Logistic_regression_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing required libraries\n",
    "# For Manipulating the data\n",
    "import pandas as pd\n",
    "# For Mathametical calculations\n",
    "import numpy as np\n",
    "# For Logistic regression model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# For Visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "# For Model performance metrics\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "# For Test train split for validation\n",
    "from sklearn.model_selection import train_test_split\n",
    "# For saving the model\n",
    "import pickle\n",
    "# Visualization style\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <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",
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       "      <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset in a dataframe object\n",
    "url = \"http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv\"\n",
    "raw_df = pd.read_csv(url)\n",
    "# View first 5 rows of the data\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cavendish, Mr. Tyrell William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                           Name  \\\n",
       "count    891.000000  891.000000  891.000000                            891   \n",
       "unique          NaN         NaN         NaN                            891   \n",
       "top             NaN         NaN         NaN  Cavendish, Mr. Tyrell William   \n",
       "freq            NaN         NaN         NaN                              1   \n",
       "mean     446.000000    0.383838    2.308642                            NaN   \n",
       "std      257.353842    0.486592    0.836071                            NaN   \n",
       "min        1.000000    0.000000    1.000000                            NaN   \n",
       "25%      223.500000    0.000000    2.000000                            NaN   \n",
       "50%      446.000000    0.000000    3.000000                            NaN   \n",
       "75%      668.500000    1.000000    3.000000                            NaN   \n",
       "max      891.000000    1.000000    3.000000                            NaN   \n",
       "\n",
       "         Sex         Age       SibSp       Parch    Ticket        Fare  \\\n",
       "count    891  714.000000  891.000000  891.000000       891  891.000000   \n",
       "unique     2         NaN         NaN         NaN       681         NaN   \n",
       "top     male         NaN         NaN         NaN  CA. 2343         NaN   \n",
       "freq     577         NaN         NaN         NaN         7         NaN   \n",
       "mean     NaN   29.699118    0.523008    0.381594       NaN   32.204208   \n",
       "std      NaN   14.526497    1.102743    0.806057       NaN   49.693429   \n",
       "min      NaN    0.420000    0.000000    0.000000       NaN    0.000000   \n",
       "25%      NaN   20.125000    0.000000    0.000000       NaN    7.910400   \n",
       "50%      NaN   28.000000    0.000000    0.000000       NaN   14.454200   \n",
       "75%      NaN   38.000000    1.000000    0.000000       NaN   31.000000   \n",
       "max      NaN   80.000000    8.000000    6.000000       NaN  512.329200   \n",
       "\n",
       "          Cabin Embarked  \n",
       "count       204      889  \n",
       "unique      147        3  \n",
       "top     B96 B98        S  \n",
       "freq          4      644  \n",
       "mean        NaN      NaN  \n",
       "std         NaN      NaN  \n",
       "min         NaN      NaN  \n",
       "25%         NaN      NaN  \n",
       "50%         NaN      NaN  \n",
       "75%         NaN      NaN  \n",
       "max         NaN      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data description\n",
    "raw_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for number of Passengers survived\n",
    "raw_df['Survived'].value_counts().plot(kind= 'bar', color= [\"#ffc299\",\"#b3d9ff\"])\n",
    "# Plot title\n",
    "plt.title('Number of Passengers survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart for number of Passengers by Gender\n",
    "raw_df['Sex'].value_counts().plot(kind= 'pie')\n",
    "plt.title('Number of Passengers by Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Horizontal Bar plot for number of Passengers port of embarkation\n",
    "raw_df['Embarked'].value_counts().plot(kind= 'barh', color= [\"skyblue\", \"orange\"])\n",
    "# Plot title\n",
    "plt.title('Number of Passengers by port of embarkation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>female</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>male</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Sex Embarked  Survived\n",
       "0  22.0    male        S         0\n",
       "1  38.0  female        C         1\n",
       "2  26.0  female        S         1\n",
       "3  35.0  female        S         1\n",
       "4  35.0    male        S         0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select a subset of data with 4 columns\n",
    "include = ['Age', 'Sex', 'Embarked', 'Survived'] \n",
    "df = raw_df[include]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         0\n",
       "Sex         0\n",
       "Embarked    0\n",
       "Survived    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove null values\n",
    "df=df.dropna()\n",
    "# Check for null values in all the columns\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</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>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Survived  Sex_female  Sex_male  Embarked_C  Embarked_Q  Embarked_S\n",
       "0  22.0         0           0         1           0           0           1\n",
       "1  38.0         1           1         0           1           0           0\n",
       "2  26.0         1           1         0           0           0           1\n",
       "3  35.0         1           1         0           0           0           1\n",
       "4  35.0         0           0         1           0           0           1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Dummy varaibles for the categorical variables in the data\n",
    "proc_df = pd.get_dummies(df, columns=[\"Sex\",\"Embarked\"])\n",
    "proc_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test train split\n",
    "y=proc_df.pop(\"Survived\")\n",
    "x=proc_df\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "model = LogisticRegression()\n",
    "# Fit the model\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.7811244979919679\n",
      "Testing Accuracy : 0.7757009345794392\n"
     ]
    }
   ],
   "source": [
    "# Predict values for the test data\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
    "print(\"Testing Accuracy :\", model.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Not Survived  Survived\n",
      "Not Survived           102        24\n",
      "Survived                24        64\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.81      0.81       126\n",
      "           1       0.73      0.73      0.73        88\n",
      "\n",
      "    accuracy                           0.78       214\n",
      "   macro avg       0.77      0.77      0.77       214\n",
      "weighted avg       0.78      0.78      0.78       214\n",
      "\n",
      "Accuracy: 77.57%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "# dataframe of cm to change the labels\n",
    "df_cm = pd.DataFrame(cm, index = [i for i in ['Not Survived','Survived'] ],\n",
    "                  columns = [i for i in ['Not Survived','Survived']])\n",
    "print(df_cm)\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(f\"Accuracy: {accuracy_score(y_test, y_pred)*100:.2f}%\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Saved\n"
     ]
    }
   ],
   "source": [
    "# save the model into disk\n",
    "pickle.dump( model, open( \"model.pkl\", \"wb\" ) )\n",
    "print(\"Model Saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model columns saved\n"
     ]
    }
   ],
   "source": [
    "# save the model columns into disk\n",
    "model_columns = list(x.columns)\n",
    "pickle.dump( model_columns, open( \"model_columns.pkl\", \"wb\" ) )\n",
    "print(\"Model columns saved\")"
   ]
  }
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