logistic2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd7fc345-a8f7-499b-8736-df4ce93719b7",
   "metadata": {},
   "source": [
    "# Full Data Science Project - Titanic survivals Prediction [Classification] :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f36156-7498-43ac-924c-4155e66b2855",
   "metadata": {},
   "source": [
    "### Import Libraries :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63c34fb0-874f-41e4-84d1-6155a44539cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2314cd-1130-401e-a649-7a504e8712c6",
   "metadata": {},
   "source": [
    "### import dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "579c0721-d590-4a16-85bd-d9f30ecc1136",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"data/titanic.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35cc8bcf-d29e-456d-858a-6aa96d52d53f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          715 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15df0b68-b7b5-490e-96f1-be24b5c49b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>891.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>32.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>49.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>512.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId  Survived  Pclass    Age  SibSp  Parch   Fare\n",
       "count        891.0     891.0   891.0  714.0  891.0  891.0  891.0\n",
       "mean         446.0       0.4     2.3   29.7    0.5    0.4   32.2\n",
       "std          257.4       0.5     0.8   14.5    1.1    0.8   49.7\n",
       "min            1.0       0.0     1.0    0.4    0.0    0.0    0.0\n",
       "25%          223.5       0.0     2.0   20.1    0.0    0.0    7.9\n",
       "50%          446.0       0.0     3.0   28.0    0.0    0.0   14.5\n",
       "75%          668.5       1.0     3.0   38.0    1.0    0.0   31.0\n",
       "max          891.0       1.0     3.0   80.0    8.0    6.0  512.3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51765d46-6953-4bba-b33d-8faa6e9b708f",
   "metadata": {},
   "source": [
    "### Data Cleaning $ preprocessing :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89068ff8-656a-439a-b50c-64cd0c400297",
   "metadata": {},
   "source": [
    "- dealing with missing value :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08aee60a-e332-4e80-ba6c-b42757ee9277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02466a89-b220-4512-b253-52b40857fb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age             True\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5534e04f-9dd8-40e4-add7-57008093529b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age']=df['Age'].fillna(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c91ff93c-ed48-435e-8dee-6d6b58905d8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age            False\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "91b80ded-9240-4718-8174-59ca286ae71f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5d0371b-99f8-4901-b8e5-f8d554aa5493",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Cabin'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da0a7268-3c14-4d3b-bd67-5fbc7b92fe24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    S\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "02d815b5-a292-40de-ae3e-41f8250bfd8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna('S',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4d12a0f-9fe6-4db9-a746-347c093cf2f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4465660-36a9-4046-a90d-7a1481aa5d85",
   "metadata": {},
   "source": [
    "- converting **categorical** features (encoding) :\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "faf96e28-628b-48d5-a48b-470cee374fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "male      577\n",
       "female    314\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8b7a8225-aee6-4b13-a6c4-2b1594716676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acac7908-a2c0-423a-a11b-1e87eca0814d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5206ced5-e40c-4cab-90cf-f5be26335b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sex=pd.get_dummies(df['Sex'],prefix='Sex_female',drop_first=True,dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "36307403-2dad-4156-86a1-15f94a231297",
   "metadata": {},
   "outputs": [],
   "source": [
    "Embarked=pd.get_dummies(df['Embarked'],prefix='Embarked',dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0860fed0-239e-48c1-a2bb-257b91bda2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sex']=Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b8a8db18-89d0-4ad8-ba38-c3cfc294c547",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([df,Embarked],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e89c10a6-d671-4b83-ac91-0372ab41932a",
   "metadata": {},
   "outputs": [
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       "     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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name  Sex        Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    1  22.000000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.000000      1   \n",
       "2                               Heikkinen, Miss. Laina    0  26.000000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.000000      1   \n",
       "4                             Allen, Mr. William Henry    1  35.000000      0   \n",
       "..                                                 ...  ...        ...    ...   \n",
       "886                              Montvila, Rev. Juozas    1  27.000000      0   \n",
       "887                       Graham, Miss. Margaret Edith    0  19.000000      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"    0  29.699118      1   \n",
       "889                              Behr, Mr. Karl Howell    1  26.000000      0   \n",
       "890                                Dooley, Mr. Patrick    1  32.000000      0   \n",
       "\n",
       "     Parch            Ticket     Fare Embarked  Embarked_C  Embarked_Q  \\\n",
       "0        0         A/5 21171   7.2500        S           0           0   \n",
       "1        0          PC 17599  71.2833        C           1           0   \n",
       "2        0  STON/O2. 3101282   7.9250        S           0           0   \n",
       "3        0            113803  53.1000        S           0           0   \n",
       "4        0            373450   8.0500        S           0           0   \n",
       "..     ...               ...      ...      ...         ...         ...   \n",
       "886      0            211536  13.0000        S           0           0   \n",
       "887      0            112053  30.0000        S           0           0   \n",
       "888      2        W./C. 6607  23.4500        S           0           0   \n",
       "889      0            111369  30.0000        C           1           0   \n",
       "890      0            370376   7.7500        Q           0           1   \n",
       "\n",
       "     Embarked_S  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "..          ...  \n",
       "886           1  \n",
       "887           1  \n",
       "888           1  \n",
       "889           0  \n",
       "890           0  \n",
       "\n",
       "[891 rows x 14 columns]"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "53613136-875d-4d43-a8fa-d063d36e248f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Embarked'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "598f3fad-3553-42c4-becb-61dd684bd274",
   "metadata": {},
   "outputs": [
    {
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      "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",
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       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
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       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0         A/5 21171   7.2500           0           0           1  \n",
       "1          PC 17599  71.2833           1           0           0  \n",
       "2  STON/O2. 3101282   7.9250           0           0           1  \n",
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     "execution_count": 29,
     "metadata": {},
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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae9d0984-5ef0-4472-a245-6f25abd43f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['PassengerId','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "06f75a20-e30e-468f-b709-34786010471a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked_C  Embarked_Q  \\\n",
       "0         0       3    1  22.0      1      0   7.2500           0           0   \n",
       "1         1       1    0  38.0      1      0  71.2833           1           0   \n",
       "2         1       3    0  26.0      0      0   7.9250           0           0   \n",
       "3         1       1    0  35.0      1      0  53.1000           0           0   \n",
       "4         0       3    1  35.0      0      0   8.0500           0           0   \n",
       "\n",
       "   Embarked_S  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "# Data Numirical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105db21f-4132-4d12-b930-40c8767aafdf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fca6edf5-52e4-4b42-be37-e7a0b4d69eaa",
   "metadata": {},
   "source": [
    "### Data Analysis(EDA) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d44fe46c-92f4-4f3b-8582-f3a9ec34ff4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "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": [
    "sns.countplot(x='Survived',data=df,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2c44427d-35c6-4fc9-a14f-15af259825b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_4256\\547781762.py:1: FutureWarning: \n",
      "\n",
      "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:blue'` for the same effect.\n",
      "\n",
      "  sns.countplot(x='Survived',data=df,hue='Sex',color='blue')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "sns.countplot(x='Survived',data=df,hue='Sex',color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eec0d122-e267-443d-a028-14847b8b9963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "df['Age'].plot.hist(bins=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "196ffbb7-2c9b-4a7e-95d2-28df75976472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "df['Fare'].plot.hist(bins=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "203d6e7c-f148-42de-9bc0-97268d9c33be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dbfa0599-2933-4f30-a52e-d1955114554f",
   "metadata": {},
   "source": [
    "## Bulding Model :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631163f4-22d0-4c94-b68a-091ea98b723d",
   "metadata": {},
   "source": [
    "- Train/Test split data :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2909135c-a778-4ce6-82ad-4ef48101cdef",
   "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_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Sex        Age  SibSp  Parch     Fare  Embarked_C  \\\n",
       "0           0       3    1  22.000000      1      0   7.2500           0   \n",
       "1           1       1    0  38.000000      1      0  71.2833           1   \n",
       "2           1       3    0  26.000000      0      0   7.9250           0   \n",
       "3           1       1    0  35.000000      1      0  53.1000           0   \n",
       "4           0       3    1  35.000000      0      0   8.0500           0   \n",
       "..        ...     ...  ...        ...    ...    ...      ...         ...   \n",
       "886         0       2    1  27.000000      0      0  13.0000           0   \n",
       "887         1       1    0  19.000000      0      0  30.0000           0   \n",
       "888         0       3    0  29.699118      1      2  23.4500           0   \n",
       "889         1       1    1  26.000000      0      0  30.0000           1   \n",
       "890         0       3    1  32.000000      0      0   7.7500           0   \n",
       "\n",
       "     Embarked_Q  Embarked_S  \n",
       "0             0           1  \n",
       "1             0           0  \n",
       "2             0           1  \n",
       "3             0           1  \n",
       "4             0           1  \n",
       "..          ...         ...  \n",
       "886           0           1  \n",
       "887           0           1  \n",
       "888           0           1  \n",
       "889           0           0  \n",
       "890           1           0  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32d2d7a6-f42c-43d8-87cc-71ae0b7a469b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(columns=['Survived'],axis=1)  ## all coulmns (تؤدى الى outcome)\n",
    "y=df['Survived']   ## outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "221b7926-443d-40e5-b600-83b986ee294e",
   "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>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_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked_C  Embarked_Q  \\\n",
       "0         3    1  22.000000      1      0   7.2500           0           0   \n",
       "1         1    0  38.000000      1      0  71.2833           1           0   \n",
       "2         3    0  26.000000      0      0   7.9250           0           0   \n",
       "3         1    0  35.000000      1      0  53.1000           0           0   \n",
       "4         3    1  35.000000      0      0   8.0500           0           0   \n",
       "..      ...  ...        ...    ...    ...      ...         ...         ...   \n",
       "886       2    1  27.000000      0      0  13.0000           0           0   \n",
       "887       1    0  19.000000      0      0  30.0000           0           0   \n",
       "888       3    0  29.699118      1      2  23.4500           0           0   \n",
       "889       1    1  26.000000      0      0  30.0000           1           0   \n",
       "890       3    1  32.000000      0      0   7.7500           0           1   \n",
       "\n",
       "     Embarked_S  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "..          ...  \n",
       "886           1  \n",
       "887           1  \n",
       "888           1  \n",
       "889           0  \n",
       "890           0  \n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "23e8b321-3ebf-4167-9053-ebad7b6ec757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "821b5144-bb2c-4bc8-b9fc-557861128dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10fb6bc2-a630-4dbd-95e8-302424d19407",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test, y_train, y_test = train_test_split(X, y,test_size=0.2,random_state=42)\n",
    "# random_state ===> shullif data (تلعب فى داتا)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "70ed5575-436b-4d11-b37e-de880e5ae23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 9)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "64b70612-ce12-4da7-a2ba-3c5989143f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179, 9)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fb309d20-a807-4ea3-b2da-78b19a294709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "26d3c4dd-3230-48db-9aaa-c573826b9e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "158c6fe1-a054-414f-92d5-37778e649c8d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "224da2c3-0701-4533-86b1-10cee1635182",
   "metadata": {},
   "source": [
    "- Model training and prediciting :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d661a9b9-9007-400a-8c47-8b6e6d9c78ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression   ## لان النتائج تحمل قيمتين فقط وهذا الالجورزم يتعامل مع الفئات الثنائيه"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bfd31dcd-05f5-4372-8e5a-6fcb362ae0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression(max_iter=5000,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "440155a4-4b1c-4337-acbf-4915f43fd57e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=5000, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=5000, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=5000, random_state=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)   ### fit ====> تدريب نموذج"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ea0703b4-2ab5-415c-a631-0d08ffaff2f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=model.predict(X_test)  ## بعد ما تدربناه نختبر جزء test\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "46c6349f-3555-4d7d-bc54-5fdd61398f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2362b564-18d8-433b-b43e-7a1df88161ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260bf36d-6b53-4d2e-91fc-b93531325872",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f4efdf7f-d405-4678-afeb-8f375223c0ee",
   "metadata": {},
   "source": [
    "- Model Evaluation :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "61fdfbf9-18f3-4d87-aa59-2db21de3a50e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ffdd5f11-cbf7-44f4-b7ad-0632b916839a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[90, 15],\n",
       "       [19, 55]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0f1edaee-7530-4fba-81fd-39e46ed2cb08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.86      0.84       105\n",
      "           1       0.79      0.74      0.76        74\n",
      "\n",
      "    accuracy                           0.81       179\n",
      "   macro avg       0.81      0.80      0.80       179\n",
      "weighted avg       0.81      0.81      0.81       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ca02229c-5921-4766-9887-c0bcb726fbcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.00558659217877\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_predict) *100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20d4814-ec48-4d57-8274-b848ad641568",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "413a0093-b63a-4f93-ae99-0f87954b5b6f",
   "metadata": {},
   "source": [
    "=================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9cfadd-d998-4f5c-a9a9-9883fe3ee78f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0ff3ee0-f619-4e76-b555-d5ec62d0b5e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a272af22-af8b-4bd1-b96d-0821c478a99b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c455daa-4291-4e50-a331-f942e648d207",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed50b5d0-90b5-41c4-885b-0af5182e455b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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