e982",
   "metadata": {},
   "outputs": [
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       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>896</td>\n",
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       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "t_data.head()"
   ]
  },
  {
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   "execution_count": 5,
   "id": "1f7e7913-74e5-48bf-8ca3-7f9914dd2b97",
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       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
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       "      <td>C</td>\n",
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       "      <th>415</th>\n",
       "      <td>1307</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>416</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",
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       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>417</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",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                          Name     Sex  \\\n",
       "413         1305         0       3            Spector, Mr. Woolf    male   \n",
       "414         1306         1       1  Oliva y Ocana, Dona. Fermina  female   \n",
       "415         1307         0       3  Saether, Mr. Simon Sivertsen    male   \n",
       "416         1308         0       3           Ware, Mr. Frederick    male   \n",
       "417         1309         0       3      Peter, Master. Michael J    male   \n",
       "\n",
       "      Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "413   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417   NaN      1      1                2668   22.3583   NaN        C  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0104a25c-0c5b-42d4-8b6c-ab063aaf4a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a003d2a-73f8-4f4b-8390-73cb7c8ee8e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Survived     418 non-null    int64  \n",
      " 2   Pclass       418 non-null    int64  \n",
      " 3   Name         418 non-null    object \n",
      " 4   Sex          418 non-null    object \n",
      " 5   Age          332 non-null    float64\n",
      " 6   SibSp        418 non-null    int64  \n",
      " 7   Parch        418 non-null    int64  \n",
      " 8   Ticket       418 non-null    object \n",
      " 9   Fare         417 non-null    float64\n",
      " 10  Cabin        91 non-null     object \n",
      " 11  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "t_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd41ce59-a744-47e0-8db8-963d47a2c4b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c30885a-9af0-42bf-8af4-ebfb044c6977",
   "metadata": {},
   "source": [
    "# we need to handle missing values \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95331557-5eb5-4cf8-9311-c241f8029d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to handle missing values in the cabin column we will drop it as they are numerous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e59a3e64-d0d5-44b5-8140-5704750af7fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data.drop(columns='Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f1b2156-73c8-43cc-a878-7f86bafbe8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to handle missing values in the age and fare column we will replace them with the mean age and fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d93c179c-cbb6-4e8d-9062-a47c3b3a550b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Age = t_data['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32d731e9-74f0-444f-b2f6-1b4b994dfc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data['Age'].fillna(Age, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f81b2ea9-86b7-4932-97f0-f33f51c89f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fare = t_data['Fare'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4146c081-2742-4dbf-aa64-412ce9670fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data['Fare'].fillna(Fare, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d7150e8-106f-4695-9476-887fb92d4d99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Survived     418 non-null    int64  \n",
      " 2   Pclass       418 non-null    int64  \n",
      " 3   Name         418 non-null    object \n",
      " 4   Sex          418 non-null    object \n",
      " 5   Age          418 non-null    float64\n",
      " 6   SibSp        418 non-null    int64  \n",
      " 7   Parch        418 non-null    int64  \n",
      " 8   Ticket       418 non-null    object \n",
      " 9   Fare         418 non-null    float64\n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "t_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9f9ed9a-c070-4b2a-bb97-cfa243f4714e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our data is now consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a7b5e5b2-d870-4d95-a310-07064a4d612b",
   "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>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</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>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</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>39.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</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>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</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>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</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>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0            892         0       3   \n",
       "1            893         1       3   \n",
       "2            894         0       2   \n",
       "3            895         0       3   \n",
       "4            896         1       3   \n",
       "..           ...       ...     ...   \n",
       "413         1305         0       3   \n",
       "414         1306         1       1   \n",
       "415         1307         0       3   \n",
       "416         1308         0       3   \n",
       "417         1309         0       3   \n",
       "\n",
       "                                             Name     Sex       Age  SibSp  \\\n",
       "0                                Kelly, Mr. James    male  34.50000      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)  female  47.00000      1   \n",
       "2                       Myles, Mr. Thomas Francis    male  62.00000      0   \n",
       "3                                Wirz, Mr. Albert    male  27.00000      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.00000      1   \n",
       "..                                            ...     ...       ...    ...   \n",
       "413                            Spector, Mr. Woolf    male  30.27259      0   \n",
       "414                  Oliva y Ocana, Dona. Fermina  female  39.00000      0   \n",
       "415                  Saether, Mr. Simon Sivertsen    male  38.50000      0   \n",
       "416                           Ware, Mr. Frederick    male  30.27259      0   \n",
       "417                      Peter, Master. Michael J    male  30.27259      1   \n",
       "\n",
       "     Parch              Ticket      Fare Embarked  \n",
       "0        0              330911    7.8292        Q  \n",
       "1        0              363272    7.0000        S  \n",
       "2        0              240276    9.6875        Q  \n",
       "3        0              315154    8.6625        S  \n",
       "4        1             3101298   12.2875        S  \n",
       "..     ...                 ...       ...      ...  \n",
       "413      0           A.5. 3236    8.0500        S  \n",
       "414      0            PC 17758  108.9000        C  \n",
       "415      0  SOTON/O.Q. 3101262    7.2500        S  \n",
       "416      0              359309    8.0500        S  \n",
       "417      1                2668   22.3583        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7873dbc-04b8-4aae-afb2-e78a13538ff4",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e730e7f-e7ee-4360-bbee-bcd440b7b070",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9239256-3448-4723-81c1-ae3285bee737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Sex', data=t_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "817c017c-1688-4a67-836d-8bd139df851f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    266\n",
       "1    152\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data['Survived'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2178a6fa-5aff-4fdf-8d1a-d959aa8ad82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', data=t_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a0438814-afbd-4c1e-82df-de7cecfeed08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot (x='Sex', hue = 'Survived', data = t_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2354792d-0c74-4ba8-b37f-7435ca91e0d6",
   "metadata": {},
   "source": [
    "# this is clearly visible that those who survived were only females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6ef2b83-4c8f-4c27-a244-008113524b45",
   "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>Survived</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived     Sex\n",
       "0           0    male\n",
       "1           1  female\n",
       "2           0    male\n",
       "3           0    male\n",
       "4           1  female\n",
       "..        ...     ...\n",
       "413         0    male\n",
       "414         1  female\n",
       "415         0    male\n",
       "416         0    male\n",
       "417         0    male\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data[['Survived', 'Sex']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e77a609c-09f8-417d-9e10-52d5e9aaf189",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass\n",
       "0           0       3\n",
       "1           1       3\n",
       "2           0       2\n",
       "3           0       3\n",
       "4           1       3\n",
       "..        ...     ...\n",
       "413         0       3\n",
       "414         1       1\n",
       "415         0       3\n",
       "416         0       3\n",
       "417         0       3\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " t_data[['Survived', 'Pclass' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28993ae6-4ffc-4fb0-a41c-e4994ae3024e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x ='Pclass',hue= 'Survived', data=t_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accd4794-e481-4a27-9394-277bf9296603",
   "metadata": {},
   "source": [
    "# converting the categorical variables into numerical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "65b93d2a-2672-4e13-9d8c-3e66cedf0c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data.replace({'Sex':{'male':0, 'female':1},'Embarked':{'S':0, 'C':1, 'Q':2}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8ccc4b5f-be6e-4fc6-87b9-13a585b2af28",
   "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>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>34.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>1</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>0</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>1</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</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>413</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>30.27259</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>414</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>39.00000</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>415</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>38.50000</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>416</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>30.27259</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>417</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>30.27259</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>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0            892         0       3   \n",
       "1            893         1       3   \n",
       "2            894         0       2   \n",
       "3            895         0       3   \n",
       "4            896         1       3   \n",
       "..           ...       ...     ...   \n",
       "413         1305         0       3   \n",
       "414         1306         1       1   \n",
       "415         1307         0       3   \n",
       "416         1308         0       3   \n",
       "417         1309         0       3   \n",
       "\n",
       "                                             Name  Sex       Age  SibSp  \\\n",
       "0                                Kelly, Mr. James    0  34.50000      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)    1  47.00000      1   \n",
       "2                       Myles, Mr. Thomas Francis    0  62.00000      0   \n",
       "3                                Wirz, Mr. Albert    0  27.00000      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)    1  22.00000      1   \n",
       "..                                            ...  ...       ...    ...   \n",
       "413                            Spector, Mr. Woolf    0  30.27259      0   \n",
       "414                  Oliva y Ocana, Dona. Fermina    1  39.00000      0   \n",
       "415                  Saether, Mr. Simon Sivertsen    0  38.50000      0   \n",
       "416                           Ware, Mr. Frederick    0  30.27259      0   \n",
       "417                      Peter, Master. Michael J    0  30.27259      1   \n",
       "\n",
       "     Parch              Ticket      Fare  Embarked  \n",
       "0        0              330911    7.8292         2  \n",
       "1        0              363272    7.0000         0  \n",
       "2        0              240276    9.6875         2  \n",
       "3        0              315154    8.6625         0  \n",
       "4        1             3101298   12.2875         0  \n",
       "..     ...                 ...       ...       ...  \n",
       "413      0           A.5. 3236    8.0500         0  \n",
       "414      0            PC 17758  108.9000         1  \n",
       "415      0  SOTON/O.Q. 3101262    7.2500         0  \n",
       "416      0              359309    8.0500         0  \n",
       "417      1                2668   22.3583         1  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f21a7fad-c239-44ec-a32b-0a0c113a5863",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now drop the columns which are irrelevant for the survival prediction, such as PassengerId, Name and Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e19e46c0-79c1-419c-aa55-d0f14a731fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_data.drop(columns={'PassengerId','Name','Ticket'},axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "592f79b5-866b-4411-a7b6-f9e6a6b98ace",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex       Age  SibSp  Parch      Fare  Embarked\n",
       "0           0       3    0  34.50000      0      0    7.8292         2\n",
       "1           1       3    1  47.00000      1      0    7.0000         0\n",
       "2           0       2    0  62.00000      0      0    9.6875         2\n",
       "3           0       3    0  27.00000      0      0    8.6625         0\n",
       "4           1       3    1  22.00000      1      1   12.2875         0\n",
       "..        ...     ...  ...       ...    ...    ...       ...       ...\n",
       "413         0       3    0  30.27259      0      0    8.0500         0\n",
       "414         1       1    1  39.00000      0      0  108.9000         1\n",
       "415         0       3    0  38.50000      0      0    7.2500         0\n",
       "416         0       3    0  30.27259      0      0    8.0500         0\n",
       "417         0       3    0  30.27259      1      1   22.3583         1\n",
       "\n",
       "[418 rows x 8 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a9ab44-8d03-4c5f-a134-689021fa66ed",
   "metadata": {},
   "source": [
    "# separating features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9f79000b-e865-4ea4-bd3f-67a2a71f3dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = t_data.drop(columns='Survived', axis=1)\n",
    "Y = t_data['Survived']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "094c9cc2-96fd-43a8-a1c1-21d73a4b32f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pclass  Sex       Age  SibSp  Parch      Fare  Embarked\n",
      "0         3    0  34.50000      0      0    7.8292         2\n",
      "1         3    1  47.00000      1      0    7.0000         0\n",
      "2         2    0  62.00000      0      0    9.6875         2\n",
      "3         3    0  27.00000      0      0    8.6625         0\n",
      "4         3    1  22.00000      1      1   12.2875         0\n",
      "..      ...  ...       ...    ...    ...       ...       ...\n",
      "413       3    0  30.27259      0      0    8.0500         0\n",
      "414       1    1  39.00000      0      0  108.9000         1\n",
      "415       3    0  38.50000      0      0    7.2500         0\n",
      "416       3    0  30.27259      0      0    8.0500         0\n",
      "417       3    0  30.27259      1      1   22.3583         1\n",
      "\n",
      "[418 rows x 7 columns]\n",
      "0      0\n",
      "1      1\n",
      "2      0\n",
      "3      0\n",
      "4      1\n",
      "      ..\n",
      "413    0\n",
      "414    1\n",
      "415    0\n",
      "416    0\n",
      "417    0\n",
      "Name: Survived, Length: 418, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3da53a0d-eea5-47f6-842f-113c3d3d6dcb",
   "metadata": {},
   "source": [
    "# splitting the data into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "53100440-d5a6-4a42-8808-6406c1d186a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7105403e-3aac-4c89-9c82-1892fa618e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1a1ca5-ca7d-41d0-b417-6eda7b08c0a0",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e99d95cf-9c0a-4870-97b8-a36f7285412d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we're using logistic regression model that uses binary classification for the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1cf4c83b-dae3-4b94-8167-71cf0dc60bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "edab2562-0ed2-4b7c-ac26-792353e0634d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#training the model with the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c0618c6d-7c90-432c-afc5-182500863519",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808cf768-82e4-4206-88eb-d3a04f93115c",
   "metadata": {},
   "source": [
    "# Model Evaluation\n",
    "Accuracy Score || Precision Score || Recall Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ee865d57-7272-46d8-88c6-0541d4ab077f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "161c3535-cd93-4ec8-bd96-cb14cc6592f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy score for training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4dbf2704-ac33-414b-ac88-d404ddd04d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy score for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ed93601-202a-4cf9-86d7-4f17aa95e2a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 1 0 0\n",
      " 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0\n",
      " 0 1 0 1 1 0 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2a6fc99-beaa-4152-a374-1caef3fdf94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0ec2c79c-a7b7-46e4-8969-38a2a066327c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of test data is :  1.0\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score of test data is : ',testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "33de6f46-b9e3-4203-9255-924765063320",
   "metadata": {},
   "outputs": [],
   "source": [
    "# precision score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c7f50f76-36a8-4845-a9c9-b01354f36b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_precision = precision_score(Y_test, X_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9214553a-892d-4863-93f8-4227bbc89e09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data precion is : 1.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('test data precion is :', test_data_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "605b7e9d-963a-4ab3-91c8-1c7797bbeba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# recall score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baa4321b-f2ca-4f4a-a063-f42caec8e37c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train_prediction' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_12100\\2348608667.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest_data_recall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrecall_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train_prediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'X_train_prediction' is not defined"
     ]
    }
   ],
   "source": [
    "test_data_recall = recall_score(Y_train, X_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d1d00fb-9f4b-4977-8d99-182914c9caa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print('test data recall is :', test_data_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6924d3d1-981e-46f3-9cd4-515cee379223",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caed378d-adc3-4407-9e1e-5c2e5c7ddc9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = model.score(X_test,Y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ee7ebcd-ecba-45d1-9777-9b010826581b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(Y_train, X_train_prediction)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ec9ed02-5bf9-49d9-8698-13050010c176",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(cm, annot = True, fmt = \"d\", square = True, cmap= \"inferno\")\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('predicted label')\n",
    "title = ('Accuracy Score :',score)\n",
    "plt.title(title, size = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52a3bd5d-3c69-4eb9-8d8e-312415210d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_report(X_test_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e584894-a12f-406e-b0c7-d6ecdac178ef",
   "metadata": {},
   "source": [
    "# Model 2 \n",
    "# Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce256d1b-dd7e-40fc-9b00-2fb88a2daba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "781b5d88-2b00-4707-8baf-d1065f86f168",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2 = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2659b33d-ac8d-4c05-bbc5-9c744e289033",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5df82608-ea68-4152-a6b2-de841dc6ad9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pred = model_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc3f9285-79d0-4764-8796-6780295c720f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2.score(X_train, Y_train)\n",
    "acc_score = round(model_2.score(X_test, Y_test) * 100, 2)\n",
    "model_2_acc = accuracy_score(X_test_pred, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42875783-2e64-44ac-912b-2d8ce4e4be5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394cec97-2423-4adf-820e-8e7037d9c22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f448eb94-c654-41ab-aa81-6e79ac975906",
   "metadata": {},
   "outputs": [],
   "source": [
    "precision = precision_score(X_test_pred, Y_test)\n",
    "recall = recall_score(X_test_pred, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf84fbb-1b7d-495f-be27-05b828813fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(precision)\n",
    "print(recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "258dedc8-112b-452e-ba52-60cf3f1e4491",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_report(X_test_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743139ff-a5ba-49b6-8f2c-79d8a9817f5c",
   "metadata": {},
   "source": [
    "# Model 3\n",
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f977fa01-b31f-4f64-abce-d5abe6de9ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14f826d3-7d52-4d10-aaba-29b6c34ff485",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3 = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf114de-3552-4ae4-a037-698730807f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "472ff006-dbb3-4e40-8069-736fea4fcfd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pred = model_3.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9792f9d-116f-4579-abb8-b1ea1ded907f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3_acc = accuracy_score(X_test_pred, Y_test, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13663f67-d127-47eb-9944-476e7ab5765b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3_score = round(model_3.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22c483cf-469c-497f-8704-c0aa95e84519",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3_precision = precision_score(X_test_pred, Y_test)\n",
    "model_3_recall = recall_score(X_test_pred, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bd44c72-3542-41fb-b320-b86f86f4be2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cac17e56-4739-4122-ad81-c1aa57bf510b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_3_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f94c3365-18e6-453b-b6ae-2a96632bf589",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model_3_precision)\n",
    "print(model_3_recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6870b92-4a64-4368-8815-e8d6454dcaab",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification_report(X_test_pred, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b6eb6c9-ff5c-4ece-b72b-e8dfc0b83b43",
   "metadata": {},
   "source": [
    "# Predicting values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b1dfe4d-0c27-4e3e-a9c7-89451594e6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X) \n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4219e278-c68a-4c70-ae3c-fce4661eeb44",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = model.predict([[3, 0, 34.5, 0, 0, 7.8292, 2]])\n",
    "p2 = model_2.predict([[3, 0, 34.5, 0, 0, 7.8292, 2]])\n",
    "p3 = model_3.predict([[3, 0, 34.5, 0, 0, 7.8292, 2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa55faa-0d5d-499d-beeb-b90de70feae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(p1)\n",
    "print(p2)\n",
    "print(p3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93e2401c-37a4-4536-b197-e0b47f3f8e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = model.predict([[2, 0, 38.5, 0, 0, 7.2500, 0]])\n",
    "p2 = model_2.predict([[2, 0, 38.5, 0, 0, 7.2500, 0]])\n",
    "p3 = model_3.predict([[2, 0, 38.5, 0, 0, 7.2500, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98aa5025-8920-4058-91da-3183d04299bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(p1)\n",
    "print(p2)\n",
    "print(p3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ef16e2-601c-45b2-bf1f-90ca78a237f3",
   "metadata": {},
   "source": [
    "# Our prediction model is ready , and we have also predicted certain values\n",
    "# We used three different models to predict our target vaiable and saw that they provided different accuracy and precision."
   ]
  }
 ],
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