Logistic_regression_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 : Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>E</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "5         0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "6         0       1    male  54.0      0      0  51.8625        S   First   \n",
       "7         0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "8         1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "9         1       2  female  14.0      1      0  30.0708        C  Second   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  \n",
       "5    man        True  NaN   Queenstown    no   True  \n",
       "6    man        True    E  Southampton    no   True  \n",
       "7  child       False  NaN  Southampton    no  False  \n",
       "8  woman       False  NaN  Southampton   yes  False  \n",
       "9  child       False  NaN    Cherbourg   yes  False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df=pd.read_csv(r\"E:\\Git_repo\\titanic.csv\")\n",
    "titanic_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      "survived       891 non-null int64\n",
      "pclass         891 non-null int64\n",
      "sex            891 non-null object\n",
      "age            714 non-null float64\n",
      "sibsp          891 non-null int64\n",
      "parch          891 non-null int64\n",
      "fare           891 non-null float64\n",
      "embarked       889 non-null object\n",
      "class          891 non-null object\n",
      "who            891 non-null object\n",
      "adult_male     891 non-null bool\n",
      "deck           203 non-null object\n",
      "embark_town    889 non-null object\n",
      "alive          891 non-null object\n",
      "alone          891 non-null bool\n",
      "dtypes: bool(2), float64(2), int64(4), object(7)\n",
      "memory usage: 92.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#check data\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passengers which were present in the ship :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check number of passengers which were present in the titanic\n",
    "print(\"Number of passengers which were present in the ship :\")\n",
    "len(titanic_df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 : Analyzing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2269527ff88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how may survived & how many did not survived |          0 = did not survived, 1 = survived\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(x=\"survived\",data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226965c5dc8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the passengers which survived & dead. How many were man & how many were female\n",
    "sns.countplot(x=\"survived\",hue=\"sex\",data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22696660908>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we will see which type of passenger class survived the most did it was passengers of class First, Second & Third\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.countplot(x=\"survived\",hue=\"class\",data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the above graph we can see that people who were in 3rd class died more than 2nd & 1st class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226966ea188>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we will see the age of the passengers which were presend on the ship\n",
    "titanic_df[\"age\"].plot.hist(rwidth=0.97)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226962ab8c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let us plot graph of the fare\n",
    "plt.figure(figsize=(10,7))\n",
    "titanic_df[\"fare\"].plot.hist(rwidth=0.97,bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 : Data Wrangling (Cleaning data by removing unwanted NaN values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check how many NaN values are present in the Dataset\n",
    "titanic_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226967c2988>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vi77WbrPZgYNkGrY0SJI0qPFpaTBpkCRpQJXmaajCpEGSpAGNT8pg0iBJi2YBs7o0ToWQzggpSZJasaVBkqQBWdMgSRPMbgJ1aXxSBpOGwdk3Ko0fP7fq0jjVNJg0DMyTgCRNN7snJElSK7Y0qDWbOSVpuo1RQ4NrT0iStK5+15648bbTF32t3Xzpfq49IUnjwBZCdWt8mhpMGiRJGtT4zLNo0iBJ0oAshJQkSS2ZNEiSpFbsnpAkSW2M0ZhLkwZJkgZkTYMkSWrJ7glJktTK+LQ0jE96I0mSBmVLgyRJgxqf+3eTBklaJKdzVpcshJSkCebaE+qWSYMkSWrF7glJktSKLQ2SJKmFsKVBbdk3KknTzpYGteTFW5KmnGtPSJKkduyeUEt2T0jSdHOeBrXmxVuSpp1JgyRNLJN9dcvuCUmaWHYrqlu2NEiSpBacp0GtecciSdPOlga15MVbkqadSYMkSWplfLonxudIJUnSoGxpkCRpQDFG00hHZs7z5Wvn+6IkSRNou16v4sn3F32tDR44SKZhS4MkLZKjntStOpUCEbE/8B5gKXBCZh4z6+ubAR8HHgP8FDgkM7/f/5FKkqSWYgMeC+wxYinwfuAAYFfgsIjYddbLjgCuy8wHA+8G3r7Qfm1pkKRF8o5fXao0udPjgCsz8yqAiDgZOAi4fOQ1BwFHNf/+N+B9ERE5T93CAknDhvfrRMSyzFyxod9v3I0zpnEnN6ZxJzemcTd22y/6WhsRy4BlI0+tmPXzbg9cM7K9BthjduCZ12TmrRHxc2Bb4H/WF7dm98SyhV9i3DGMadzJjWncyY1p3AmTmSsy87Ejj9kJ0lyJyOwWhDavWYc1DZIkTZ41wI4j2zsA167vNRGxCbAl8LP5dmrSIEnS5FkN7BwRO0XEXYBDgZWzXrMSeGnz74OB/5ivngHqFkIO1Zc0TXGn6WedtrjT9LNOW9xp+lmnMe5GoalReCVwGmXI5YmZeVlELAfOz8yVwEeAT0TElZQWhkMX2u8CkztJkiQVdk9IkqRWTBokSVIrJg2SJKkVkwZJktTKREwjHRHPB07NzBsi4o3AbsDfZeaFFWO+DXhrZt7abN8TeE9mHl4r5qz4d8vMX/URayTmfSlTkyawOjP/u4eYmwHPAx7IyPs1M5dXjrsnsHNmnhQR9wbunplXV4r1XuaZUCUzj6wRdyT+7wJrMvOmiHgK8Ajg45l5fcWYOwDvBfYEbge+Brw6M9fUijkS+3eAvwe2y8wDmvn4n5CZH6kY8zGZecGs556dmZ+vFbOJMcjnp4n9AMpn6EsRsQWwSWbeUDuu6uqkpSEiboiIX6zv0UWMBbypSRj2BJ4OfAw4vnLMTYDzIuIREbEfZUzsBQt8z50WEU+MiMuBK5rtR0bEB3qI+3Lgm8BzKeN5z42IP6odF/gcZX70W4FfjTyqiYi3AH8F/HXz1KbAJyuGPJ/y3tmckvB+r3k8CritYtwZ/w7cFhEPpgzB2gn458oxT6KMEb8fZSrbzzfP9eGjlGFo2zXb/xd4TeWYH46Ih89sRMRhwBsrx4QBPj8AEfHHlLUMPtQ8tQPw2coxfy8izoyIbzfbj2huItWlzOzsASwH/hS4B3BP4BXA67uMsZ64FzX//wfghaPPVY67D/AbyixbD64dr4l5HmUGr4tGnvt2D3G/C2w7sr0t8N0e4lb/2eaIeTFletXR3/ElPcQ9C9h0ZHtT4Kwe4l7Y/P91wKuaf1f9/AAXt3muUuzVs3/G2rGBBwEXArsAfwx8Fdiyh5+198/PzO8TuMus3/GllWOeTWkJ7fXcOG2Prmsanp6ZH8jMGzLzF5l5PKVprLYfRsSHgBcAq5omuar1GhHx+5R1ypcDX6asDrbdvN/Ukcy8ZtZTfdyNrgFGmxZvYN3FUGr5+ugdWk9uznLGSShdQT3F3Y6ScM+4O2vvhmu6pbnzfSnwhea5TSvH/J+IeHFELG0eLwZ+WjnmjF9FxLas/fs+Hvh5zYBZVho8lNKqczCwX2ZWjdkY4vMDcFNm3jyz0UxRXHtSoLtm5jdnPXdr5ZhTp+uahtsi4kXAyZQ3yGH0c0F7AbA/8M7MvD4i7ke5a6rpncDzM/NygIh4LvAfwEMrx70mIp4IZDM16JE0XRWV/ZDSHfM5yt/2IOCbEfFagMx8V5fBIuLSJs4mwOERcRVwE6UFIDPzEV3Gm+VTTRK6VdPM+kfAhyvGm3EMcFFEnNVs78XaZWtrOhz4E+DozLw6InaibncMlN/p+4B3U/7OX2+e68NrKV0jvxsR5wD3plzIOzfyPp6xDWV2vvMigsrvYyg1Iy+LiKvp7/MDcHZE/A2wRUTsS2mBrlq/QUlEf5e1yeDBwI8qx5w6nc4IGREPpNx9P4nyhzsHeE1mfr+zIHPHHaKQa2lm3jbruW0zs+rdUkTci/I73odyAjidUkBWO+5b5vt6Zr6143gPWCDeD7qMN0f8fYH9KL/j0zLzjJrxRuLel7XL156XPRSbzoq/NbBjZl7SZ9y+NXe+D6H8fb+bmbdUijP0+3jO+D3EXQIcwchnCDghu7zg/HbMB1Gmjn4icB1wNfDi2tefaTMR00hHxMXAYykVwqdR7iIekpnPqBhzpgJ7+8zcv48K7I1Fc0K4e2ZWL3Jtmo4vy6bqOiLuAeyamedVireUkiTsU2P/C8QO4EXAgzJzeUTcH7jvHE2uXcf9MnAgpVXnYuAnwNmZ+dqKMe9N6dt/IOtW9VdrbWhaA9crMz9dMXav7+ORuMsp9RNfzx5HW0XEHwCrMvOmvmKOxL4bsCQdqVFF1y0Nv0cZtfA7mfmwiHgEcGBm/l1nQeaOe2Fm7hYRrwd+k5nvjYiLMvPRFWN+kVLt/beZ+cjmzuWizKzafxgRx83x9M8pC5B8rmLcf6Y0Yd9GqfTfEnhXZv5jrZhN3IuA3WbuUJqE5fzM3K1izJXAS3rqcx6Nezxl+OFTM3OX5q7/9MzcvXLcizLz0c0ImR0z8y0RcUnNJuyI+DrlYnYBI12YmfnvFWPONzojKycsvb+Pmzh/ROmieAKlDumrwFdqniuauCcBTwW+QumuPi2b4ekVYw42vHSadF3T8GFKLcGHADLzkuZiUzVpYG0h1x8Cz26eq13Ida/M/FRE/DXcsaJYH/Ubm1PqJk5ptp8HXAYcERF7Z2atoWO7ZuYvmpqVVZQhiRcAVZMGSmJ7R2abmbc3CVpNNwKXRsQZjAxPy8rzJQB7NMnvRU2865q6ldo2aeqAXgD8bQ/xoBSt/VVPsQDInuZQWY8h3sdk5onAiU231wuAvwSWsW7BbY24h0fEpsABwAuBD0TEGZn58ophP0e5gbqAUr+hCrp+0941M79ZWlnv0Ef16hCFXL1XYDceTLkTnZlU6nhKXcO+wKUV427anASeA7wvM2+JiD76tq6KiCNZO+/GnwJXVY75f5pH325pukdm3lP3prQ81Lac0q33tcxc3fQNf69yzC9ExDMyc1XlOHOKiGcC/4uShAPV70iHeB8TEScAuwL/j9LKcDBl6Gd1zTnii5T38xaU4umaScMOmbl/xf2L7pOGQapXmxEMR45sX02pRK+ptwrsWbYH7sbaBOVulJntbouImtn1h4DvA98CvtIUWPUxcdefAMdRJsJJ4EzKnVI1mfmxmvufx3HAZ4D7RMTRlPfTm2oHzcxTWNtyNTM8sMpQ6Yi4gbWjCf6mec/O3FhkZt6zRtxZx/BB4K7A3sAJlN9z1boRBngfN7aljNa4HvgZ8D+1uwkAImJ/yhDTvSlD0k+gtHTU9PWIeHhm1rx5mnpd1zTMVb36oh4qdXemTOy0K+veOTyoYsznU+7OdqScYPegzExZNYuPiCMoJ54vU6qSf59SkPkvwFGZWXuo6eixbFLzBNTcdR+Zme+uFWM9cXt/P43EfijwNMrf9szMrD6cNiI2p1S6z77zrtnH/wnKne9X+/gZZ8W+JDMfMfL/uwOfzsz9+jyOPkXELpTZcv8cWJqZO1SOdzKlluGLfRVDRpkp98GU606fw0unStctDT/IzH0GqF49CXgLZcz33pTuipj3O+68N2XmKU2x2j7AsZSmxz3m/7Y7JzM/0jT5vQT4DqVrYk1TGV0tYWi6Yt5CKapKyloBy6k4IU/TenIQ5e/apyHeT0TEEc3om++MPHdMZr6hcuhPNDGfTvmbvoj6c3+cRHkvHdfcbFxESSDeUzkulFlcAX4dZUK2n1Kmzq5mwCLxZwFPptxcbE2ZS+arNWMCZOahtWPM4YABYk6drmdNvDoiVgCPB37Z8b7ns0VmnklpOflBZh5Fqdytaabo8ZnAB5tq5OpFa02F+2nAGyjz5X+EfiYAOpkyFO95lObcnwD/2kPccyLifRHx5IjYbeZROeYQ7yeAg5tCUwCirCly7x7iPjgz3wT8qumaeSZQdRRQZv4HcDSl++UEYHfKtPN9+EJEbEUp4r2Q0u12cuWYH6asZXILlCJxSvN9bQdQfsbnZeZDM/Pwpjiyioj4WvP/mfWI1vl/rbhwx9wTW1GK4Z8NbFW7lXsadd3S8BDKH+vPgI9ExBeAkzPzax3Hme3GZgjT9yLilZTZC+9TOebM1NX7AG+PHqaubryacoI9NzP3bpqzO51YaT22ycy3jWz/XUQ8p4e4T2z+P1qkltS9iA/xfoKyGNjKiLidcrL/WWb+aQ9xZyY2uj4iHgb8N2XYWjURcSalHucblDvf3TPzxzVjzhh5H/97c47avIfhtYMUiWfmn0WZU2b3Jtn+Zs3fc2bu2fy/6uiMuUTEqylzf8zMt/HJiFiRme/t+1gmWadJQ2b+BvgUZRrerSkzF55NKcSp6TWUwqYjgbdRLigvrRxziKmrAW7MzBsjgojYLDO/ExEP6SHuWRFxKOXvC6W1ofoIg8zcu3aMGRHxicx8CWXoVm/vp4jYZmTz5ZTVAM8BlkfENpn5s1qxGyuaz+ubKMW9dwfeXDnmJcBjgIdRinqvj4hvNOeQqppamWcyMp4/ypTOnU6FPssgReJN7dU7WVsD9d6IeF1m/luleNvM9/XK7+UjKMOWf9Ucy9spSalJQ4c6nxEyIvYCDqHcKa0G/rXmhC3TJiI+Q+ljfw3lYnYdZWXEKrNfjlS7B+XOcKZbZinwy56q3XsZHtcUUh1AuXA+hVl1DLVOeFHWBRj9II7GzT4KMIfSFCEeTpk/4L6ZuVkPMVfRzMXByJDW7Hgq9FkxhyoS/xaw70zrQjOM90uZ+chK8Wbey+u8h1lblFizOP1SSovVjc325pQVTYdYsGtiddrS0LxhLqbcjb4uK09bGhGfZ56V0zLzwJrxh5CZf9D886goCxttCZxaMV7vzYyjeh4e90HK7/JBlAligpETXvN85zJzp6Y75AmZeU6NGHOJZrGx9al55910+zyZ0trwA+BEeijQa+zQV0X9rN/xKsry50sok4Y9D6jZugGlIH20O+KnVOxGzcw7CkqbVoedGUn2KzuJshDYZ5rt51BqvtShrmsaHpk9rEcw4p09xtroZObZtWNExEObLpA5iw9rDzEFnjgyPO6tEXEsa/ssO5WZx1Gq+Y/PzL6K8mZi3x4R76RM99uXmYRw9p3hzHM1bUG5YF7Qx7wBs3wxIvbLzNN7iDXzO34IpRbpc5Tf9UsoUyzXdmpEnEYZkg2lFfiLtYM2BduvBnag3Eg+nrKS6dNqxczMd0VZR2VPyu/48My8qFa8adVJ90REvD4z3xER72WOk01Wnn63GeL5m8y8vdleCmyWmb+uGXcaNIVEy2Ltcs0w8jfOzKqjCiLivMzcIyLOpRQK/hT4dmbuXDPuECLirZS+/k9n1/2G88f9GGWl1Oub7a2BY2vO0zCkKIspfZJyx30La5vOq3W1RcTplBEMowtWnZI9zGAYZaGumQvpVzLzMwt8SxcxL2VtwfajZgq2M/OQCrGGrKOYOl21NMyM6T6/o/0t1pmUUQwzwzy3oMxf8MT1fodaycyZWeuOB07Nsv7Em4DdKEWCtc0Mj3sHpcsASjfFJHotpW7k1oi4kR4uZo1H5Mgy8lnWvKi22NtG4FhKi86lPSZn9wduHtm+mcojVKAUA2ZZ4+PTczxXU58F2xewbmvZzN+0arfitDWK8awAAAioSURBVOokacjMzzf/vGSg5qDNM/OOeSEy85cRcdcBjmOSvTHLAl17Uta56GUyK0oX1Cso/d8zw/OOn/c7xtSA9SNLImLrzLwO7rhzq76Y0oC+R2mt6q01hzKB1jeb/vYE/gDoY7ryfSmLy406YI7nuramSfY/C5wREdcB19YINHAdxdTp+sTwrmbo4SmU+Rku63j/6/OriNhtpn89Ih7L2lnf1I3fmswqIo7qIe7HKEv6ziwJfhjwcerPYz+IpmtgnZNeZtbu+z6WMm//v1EuaC+gTLw0qX4EfDnKzKp3THFcs/AzM49u4j25eapqf3tEvIKyKNaDIuKSkS/dgzKct6q+C7ZhmDqKaVRjyOXMEqyHAPekDLmsPVXq7pQZ3a6lnPS2Aw7JzAvm/Ua11kyC80NKN9BjKEnZN2sN3RqJ+63ZMeZ6bhKs56T3jdp1I03sXSlDeGfWvLi8dsyhRMRb5nq+5pDLvkXElpRpo/+BMnvsjBtG+/hHW5jGXZ91FNOs86Thjh1HPBx4PeXiXXV65WY87qsoc+f/gmZCj5nxurrzmu6e/Sn9wN9rWpQeXrsCPSI+SmnZOLfZ3gN4afYzU2KvPOltHCLivZn5qqGPow8RcWFm1p6WvRcRsTozd4+IiymTPN0UERdn5qOGPrZJ0vU8DbtQWhgOplS5nwz8RZcx1uPjlGRhpkn1MEof4vN7iD0VmpEonx7Z/hE9zGhHqZn4w4j4r2b7/sAVzQU2+xpv35OhZvvUup409AH0qPpCbD3qrY5imnVd03ASZTzwfpnZ5x/rIbOaq89qZkLT+Ks+JG0j4klPfeuzGLSqIeooplFnSUMzN8J/Zj9L2852UUQ8flYTdm8z66me2tPsbkw86Und6GPiu2nVWdKQmbdFxLYRcZfMvHnh7+jUNDVha4I1M2/uSbkDPGeAz5Imq8l+IdP0s6oDXXdP/AA4JyJWUuZWB+oOZWpMUxO2JlREvJlShzNTO3JSRJxSe/TRtImI52fmKfM8N0RraRURcURmfmTWc8dk5syICocjalE6HT0xDUOZpFoi4grg0bl2lb4tgAszc5dhj2yyzDViYJJGEYxq5ob4ZGb+U7P9AcoU+0cMe2QaV522NJgcSHfK9ymTOs0MFd4M+M/BjmbCRMQBwDOA7SPiuJEv3RPoe9GsvjwXWBkRt1NmgvzZJA5XVn+6HnJ5FnMvWFV9chppXI0s9HYTcFlEnNFs7wt8bchjmzDXUtYpOJC165hAmXH0zwc5okpmLeL0csqInHOA5RGxjYs4aUN13T3xmJHNzSnrxd+ama/vLIg0YSLipfN9PTP7WKNgakTEJgMsx92riLiadW/gRgseMzNdxEkbpNqMkHcEiDg7M/eqGkSSFjAzmmp9X5+0UVYRsQR4QmY6/Fyd6bp7YrRJbAnwWOC+XcaQJlVEPIuy3PgDKJ/NvpbGnhbPGvoA+pSZt0fEOynLgEud6Lp7YqZJLIBbKIVdyzPTfllpARFxJaVw7dKel23WhIqItwKXAJ/2PaUudD1Pw18Bp2bmLyLiTcBuwK87jiFNqmuAb3tyrysibmBtN8VdgE2BX01oi85rgbsBt0bEjdh6pTup66ThjZn5qYjYk1L5fSxwPGXGRknzez2wKiLOpoykAHqZHG2qZOY9Rrcj4jnA4wY6nKpm/6zSnbWk4/3d1vz/mZTljD9HyeQlLexoSsvc5sA9Rh6qKDM/C0zssPCI2DoiHhcRvz/zGPqYNL66bmn4YUR8CNgHeHtEbEb3iYk0qbbJzP2GPohJFxHPHdmcKdieyC6hiHg58GpgB+Bi4PHAN5jgJEl1dX1BfwFwGrB/Zl4PbAO8ruMY0qT6UkSYNNT37JHH0ymTOx006BHV82pgd+AHmbk38GjgJ8MeksZZ9XkaJLXTFOjdFbiZMvrIojXdKRGxOjN3j4iLgT0y86aIuDgzHzX0sWk8dd09IWnDbQm8CNgpM5dHxP2B+w18TBNjZLruOWXmkT0eTl/WRMRWlGmkz4iI6yjTaUsbxJYGaSMREccDtwNPzcxdImJr4PTM3H3gQ5sII9N1PwnYFfjXZvv5wAWZOVHrT8wWEXtREtNTM/PmoY9H48mWBmnjsUdm7hYRFwFk5nUR4eijjsys4RERLwP2zsxbmu0PAqcPeGhVRcRuwJ6UVpZzTBh0ZziyQdp43BIRS2ma0CPi3pSWB3VrO9Ydynr35rmJExFvBj4GbAvcCzgpIt447FFpnNk9IW0kIuJFwCGUmVQ/BhxMmTDtlEEPbMJExOHAUcBZzVN7AUdN4mqiEXEF8OjMvLHZ3gK4MDN3GfbINK7snpA2Epn5TxFxAfA0ysiJ52TmFQMf1sTJzJMi4jTgJcAVwKlMbnHg9ymThd3YbG8G/OdgR6OxZ9IgbUQy8zvAd4Y+jkk2DRMejYwUuQm4LCLOaLb3BVxAUBvM7glJUyUiLqVMeHRuZj4qIh4KvDUzDxn40DozMlJkTpPYFaN+2NIgadrcmJk3RgQRsVlmficiHjL0QXXJpEC1mDRImjZTM+FRRDwLeBvwAMr53llGdafYPSFpak36hEcRcSXwXODS9GSvDpg0SNKEioizgKdlpvN9qBMmDZI0oSJid0r3xNmUkRQAZOa7BjsojTVrGiRpch0N/JIyV4NTkutOM2mQpMm1TWbuN/RBaHK49oQkTa4vRYRJgzpjTYMkTaiIuAG4K3AzcAsOudSdZPeEJE2uLYEXATtl5vKIuD9wv4GPSWPMlgZJmlARcTxlefWnZuYuEbE1cHpm7j7woWlM2dIgSZNrj8zcLSIuAsjM6yLCURTaYBZCStLkuiUillJWuCQi7k1peZA2iEmDJE2u44DPAPeJiKMpy2L//bCHpHFmTYMkTbBm6e+nUUZOnJmZVwx8SBpjJg2SJKkVuyckSVIrJg2SJKkVkwZJktSKSYMkSWrFpEGSJLXy/wHZxH55LzX2GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets draw a heap map for better understanding\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(titanic_df.isnull(),yticklabels=False,cmap=\"YlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22696956b08>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now lets take age column & fix the NaN values in that\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.boxplot(x=\"class\",y=\"age\",data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now for logistic regression lets analyze the dataset\n",
    "titanic_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226962a9d08>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To get accurate data lets take mean of the columns & put them in to missing values eg : \"age\"\n",
    "plt.figure(figsize=(10,5))\n",
    "titanic_df.age=titanic_df.age.fillna(titanic_df.age.mean())\n",
    "sns.heatmap(titanic_df.isnull(),yticklabels=False,cmap=\"YlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As you can see that we had filled NaN in age with mean values\n",
    "##### Now the deck column is not usefull for us in any way to predict the survival rate. So we can drop it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex        age  sibsp  parch     fare embarked  \\\n",
       "0           0       3    male  22.000000      1      0   7.2500        S   \n",
       "1           1       1  female  38.000000      1      0  71.2833        C   \n",
       "2           1       3  female  26.000000      0      0   7.9250        S   \n",
       "3           1       1  female  35.000000      1      0  53.1000        S   \n",
       "4           0       3    male  35.000000      0      0   8.0500        S   \n",
       "..        ...     ...     ...        ...    ...    ...      ...      ...   \n",
       "886         0       2    male  27.000000      0      0  13.0000        S   \n",
       "887         1       1  female  19.000000      0      0  30.0000        S   \n",
       "888         0       3  female  29.699118      1      2  23.4500        S   \n",
       "889         1       1    male  26.000000      0      0  30.0000        C   \n",
       "890         0       3    male  32.000000      0      0   7.7500        Q   \n",
       "\n",
       "      class    who  adult_male  embark_town alive  alone  \n",
       "0     Third    man        True  Southampton    no  False  \n",
       "1     First  woman       False    Cherbourg   yes  False  \n",
       "2     Third  woman       False  Southampton   yes   True  \n",
       "3     First  woman       False  Southampton   yes  False  \n",
       "4     Third    man        True  Southampton    no   True  \n",
       "..      ...    ...         ...          ...   ...    ...  \n",
       "886  Second    man        True  Southampton    no   True  \n",
       "887   First  woman       False  Southampton   yes   True  \n",
       "888   Third  woman       False  Southampton    no  False  \n",
       "889   First    man        True    Cherbourg   yes   True  \n",
       "890   Third    man        True   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Droping the deck column\n",
    "titanic_df=titanic_df.drop(\"deck\",axis=1)\n",
    "titanic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22696acac08>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now we can see our dataframe is clean to predict the survival & implement the logistic regression\n",
    "plt.figure(figsize=(10,5))\n",
    "titanic_df.age=titanic_df.age.fillna(titanic_df.age.mean())\n",
    "sns.heatmap(titanic_df.isnull(),yticklabels=False,cmap=\"YlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       0\n",
       "pclass         0\n",
       "sex            0\n",
       "age            0\n",
       "sibsp          0\n",
       "parch          0\n",
       "fare           0\n",
       "embarked       2\n",
       "class          0\n",
       "who            0\n",
       "adult_male     0\n",
       "embark_town    2\n",
       "alive          0\n",
       "alone          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the presence of NaN value in Data\n",
    "titanic_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       0\n",
       "pclass         0\n",
       "sex            0\n",
       "age            0\n",
       "sibsp          0\n",
       "parch          0\n",
       "fare           0\n",
       "embarked       0\n",
       "class          0\n",
       "who            0\n",
       "adult_male     0\n",
       "embark_town    0\n",
       "alive          0\n",
       "alone          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now our data is clean but there is one problem \n",
    "##### To implement logistic regression we have to convert string into categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will use pandas to do that\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now above table means in female column : 0 = not female i.e male & 1 = female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male  embark_town alive  alone  \n",
       "0    man        True  Southampton    no  False  \n",
       "1  woman       False    Cherbourg   yes  False  \n",
       "2  woman       False  Southampton   yes   True  \n",
       "3  woman       False  Southampton   yes  False  \n",
       "4    man        True  Southampton    no   True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "     male\n",
       "0       1\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       1\n",
       "..    ...\n",
       "886     1\n",
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       "888     0\n",
       "889     1\n",
       "890     1\n",
       "\n",
       "[889 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "d_sex=pd.get_dummies(titanic_df['sex'],drop_first=True)\n",
    "d_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     Q  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",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we can drop any of these column from C, Q, S\n",
    "# so we will get S=[1,0], Q=[0,1] & C=[0,0]\n",
    "d_emb=pd.get_dummies(titanic_df['embarked'],drop_first=True)\n",
    "d_emb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     Second  Third\n",
       "0         0      1\n",
       "1         0      0\n",
       "2         0      1\n",
       "3         0      0\n",
       "4         0      1\n",
       "..      ...    ...\n",
       "886       1      0\n",
       "887       0      0\n",
       "888       0      1\n",
       "889       0      0\n",
       "890       0      1\n",
       "\n",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_class=pd.get_dummies(titanic_df['class'],drop_first=True)\n",
    "d_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
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       "      <td>887</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
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       "      <td>female</td>\n",
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       "      <td>no</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex        age  sibsp  parch     fare embarked  \\\n",
       "0           0       3    male  22.000000      1      0   7.2500        S   \n",
       "1           1       1  female  38.000000      1      0  71.2833        C   \n",
       "2           1       3  female  26.000000      0      0   7.9250        S   \n",
       "3           1       1  female  35.000000      1      0  53.1000        S   \n",
       "4           0       3    male  35.000000      0      0   8.0500        S   \n",
       "..        ...     ...     ...        ...    ...    ...      ...      ...   \n",
       "886         0       2    male  27.000000      0      0  13.0000        S   \n",
       "887         1       1  female  19.000000      0      0  30.0000        S   \n",
       "888         0       3  female  29.699118      1      2  23.4500        S   \n",
       "889         1       1    male  26.000000      0      0  30.0000        C   \n",
       "890         0       3    male  32.000000      0      0   7.7500        Q   \n",
       "\n",
       "      class    who  adult_male  embark_town alive  alone  male  Q  S  Second  \\\n",
       "0     Third    man        True  Southampton    no  False     1  0  1       0   \n",
       "1     First  woman       False    Cherbourg   yes  False     0  0  0       0   \n",
       "2     Third  woman       False  Southampton   yes   True     0  0  1       0   \n",
       "3     First  woman       False  Southampton   yes  False     0  0  1       0   \n",
       "4     Third    man        True  Southampton    no   True     1  0  1       0   \n",
       "..      ...    ...         ...          ...   ...    ...   ... .. ..     ...   \n",
       "886  Second    man        True  Southampton    no   True     1  0  1       1   \n",
       "887   First  woman       False  Southampton   yes   True     0  0  1       0   \n",
       "888   Third  woman       False  Southampton    no  False     0  0  1       0   \n",
       "889   First    man        True    Cherbourg   yes   True     1  0  0       0   \n",
       "890   Third    man        True   Queenstown    no   True     1  1  0       0   \n",
       "\n",
       "     Third  \n",
       "0        1  \n",
       "1        0  \n",
       "2        1  \n",
       "3        0  \n",
       "4        1  \n",
       "..     ...  \n",
       "886      0  \n",
       "887      0  \n",
       "888      1  \n",
       "889      0  \n",
       "890      1  \n",
       "\n",
       "[889 rows x 19 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df=pd.concat([titanic_df,d_sex,d_emb,d_class],axis=1)\n",
    "titanic_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived        age  sibsp  parch  male  Q  S  Second  Third\n",
       "0           0  22.000000      1      0     1  0  1       0      1\n",
       "1           1  38.000000      1      0     0  0  0       0      0\n",
       "2           1  26.000000      0      0     0  0  1       0      1\n",
       "3           1  35.000000      1      0     0  0  1       0      0\n",
       "4           0  35.000000      0      0     1  0  1       0      1\n",
       "..        ...        ...    ...    ...   ... .. ..     ...    ...\n",
       "886         0  27.000000      0      0     1  0  1       1      0\n",
       "887         1  19.000000      0      0     0  0  1       0      0\n",
       "888         0  29.699118      1      2     0  0  1       0      1\n",
       "889         1  26.000000      0      0     1  0  0       0      0\n",
       "890         0  32.000000      0      0     1  1  0       0      1\n",
       "\n",
       "[889 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now we have 3 useless columns\n",
    "titanic_df=titanic_df.drop(['sex','class','embarked','pclass','fare','who','embark_town','alone','alive','adult_male'],axis=1)\n",
    "titanic_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 : Train & Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=titanic_df.drop(\"survived\",axis=1)\n",
    "y=titanic_df[\"survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.790436005625879"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=model.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.87      0.89      0.88       123\\n           1       0.73      0.69      0.71        55\\n\\n    accuracy                           0.83       178\\n   macro avg       0.80      0.79      0.79       178\\nweighted avg       0.82      0.83      0.82       178\\n'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "val=classification_report(y_test,pred)\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    123\n",
       "1     55\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[109  14]\n",
      " [ 17  38]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6909090909090909\n"
     ]
    }
   ],
   "source": [
    "print(metrics.recall_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5 : Check the Accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8258426966292135"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "val2=accuracy_score(y_test,pred)\n",
    "val2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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