Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "\n",
    "titanic_data=pd.read_csv('Titanic.csv')\n",
    "titanic_data.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#total passengers in original data=891\n"
     ]
    }
   ],
   "source": [
    "print(\"#total passengers in original data=\"+str(len(titanic_data.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a29fbe88>"
      ]
     },
     "execution_count": 3,
     "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": [
    "sns.countplot(x=\"Survived\",data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a2d27b88>"
      ]
     },
     "execution_count": 4,
     "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": [
    "sns.countplot(x=\"Survived\",hue=\"Sex\",data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a2d8c488>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAW90lEQVR4nO3de5CddZ3n8feHkCHMgCIkaEzAoMuOXNNAQ3ACyMKwAuUIw21A5KIp41YhYjnDjo6WAi4M1ojXHWFBFIIsGHVUFhRFkHXMjmCQiImMS8RoWqKEIBqWi0n87h/95KElDekkffp00+9X1anznN/ze37nezTVH37PNVWFJEkAW3W7AEnS6GEoSJJahoIkqWUoSJJahoIkqbV1twvYEpMnT64ZM2Z0uwxJGlPuueeeR6pqymDrxnQozJgxg4ULF3a7DEkaU5L8/LnWuftIktQyFCRJLUNBktQa08cUJKlb1qxZQ19fH0899VS3S3lOkyZNYvr06UycOHHI2xgKkrQZ+vr62H777ZkxYwZJul3OBqqKVatW0dfXx2677Tbk7dx9JEmb4amnnmKnnXYalYEAkISddtppk2cyhoIkbabRGgjrbU59hoIkqWUoSNIwmjBhAj09Pey9996cfPLJPPHEE8/Z94ILLuDDH/7wCFa3cR5oVsfN/uTsbpewyRacu6DbJWiM2nbbbVm0aBEAp59+OldccQXvete7ulzV0DlTkKQOOfTQQ1m6dCkA8+bNY99992XmzJmcccYZG/S96qqrOPDAA5k5cyYnnnhiO8P4whe+wN57783MmTM57LDDAFiyZAkHHXQQPT097LvvvjzwwAPDVrMzBUnqgLVr1/L1r3+do48+miVLlnDxxRezYMECJk+ezKOPPrpB/xNOOIG3vvWtALzvfe/j6quv5txzz+Wiiy7iG9/4BtOmTeOxxx4D4IorruC8887j9NNP5/e//z3r1q0btrqdKUjSMHryySfp6emht7eXXXfdlTlz5nDHHXdw0kknMXnyZAB23HHHDbZbvHgxhx56KPvssw/XX389S5YsAWD27NmcffbZXHXVVe0f/9e85jVccsklfOhDH+LnP/8522677bDV70xBkobRwGMK61XVRk8PPfvss/nKV77CzJkzueaaa7jzzjuB/lnBXXfdxS233EJPTw+LFi3ijW98I7NmzeKWW27hda97HZ/+9Kc54ogjhqV+ZwqS1GFHHnkk8+fPZ9WqVQCD7j5avXo1U6dOZc2aNVx//fVt+09/+lNmzZrFRRddxOTJk1m+fDkPPvggr3zlK3nHO97BG97wBu67775hq9WZgiR12F577cV73/teXvva1zJhwgT2228/rrnmmj/q88EPfpBZs2bxile8gn322YfVq1cDcP755/PAAw9QVRx55JHMnDmTSy+9lM997nNMnDiRl73sZbz//e8ftlpTVcM22Ejr7e0tH7Iz+nlKql6I7r//fvbYY49ul7FRg9WZ5J6q6h2sv7uPJEmtjoVCkklJ7k7ywyRLklzYtF+T5GdJFjWvnqY9ST6RZGmS+5Ls36naJEmD6+QxhaeBI6rq8SQTge8m+Xqz7vyq+uKz+h8D7N68ZgGXN++SpBHSsZlC9Xu8+TixeT3fAYzjgHnNdt8DdkgytVP1SZI21NFjCkkmJFkEPAzcVlV3NasubnYRfTTJNk3bNGD5gM37mrZnjzk3ycIkC1euXNnJ8iVp3OloKFTVuqrqAaYDByXZG3gP8GrgQGBH4O+b7oNd2bHBzKKqrqyq3qrqnTJlSocql6TxaUSuU6iqx5LcCRxdVevvE/t0ks8Cf9d87gN2GbDZdOChkahPkrbUAefPG9bx7vmnMzfa5y1veQs333wzO++8M4sXLx6W7+3k2UdTkuzQLG8L/CXw7+uPE6T/mu/jgfW/5CbgzOYspIOB31bVik7VJ0lj3dlnn82tt946rGN2cqYwFbg2yQT6w2d+Vd2c5I4kU+jfXbQI+C9N/68BxwJLgSeAN3ewNkka8w477DCWLVs2rGN2LBSq6j5gv0HaB71rU/VfWn1Op+qRJG2cVzRLklqGgiSpZShIklreOluShsFQTiEdbqeddhp33nknjzzyCNOnT+fCCy9kzpw5WzSmoSBJY9QNN9ww7GO6+0iS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktT0mVpGHwi4v2Gdbxdn3/j553/fLlyznzzDP51a9+xVZbbcXcuXM577zztvh7DQVJGoO23nprLrvsMvbff39Wr17NAQccwFFHHcWee+65ReO6+0iSxqCpU6ey//77A7D99tuzxx578Mtf/nKLxzUUJGmMW7ZsGffeey+zZs3a4rEMBUkawx5//HFOPPFEPvaxj/GiF71oi8czFCRpjFqzZg0nnngip59+OieccMKwjGkoSNIYVFXMmTOHPfbYg3e9613DNq5nH0nSMNjYKaTDbcGCBVx33XXss88+9PT0AHDJJZdw7LHHbtG4HQuFJJOA7wDbNN/zxar6QJLdgBuBHYEfAGdU1e+TbAPMAw4AVgF/U1XLOlWfJI1lhxxyCP2Pth9endx99DRwRFXNBHqAo5McDHwI+GhV7Q78Blj/RIg5wG+q6j8AH236SZJGUMdCofo93nyc2LwKOAL4YtN+LXB8s3xc85lm/ZFJ0qn6JEkb6uiB5iQTkiwCHgZuA34KPFZVa5sufcC0ZnkasBygWf9bYKdBxpybZGGShStXruxk+ZI07nQ0FKpqXVX1ANOBg4A9BuvWvA82K9hgh1lVXVlVvVXVO2XKlOErVpI0MqekVtVjwJ3AwcAOSdYf4J4OPNQs9wG7ADTrXww8OhL1SZL6dSwUkkxJskOzvC3wl8D9wLeBk5puZwFfbZZvaj7TrL+jOnFoXZL0nDp5ncJU4NokE+gPn/lVdXOSHwM3JvlvwL3A1U3/q4Hrkiylf4Zwagdrk6RhNfuTs4d1vAXnLnje9U899RSHHXYYTz/9NGvXruWkk07iwgsv3OLv7VgoVNV9wH6DtD9I//GFZ7c/BZzcqXok6YVkm2224Y477mC77bZjzZo1HHLIIRxzzDEcfPDBWzSut7mQpDEoCdtttx3Qfw+kNWvWMBxn8RsKkjRGrVu3jp6eHnbeeWeOOuoob50tSePZhAkTWLRoEX19fdx9990sXrx4i8c0FCRpjNthhx04/PDDufXWW7d4LENBksaglStX8thjjwHw5JNP8q1vfYtXv/rVWzyut86WpGGwsVNIh9uKFSs466yzWLduHX/4wx845ZRTeP3rX7/F4xoKkjQG7bvvvtx7773DPq67jyRJLUNBktQyFCRpM43227NtTn2GgiRthkmTJrFq1apRGwxVxapVq5g0adImbeeBZknaDNOnT6evr4/R/LCvSZMmMX369E3axlCQpM0wceJEdtttt26XMezcfSRJahkKkqSWoSBJahkKkqSWoSBJanUsFJLskuTbSe5PsiTJeU37BUl+mWRR8zp2wDbvSbI0yU+SvK5TtUmSBtfJU1LXAn9bVT9Isj1wT5LbmnUfraoPD+ycZE/gVGAv4OXAt5L8x6pa18EaJUkDdGymUFUrquoHzfJq4H5g2vNschxwY1U9XVU/A5YCB3WqPknShkbkmEKSGcB+wF1N09uT3JfkM0le0rRNA5YP2KyP5w8RSdIw63goJNkO+BLwzqr6HXA58CqgB1gBXLa+6yCbb3BTkSRzkyxMsnA0X14uSWNRR0MhyUT6A+H6qvoXgKr6dVWtq6o/AFfxzC6iPmCXAZtPBx569phVdWVV9VZV75QpUzpZviSNO508+yjA1cD9VfWRAe1TB3T7a2Bxs3wTcGqSbZLsBuwO3N2p+iRJG+rk2UezgTOAHyVZ1LT9A3Bakh76dw0tA94GUFVLkswHfkz/mUvneOaRJI2sjoVCVX2XwY8TfO15trkYuLhTNUmSnp9XNEuSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKk1pFBIcvtQ2iRJY9vzPk8hySTgT4HJSV7CM89HeBHw8g7XJkkaYRt7yM7bgHfSHwD38Ewo/A745w7WJUnqgucNhar6OPDxJOdW1SdHqCZJUpcM6XGcVfXJJH8BzBi4TVXN61BdkqQuGOqB5uuADwOHAAc2r96NbLNLkm8nuT/JkiTnNe07JrktyQPN+0ua9iT5RJKlSe5Lsv8W/TJJ0iYb0kyB/gDYs6pqE8ZeC/xtVf0gyfbAPUluA84Gbq+qS5O8G3g38PfAMcDuzWsWcHnzLkkaIUO9TmEx8LJNGbiqVlTVD5rl1cD9wDTgOODaptu1wPHN8nHAvOr3PWCHJFM35TslSVtmqDOFycCPk9wNPL2+sareMJSNk8wA9gPuAl5aVSua7Vck2bnpNg1YPmCzvqZtxbPGmgvMBdh1112HWL4kaSiGGgoXbO4XJNkO+BLwzqr6XZLn7DpI2wa7q6rqSuBKgN7e3k3ZnSVJ2oihnn30vzdn8CQT6Q+E66vqX5rmXyeZ2swSpgIPN+19wC4DNp8OPLQ53ytJ2jxDPftodZLfNa+nkqxL8ruNbBPgauD+qvrIgFU3AWc1y2cBXx3QfmZzFtLBwG/X72aSJI2Moc4Uth/4OcnxwEEb2Ww2cAbwoySLmrZ/AC4F5ieZA/wCOLlZ9zXgWGAp8ATw5qHUJkkaPkM9pvBHquorzemkz9fnuwx+nADgyEH6F3DO5tQjSRoeQwqFJCcM+LgV/dcteJBXkl5ghjpT+KsBy2uBZfRfVyBJegEZ6jEF9+9L0jgw1LOPpif5cpKHk/w6yZeSTO90cZKkkTXU21x8lv5TRl9O/1XG/6tpkyS9gAw1FKZU1Weram3zugaY0sG6JEldMNRQeCTJm5JMaF5vAlZ1sjBJ0sgbaii8BTgF+BX9N6g7CS8uk6QXnKGekvpB4Kyq+g30PyiH/ofuvKVThUmSRt5QZwr7rg8EgKp6lP5bYUuSXkCGGgpbrX9sJrQzhc26RYYkafQa6h/2y4D/k+SL9N/e4hTg4o5VJUnqiqFe0TwvyULgCPpvcndCVf24o5VJkkbckHcBNSFgEEjSC9hQjylIksYBQ0GS1DIUJEktQ0GS1DIUJEmtjoVCks80z19YPKDtgiS/TLKoeR07YN17kixN8pMkr+tUXZKk59bJmcI1wNGDtH+0qnqa19cAkuwJnArs1WzzqSQTOlibJGkQHQuFqvoO8OgQux8H3FhVT1fVz4ClwEGdqk2SNLhuHFN4e5L7mt1L6++nNA1YPqBPX9O2gSRzkyxMsnDlypWdrlWSxpWRDoXLgVcBPfQ/l+Gypj2D9K3BBqiqK6uqt6p6p0zx4W+SNJxGNBSq6tdVta6q/gBcxTO7iPqAXQZ0nQ48NJK1SZJGOBSSTB3w8a+B9Wcm3QScmmSbJLsBuwN3j2RtkqQOPhMhyQ3A4cDkJH3AB4DDk/TQv2toGfA2gKpakmQ+/TfcWwucU1XrOlWbJGlwHQuFqjptkOarn6f/xfiMBknqKq9oliS1DAVJUsvnLEuj1AHnz+t2CZvsnn86s9slaAs5U5AktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktbyiWdK4NvuTs7tdwiZZcO6Cjo7vTEGS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEmtjoVCks8keTjJ4gFtOya5LckDzftLmvYk+USSpUnuS7J/p+qSJD23Ts4UrgGOflbbu4Hbq2p34PbmM8AxwO7Nay5weQfrkiQ9h46FQlV9B3j0Wc3HAdc2y9cCxw9on1f9vgfskGRqp2qTJA1upI8pvLSqVgA07zs37dOA5QP69TVtG0gyN8nCJAtXrlzZ0WIlabwZLQeaM0hbDdaxqq6sqt6q6p0yZUqHy5Kk8WWk73306yRTq2pFs3vo4aa9D9hlQL/pwEMjXNuY8YuL9ul2CZvmJS/qdgWShmikZwo3AWc1y2cBXx3QfmZzFtLBwG/X72aSJI2cjs0UktwAHA5MTtIHfAC4FJifZA7wC+DkpvvXgGOBpcATwJs7VZck6bl1LBSq6rTnWHXkIH0LOKdTtUiShma0HGiWJI0CPmRH0rAZcydBgCdCPIszBUlSy1CQJLUMBUlSa9wfUzjg/HndLmGTfXn7blcg6YXKmYIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqdWVu6QmWQasBtYBa6uqN8mOwOeBGcAy4JSq+k036pOk8aqbM4X/VFU9VdXbfH43cHtV7Q7c3nyWJI2g0bT76Djg2mb5WuD4LtYiSeNSt0KhgG8muSfJ3KbtpVW1AqB533mwDZPMTbIwycKVK1eOULmSND5068lrs6vqoSQ7A7cl+fehblhVVwJXAvT29lanCpSk8agrM4Wqeqh5fxj4MnAQ8OskUwGa94e7UZskjWcjHgpJ/izJ9uuXgf8MLAZuAs5qup0FfHWka5Ok8a4bu49eCnw5yfrv/59VdWuS7wPzk8wBfgGc3IXaJGlcG/FQqKoHgZmDtK8CjhzpeiRJzxhNp6RKkrrMUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVLLUJAktQwFSVJr1IVCkqOT/CTJ0iTv7nY9kjSejKpQSDIB+GfgGGBP4LQke3a3KkkaP0ZVKAAHAUur6sGq+j1wI3Bcl2uSpHFj624X8CzTgOUDPvcBswZ2SDIXmNt8fDzJT0aotlHjFZ0bejLwSOeGHzvyjnS7hDHJf5udN0z/Np/z/6rRFgqD/dr6ow9VVwJXjkw540uShVXV2+06pGfz3+bIGW27j/qAXQZ8ng481KVaJGncGW2h8H1g9yS7JfkT4FTgpi7XJEnjxqjafVRVa5O8HfgGMAH4TFUt6XJZ44m75TRa+W9zhKSqNt5LkjQujLbdR5KkLjIUJEktQ0HeWkSjVpLPJHk4yeJu1zJeGArjnLcW0Sh3DXB0t4sYTwwFeWsRjVpV9R3g0W7XMZ4YChrs1iLTulSLpC4zFLTRW4tIGj8MBXlrEUktQ0HeWkRSy1AY56pqLbD+1iL3A/O9tYhGiyQ3AP8G/HmSviRzul3TC523uZAktZwpSJJahoIkqWUoSJJahoIkqWUoSJJahoIEJHlvkiVJ7kuyKMmsYRjzDcN119kkjw/HONLGeEqqxr0krwE+AhxeVU8nmQz8SVVt9MruJFs313p0usbHq2q7Tn+P5ExBgqnAI1X1NEBVPVJVDyVZ1gQESXqT3NksX5DkyiTfBOYluSvJXusHS3JnkgOSnJ3kvyd5cTPWVs36P02yPMnEJK9KcmuSe5L8a5JXN312S/JvSb6f5IMj/L+HxjFDQYJvArsk+b9JPpXktUPY5gDguKp6I/23Gz8FIMlU4OVVdc/6jlX1W+CHwPpx/wr4RlWtof+B9OdW1QHA3wGfavp8HLi8qg4EfrXFv1AaIkNB415VPU7/H/m5wErg80nO3shmN1XVk83yfODkZvkU4AuD9P888DfN8qnNd2wH/AXwhSSLgP9B/6wFYDZwQ7N83Sb9IGkLbN3tAqTRoKrWAXcCdyb5EXAWsJZn/sNp0rM2+X8Dtv1lklVJ9qX/D//bBvmKm4B/TLIj/QF0B/BnwGNV1fNcZW3mz5E2mzMFjXtJ/jzJ7gOaeoCfA8vo/wMOcOJGhrkR+K/Ai6vqR89e2cxG7qZ/t9DNVbWuqn4H/CzJyU0dSTKz2WQB/TMKgNM3/VdJm8dQkGA74NokP05yH/3Pqr4AuBD4eJJ/BdZtZIwv0v9HfP7z9Pk88Kbmfb3TgTlJfggs4ZlHoZ4HnJPk+8CLN+3nSJvPU1IlSS1nCpKklqEgSWoZCpKklqEgSWoZCpKklqEgSWoZCpKk1v8H6eFHp5MROLoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\",hue=\"Pclass\",data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a2e2ac88>"
      ]
     },
     "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": [
    "titanic_data[\"Age\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a2eb2548>"
      ]
     },
     "execution_count": 7,
     "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": [
    "titanic_data[\"Fare\"].plot.hist(bins=20,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Wrapping data ,Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a2fb8348>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull(),yticklabels=False,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a3089c08>"
      ]
     },
     "execution_count": 11,
     "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": [
    "sns.boxplot(x=\"Pclass\",y=\"Age\",data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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",
       "5            6         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",
       "5                                   Moran, Mr. James    male   NaN      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  \n",
       "5      0            330877   8.4583   NaN        Q  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.dropna(inplace=True)"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</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",
       "6            7         0       1   \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",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  \n",
       "6      0             17463  51.8625        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217a3145108>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull(),yticklabels=False,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(titanic_data[\"Sex\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    male\n",
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1\n",
       "6      1\n",
       "7      1\n",
       "8      0\n",
       "9      0\n",
       "10     0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked=pd.get_dummies(titanic_data[\"Embarked\"],drop_first=True)\n"
   ]
  },
  {
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "6   0  1\n",
       "7   0  1\n",
       "8   0  1\n",
       "9   0  0\n",
       "10  0  1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</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",
       "6             7         0       1   \n",
       "7             8         0       3   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "10           11         1       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",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "\n",
       "    Parch            Ticket     Fare Embarked  \n",
       "0       0         A/5 21171   7.2500        S  \n",
       "1       0          PC 17599  71.2833        C  \n",
       "2       0  STON/O2. 3101282   7.9250        S  \n",
       "3       0            113803  53.1000        S  \n",
       "4       0            373450   8.0500        S  \n",
       "6       0             17463  51.8625        S  \n",
       "7       1            349909  21.0750        S  \n",
       "8       2            347742  11.1333        S  \n",
       "9       0            237736  30.0708        C  \n",
       "10      1           PP 9549  16.7000        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2  3\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  0\n",
       "4  0  1\n",
       "6  0  0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pcl=pd.get_dummies(titanic_data[\"Pclass\"],drop_first=True)\n",
    "Pcl.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>S</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>1</td>\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>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>S</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>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>S</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>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>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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 Embarked  male  Q  S  2  3  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  0  0  \n",
       "4      0            373450   8.0500        S     1  0  1  0  1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data=pd.concat([titanic_data,sex,embarked,Pcl],axis=1)\n",
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop(['Sex','Embarked','PassengerId','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</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",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</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>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</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>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</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>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</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>12</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0          0       3  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1          1       1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2          1       3  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3          1       1  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4          0       3  35.0      0      0   8.0500     1  0  1  0  1\n",
       "6          0       1  54.0      0      0  51.8625     1  0  1  0  0\n",
       "7          0       3   2.0      3      1  21.0750     1  0  1  0  1\n",
       "8          1       3  27.0      0      2  11.1333     0  0  1  0  1\n",
       "9          1       2  14.0      1      0  30.0708     0  0  0  1  0\n",
       "10         1       3   4.0      1      1  16.7000     0  0  1  0  1\n",
       "11         1       1  58.0      0      0  26.5500     0  0  1  0  0\n",
       "12         0       3  20.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</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",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</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>8</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</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>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</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>11</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived   Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0          0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1          1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2          1  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3          1  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4          0  35.0      0      0   8.0500     1  0  1  0  1\n",
       "6          0  54.0      0      0  51.8625     1  0  1  0  0\n",
       "7          0   2.0      3      1  21.0750     1  0  1  0  1\n",
       "8          1  27.0      0      2  11.1333     0  0  1  0  1\n",
       "9          1  14.0      1      0  30.0708     0  0  0  1  0\n",
       "10         1   4.0      1      1  16.7000     0  0  1  0  1\n",
       "11         1  58.0      0      0  26.5500     0  0  1  0  0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.drop('Pclass',axis=1,inplace=True)\n",
    "titanic_data.head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=titanic_data.drop('Survived',axis=1)\n",
    "y=titanic_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</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>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.30,random_state=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>699</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.650</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>642</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.900</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>434</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55.900</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",
       "      <td>203</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.225</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",
       "      <td>586</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch    Fare  male  Q  S  2  3\n",
       "699  42.0      0      0   7.650     1  0  1  0  1\n",
       "642   2.0      3      2  27.900     0  0  1  0  1\n",
       "434  50.0      1      0  55.900     1  0  1  0  0\n",
       "203  45.5      0      0   7.225     1  0  0  0  1\n",
       "586  47.0      0      0  15.000     1  0  1  1  0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</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>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "699    0\n",
       "642    0\n",
       "434    0\n",
       "203    0\n",
       "586    0\n",
       "      ..\n",
       "182    0\n",
       "809    1\n",
       "94     0\n",
       "292    0\n",
       "51     0\n",
       "Name: Survived, Length: 498, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "6    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>689</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>211.3375</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>279</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.2500</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>508</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5250</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>9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78.2667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch      Fare  male  Q  S  2  3\n",
       "689  15.0      0      1  211.3375     0  0  1  0  0\n",
       "279  35.0      1      1   20.2500     0  0  1  0  1\n",
       "508  28.0      0      0   22.5250     1  0  1  0  1\n",
       "9    14.0      1      0   30.0708     0  0  0  1  0\n",
       "496  54.0      1      0   78.2667     0  0  0  0  0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "689    1\n",
       "279    1\n",
       "508    0\n",
       "9      1\n",
       "496    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "logmodel.fit(X_train,y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.80      0.81      0.81       126\\n           1       0.72      0.72      0.72        88\\n\\n    accuracy                           0.77       214\\n   macro avg       0.76      0.76      0.76       214\\nweighted avg       0.77      0.77      0.77       214\\n'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102,  24],\n",
       "       [ 25,  63]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.10280373831776"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}