napsterv/LogR.ipynb

## LogR.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087dd07b00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEnCAYAAAAKMZAQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087dd07f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087dfcd4a8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEFCAYAAADuT+DpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087dfdaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', hue='Sex',data=train, palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e148a90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEFCAYAAADuT+DpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087a74e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', hue='Pclass',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e1ee710>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEGCAYAAACJnEVTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQsYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087e26bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(),bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e2f6e80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD3CAYAAADxJYRbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087e3900f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Age'].plot.hist(bins=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e4d84a8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEFCAYAAADuT+DpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087e445668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e5a04a8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAAD5CAYAAADhhaswAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAADBGoAAAADBGoAAAADP0PXxsXukAEeGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087e5a9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(bins=40,figsize=(10,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e5a0c88>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEFCAYAAAAPCDf9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABiE0AcAgxD6AGAQQh8ADELoA4BB/j+REkTkN7nk4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087e647978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Pclass',y='Age',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        if Pclass==1:\n",
    "            return 37\n",
    "        elif Pclass==2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "        \n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2087e6f2da0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAEnCAYAAAAKMZAQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHSNJREFUeJzt3X98z/X+//H7G3ub/NyapbIooTh8DsKZDmlOdY5KiEwi\nZeis1AxpoxmVpZEfXXKKQsuPHTsX+mE1HbpkOSVKJ1IrGxlhG6L9YNP2+vyxy/v9mV99vt+P1/PZ\nztyul4vLhe1yeT3mx+6er+ePx9PjOI4jAIAVtX7rLwAALiWELgBYROgCgEWELgBYROgCgEWELgBY\nVOfXPnlbrcG2vg4Al5D1B7+yWu+Oq/7Lar1/VqRd8HOMdAHAIkIXACwidAHAIkIXACwidAHAIkIX\nACwidAHAIkIXACwidAHAIkIXACwidAHAIkIXACwidAHAIkIXACz61daOAGCC7VaLtltJ/hpCF4B1\nNb+f7oU/x/QCAFhE6AKARYQuAFhE6AKARYQuAFhE6AKARYQuAFhE6AKARRyOAGCd7cMK1QmhC8C6\nmn4i7dcQugCsq04haBuhC8A6RroAYFF1CkHbCF0A1l3KI122jAGARYQuAFhE6AKARczpArCuOs2x\n2kboArCOhTQAgBWMdAFYV51GnrYRugCsY3oBAGAFoQsAFhG6AGARoQsAFhG6AGARoQsAFhG6AGAR\noQsAFhG6AGARoQsAFhG6AGARoQsAFtHwBoB11akBjW2ELgDr6DIGALCC0AUAiwhdALCI0AUAiwhd\nALCI0AUAiwhdALCIfboArKtO+2ZtI3QBWMfhCACAFYQuAFjE9AIA66rT675thC4A65jTBQBYQegC\ngEWELgBYROgCgEWELgBYxO4FANZVp90EthG6AKy7lLeMEboArKtOIWgbc7oAYBGhCwAWEboAYBFz\nugCsYyENACyqTiFoG6ELwDpGugBgUXUKQdtYSAMAiwhdALCI0AUAi5jTBWDdpbyQxkgXACwidAHA\nIqYXAFhXnV73bSN0AVjHnC4AwApCFwAsInQBwCLmdAFYV53mWG0jdAFYx0IaAMAKQhcALGJ6AYB1\n1el13zZCF4B1zOkCAKwgdAHAIkIXACwidAHAIkIXACwidAHAIkIXACxiny4A66rTvlnbGOkCgEWE\nLgBYxPQCAOs4BgwAsILQBQCLCF0AsIjQBQCLCF0AsIjQBQCLCF0AsIh9ugCsq077Zm0jdAFYx+EI\nAIAVjHQBWFedRp62EboArGN6AQBgBaELABYxvQDAuur0um8boQvAukt5TpfQBWBddQpB25jTBQCL\nCF0AsIjpBQDWXcpzuox0AcAiQhcALGJ6AYB11el13zZGugBgESNdANaxkAYAsILQBQCLCF0AsIjQ\nBQCLCF0AsIjQBQCLCF0AsIh9ugCsq077Zm1jpAsAFjHSBWAdJ9IAAFYQugBgEaELABYRugBgEaEL\nABYRugBgEaELABaxTxeAddVp36xthC4A6y7lwxGELgDrqlMI2kboArDuUh7pspAGABYRugBgEaEL\nABYxpwvAuuo0x2oboQvAOhbSAABWMNIFYF11GnnaRugCsI7pBQCAFYQuAFhE6AKARYQuAFhE6AKA\nRYQuAFhE6AKARYQuAFjE4QgA1lWnwwq2EboArONEGgDACkIXACxiegGAddXpdd82QheAdczpAgCs\nIHQBwCJCFwAsYk4XgHXVaY7VNka6AGARoQsAFjG9AMA6towBAKwgdAHAIkIXACwidAHAIkIXACwi\ndAHAIkIXACxiny4A66rTvlnbCF0A1nE4AgBgBaELABYRugBgEaELABYRugBgEaELABaxZQyAddVp\nC5dthC4A6y7lfbqELgDrqlMI2kboArDuUh7pspAGABYx0gVgXXUaedpG6AKwjukFAIAVhC4AWETo\nAoBFhC4AWEToAoBFhC4AWEToAoBF7NMFYF112jdrG6ELwDoORwAArCB0AcAij+M4zm/9RQDApYKR\nLgBYROgCgEWELgBYROgCgEWELgBYROgCgEWELgBYROjiN/HDDz9o06ZNOnz4sNgqjksJofv/oaKi\nQuXl5fr8889VVlZmvF5NDably5dr2rRpmjt3rjIyMvTMM89YqVtRUaEjR47UqD9LSUpLSzvj1ykp\nKVbqFhUV6bvvvlNJSYmVejXFRTe8iYuLu+DnkpKSLvbx59i2bdsFP9e1a1fX6/kkJycrLCxMBw8e\n1K5duxQSEqJZs2YZq7d8+XL985//1IkTJ9S/f3/l5uYqISHBWL3Zs2crNjZWtWrVUmFhoaZMmaIF\nCxYYqZWenq6VK1dqxIgRGjlypO69914jdar64IMP9Pzzz6tRo0YqLi5WYmKibr75ZmP1Dhw4oPXr\n1+vkyZP+jz322GOu1li3bp0+/PBDffbZZ9qyZYskqby8XLt379aIESNcrXW2jIwMvfLKKyovL9ef\n//xneTweRUdHG6lVVFSkzMzMMwY6/fv3d71ORESEPB6P/9d16tTRL7/8Iq/Xq/fff9+1Ohcdun37\n9pUkrVq1Sp06dVLnzp21c+dO7dy586K/uPNZtWqVJCk3N1enT59Whw4d9M0336h+/fp68803jdSU\npC+++EKTJk3S8OHD9eabb+rBBx80VkuyH0xer1cjR47UiBEjtGDBAj300EPGavlGmr5/4F6v11gt\nn4ULFyotLU2XX365jhw5okceecRo6E6YMEE9e/ZUSEiIsRo9e/ZU06ZNdfz4cQ0ZMkSSVKtWLYWF\nhRmr6bNs2TKtXr1ao0aNUnR0tO69915joRsdHa3Q0FBdeeWVknRGMLopIyNDjuNo+vTpioyMVMeO\nHfXNN99o5cqVrta56NDt2bOnJGnp0qUaPXq0JKlLly7GvmlffPFFSdKYMWO0cOFC1alTR+Xl5Roz\nZoyRej4VFRXasWOHmjdvrrKyMh07dsxoPdvBNG7cOE2ePFkxMTGKj4/XgAEDjNW68847NWzYMB08\neFCjR4/Wn/70J2O1fJo0aaLLL79ckhQSEqIGDRoYrRcYGOj6yPZsjRs3Vvfu3dW9e3d9+umn2r9/\nvzp27KgmTZoYrStVhrvX65XH45HH41G9evWM1XIcR7Nnzzb2fB/f95jvz1GS2rVrp71797pax7V+\nuiUlJfr000/VoUMHffnllzp9+rRbjz6vgoIC/8/Ly8uNh+A999yjZ555RjNnzlRycrLx17e77rrL\najA98MADat++vTZu3KjExER9++23xuZahw8frh49euj777/Xddddp7Zt2xqpU1WDBg00atQode3a\nVV9//bVOnTrl/w88NjbWtTq+b9CQkBC9++67at++vf8/zmuvvda1OlW9+OKLOnz4sHJychQQEKBF\nixb5f2+m3HTTTZowYYLy8vKUkJCgDh06GKvVtm1bffXVV7rxxhv9HzM5CGnYsKHmzZunjh076ssv\nv9TVV1/t6vNd6zKWk5Oj+fPnKzs7W61atVJCQoKaNm3qxqPPa8WKFUpJSVGbNm2UnZ2tcePG+ac6\nTDt06JD/VceknJwca8H00UcfqXfv3v5fp6SkGPuP5ex1gICAADVr1kzDhg1T48aNjdRcu3btBT/n\n5qh++PDh5/24x+MxtsA1bNgwrVixwj/1dd9992n16tVGavkUFhbqyy+/9P/7jIiIMFarX79+Kioq\n8v/a4/Fo48aNxuqVlJRo7dq1ys7O1nXXXaf7779ftWvXdu35ro10W7VqZWzh5XyGDRume+65R3v2\n7FHz5s0VHBxstF5KSooCAwP1888/a82aNerZs+evLiJerB07dig9PV2lpaX67LPPJEmJiYnG6t10\n002aN2+e8vPz1bt3b91yyy3GapWWliosLEw33XSTvvrqK+3cuVPBwcGaPHmyXnnlFdfrZWVlacCA\nASorK1NaWpq8Xq/uvfde1arl/uYd37pCaWmpcnJy1K5dO23YsMHon2d5eblKS0vl8XhUXl5u5Pd1\ntjFjxmjVqlXq1auX8VrvvPOO8RpV1a1bV16vV0FBQWrTpo1OnDjhar5cdOj+8Y9/vODnNm/efLGP\nv6Ddu3dr2rRpKiws1N13363WrVvr1ltvNVYvPT1db775pqKiopSenm58IW3y5MkaPXq0GjVqZLSO\nT3x8vHr16qWtW7cqJCREU6ZM0fLly43UOnbsmP/1t2fPnnr44YcVExOjYcOGuV5r6dKleu+997Rq\n1Sq98MILOnjwoK666irNnDlTU6dOdb2ez6RJkxQeHu6fE3z//fc1Z84cI7UefPBBDRw4UMeOHdPg\nwYM1cuRII3Wqaty4sd544w1de+21/pD/tSz4v5gxY4YSEhI0ZMiQcxbPUlNTXa1VVUJCgkJDQ/XJ\nJ5/od7/7nSZPnqzFixe79vyLDl1fsP7888/WAkKSnn32WSUlJWnq1KkaNGiQoqKijIaux+NRQUGB\nQkJC5PF4dOLECWO1JKlFixYaOHCg0RpVHT9+XIMGDdI777yjzp07G93LWlRUpJycHLVq1Uo5OTkq\nKSnRTz/9ZGS/Z2ZmplJTU+XxeLRu3TqtX79ejRs3VmRkpOu1qsrLy9PQoUMlSaNHj77gtIMb/vKX\nv6hHjx7at2+fmjdv7uqr8IUEBQUpKytLWVlZ/o+5Hbq+3RCm56fPlpubq+eee06ff/65IiIitGjR\nIlef79r0wtixY/3buWxp0aKFPB6PgoODVb9+faO1unfvrgceeEBz5szRzJkzdfvttxutd8cdd2j8\n+PFq1aqV/2OmV8NzcnIkSYcPHzb6ipqQkKBJkyYpPz9fgYGBGjBggN577z098sgjrteqVauWateu\nrV27diksLMw/Z2zjgMTevXt17bXXKjc3VxUVFcbqPPPMM3r66afVsWNHffzxx3r22We1fv16Y/Wk\nc/fg5+fnu17Dt92uoqJCL7zwgn744Qe1bt1akyZNcr1WVb6FeY/Ho6KiIte/F1wLXRuvG2fXS01N\n1cmTJ5Wenm58lD1+/HiNHz9ektShQwcFBAQYrbdy5Urddttt1t4epk6dqvj4eGVnZys6OlrPPvus\nsVodO3ZUYmKili9frn/96186evSoHn30UWP19u7dqzVr1vgXe3bv3m183nPKlCmKiYnR0aNHFRoa\nqhkzZhir1aBBA82ePVslJSXavXu3XnvtNWO1fBYsWKCVK1fq9OnTOnXqlFq2bKn09HQjteLj4xUV\nFaXOnTtr27Ztio+P19KlS43UkqSYmBgNHTpUBQUFGjJkiKZMmeLq810LXRuvG1XNnDlTr7zyioKC\ngvT111/rueeeM1ZLkjZu3Oj/R+Y4jo4fP653333XWL3GjRsb33ssSbt27dKUKVOUlpamUaNGKTEx\nUcXFxTp06JDatWvnaq2ysjKlp6drxYoV8nq9Kioq0saNGxUYGOhqnaqeeOIJPfnkk7r66qsVGxur\nrVu3atKkSZo/f76xmlLlycm3337baA2f8ePHa9asWdq3b5/RA0JVZWZmKjMzUzNnztRDDz2k6dOn\nG6tVu3Zt/0JkRESE3njjDWO1JKlTp05av369jh07pqCgIO3fv9/V57sWuklJSdq7d69yc3PVtm1b\nhYaGuvXo81qwYIHuu+8+XX/99Ubr+Lz88st6+umnlZqaqu7du+uTTz4xWi8oKEgJCQlq166dfxHB\nd+rITXPnztXzzz+vgIAAzZs3T4sXL1aLFi0UFRWlPn36uForIiJCd911l2bPnq2WLVsqKirKaOBK\nlaPqqr0Jfv/732vDhg3G31Q2bdqkkSNHGp1fPXtQc+TIEf/HTC5iS5WHTbxer4qLi9WiRYszjju7\nxfd7qFevnhYvXqyuXbtqx44dRk/5SZWnCRcsWKDg4GClpqZq6dKlrk7XuBa6VXsFDBgwQPv27TPa\nK6Bz585KTk5WcXGxBg4cqL59+xr9Bg4KClKnTp2UmpqqgQMHas2aNcZqSZXz1VLlN5JJjuPohhtu\nUF5enk6ePKn27dtLkpHX7xEjRmjdunX68ccfNWjQIKuNZ3bu3Klp06bpyJEjuuqqqzR9+nSje59/\n+ukn9ezZU82bN/ef2nJ7xb1qsJaUlOiyyy5TXl6errjiClfrnE+zZs30j3/8Q/Xq1dOcOXPO2Efr\nFt90RZMmTbRnzx7t2bNHkvnTmeHh4Zo0aZIKCwvVsGFD9/c8Oy6JjIx0KioqnAceeMBxHMcZOHCg\nW4/+VXl5eU5MTIzTpUsXo3XGjh3rbN261YmNjXUyMzOd22+/3Wg9x6n8vf3444/OgQMHnO3btxup\nMXLkSMdxHCctLc2Ji4tzHMdxSktLnX79+hmp5ziO89lnnzkTJ050unXr5rzwwgvOd999Z6yWz5Ah\nQ5zdu3c7juM4WVlZztChQ43WO3DgwDk/THnppZecpKQkx3EcZ9y4cc6rr75qrNbLL7/sOI7jlJeX\nO9u3b3cKCwudlJQU/5+tDXl5eUaeW1pa6v/x2muvOaNGjfL/2k2ujXQdy70CDh48qLVr1+qDDz5Q\nu3btXN1Hdz7Tp0/Xnj179Ne//lXz58/X448/brRefHy8/v3vf+vkyZM6deqUwsLCjJwyCg8PV2Rk\npA4fPqy//e1vys3NVWJiotHTfd26dVO3bt30888/6+2339aTTz6pt956y1g9qXLDu28qqm3btsan\nF3755RdlZGT4j8Pn5+cbW0z78MMP/W9eCxYsUGRkpLH1gC1btig6Olq1atXS3LlzlZKSYnQ7nGRv\n0c7XLU36nzzzfczNE3Cuha7tJibjxo3T4MGDtWLFCqPNS6o2u2jWrJmkyoULU52OfPbs2aP09HQl\nJCRo/PjxeuKJJ4zUGTNmjPr06aPg4GAFBQUpNzdXQ4cO1W233WakXlWNGjXS8OHDjX7T/v3vf5dU\n2aYvMTHRPy9ouuHN5MmTdeutt2r79u0KDQ012nPW4/GorKxMXq/Xv9BrStVnm6xTla1Fuw8//FCS\n9Pbbb+uee+4xUkNyMXRtNTE5fPiwmjVrpuTkZP+BBV/zGxMNRarOS3s8HjmO4w9ck82i69evL4/H\no5KSEgUHBxttIFR1L/A111yja665xlgt23z/Njp16iSp8j/Rhg0bntE8xYTAwECNHTtWP/zwg5KS\nknT//fcbqxUZGam7775bbdq00Z49exQVFWWsVtXBhumBh4+NRbuq0tLS/jNCt2ofgszMTGNNTJYu\nXaq4uDhNmzbtjI+baijyW5yll6T27dvr9ddfV2hoqMaPH6/y8nKj9WqqQYMGqVmzZq635/vfOI6j\ngoIClZSUqKSkxOgJxsGDB6tPnz7av3+/wsLCjPYh2bVrlyIjI+U4jrKzs/0/N7FQ6FN10W727NlG\nFu2qKisrU//+/c84c+DmEW7XuozFxsae08TkxhtvVFZWlpEmJhs2bFBERISV5h6S9Pjjjys8PFxD\nhw7V4sWLlZWVZeQsfdW5zaKiItWtW1enTp3SqVOn/P2K8f8uKSlJcXFxGj58uP/4du3atdWgQQNj\nbypFRUX69ttvlZ2drdDQUE2dOlX9+/fX5MmTXa2zcOFCRUdHKzY29pxRp6k+Dz/++OMFP+d2C0Sp\ncnpowIABKigo0KFDh5SVlaXw8PAz3s7ctnXr1nM+1q1bN9ee79pI12YTE0n65JNPNH/+fEVERGjQ\noEHGu+XbOkvvO4rr4ziO1qxZo8DAQEL3/6Bfv37q37+/Vq9erY8++kiJiYlq2LChsRNwy5cv15Il\nS1SnTh1NnTpVvXr1cn2/s88XX3whScb7SFRlIlgv5KWXXtLu3bvVr18/XX311XIcR8uWLdOJEyeM\nnmBs06aNNm/erF9++UWO4yg/P796hu7ZTUyKi4uNNTGRKuday8rKtHHjRs2YMUOnT5/WsmXLjNTy\nsXGWfsKECf6f79u3T0899ZR69+6t+Ph4I/VqOt/hD6/Xa/zwh1R5b1lGRoaKior05JNPGm196Jvn\ndzMQqpPMzEytXr3aP4pv3ry55s6dq8jISKOh+/jjj6tly5b6/vvvVbduXddvxXAtdKs2MbnyyiuV\nkJBgrImJz44dO7R582YdPXpUd9xxh7E6RUVFmjhxorWz9FJlk/Y33nhDcXFxRrun1XTOBQ5/mFoE\n8nq98nq9xhc/pcprZS7UgcvN2zB+K5dddtk5f08BAQHGm1tJlW0l4+Li9Nxzz7n+tu5a6Hbs2PGc\nU1omr/Do27evbrjhBg0ePNho34XzvS6alJeXp7i4ODVu3FhpaWnGblK4VPjeSD7++GOFh4dLqlwo\nsXFtuOktVYGBgcauAKoOAgMD/YuDPvv377eya6K0tFQnT5707yByk2uh+9Zbb2nRokUqLS31f8zk\nlRoDBw40ujXGx+brolR5N1pAQID+8Ic/nDOaNrU4UpPZPvyRnZ2tCRMm+Ff3q04Xuf33FxISYvQC\n0d/axIkTFR0drfDwcIWFhengwYPavHmzZs2aZbTusGHDtGzZMt1888265ZZb1KVLF1ef79ruhTvv\nvFMLFy484+4wk6fSRowYoaVLlxpv2DxixAj/KveDDz5ovMPR+VZOfWrq3J1pOTk5Zxz++O6774wd\n/rD59zdr1izXd0RUN4WFhdq4caPy8/N11VVXqXfv3sYPtvj89NNPCggIcL2eayPdsLAwf5MWG2w0\nFDmbjRM4BKv7bB7+sPn3V9MDV6q8mbd///5Wa27atEkzZsxQo0aNVFJSohkzZqh79+6uPd+1kW5M\nTIyKiop04403+udcTE7mn2+/oIntLD169FB4eLgcx9GWLVv884ISr/tATTR48GC9+uqrCg4OVkFB\ngR599FFX+564NtI1fULrbOe7UtvEdTbz5s3z/9zmfkgAv4369ev7T/U1bdq0+m4Zu/vuu7V27Vod\nOnRI3bt3V+vWrd169Hn5Ghk7jqNvvvnG2L5ZXveBS4Nv+115ebnGjh2rLl26aMeOHa6vTbkWutOm\nTTN6bfHZzh512tjJAKDm8m2/q7oNz8QBGtdC1/S1xWer2sAkPz9fhw4dMloPQM3m235XWFiorVu3\nnrH91U2uha7pa4vPlpCQ4G9g0qRJk0tiJReAeQ8//LCuv/56NWzYUFLl6UU393W7FrpnX1tsqleA\n7/baqg1MTp06ZfzIJYBLQ8OGDZWUlGTs+a5tGfM5duyYGjVqpDp1XMvzM0RFRWnixIm64YYb1Ldv\nXyUnJ/sbmJjepwug5luyZInq1at3xk3jXbt2de35riXj+++/r4qKCpWVlSk5OVmjRo3SqFGj3Hq8\n34UamNjqqwugZvv8889VVlambdu2SaqcXqiWobtkyRItWrRIsbGx+uijj/Twww8bCd0LNTApLi52\nvRaAS09JSYnRNrGuhW7dunUlVW4s9t1nZMJvcXstgEtH69attW7dOrVr185/utbNbm6uzek+9dRT\n2rp1q55++mnt2rVLBQUFxm7ttNnABMClxXe1ky8ay8rK/LdKu8HVhbTi4mLVr19fR44c8Z8YA4D/\nBDExMf5j/6+//rp/enT48OH+C2rd4Nrq07Zt2/TFF19o06ZNioyM1LvvvuvWowHAuKNHj/p/vmnT\nJv/P3W6a7lroJicnq2XLlkpJSdGqVavYvgXgP5bJNq6uhW7dunV1+eWXq06dOmratKnKysrcejQA\nGFd1RGvySiDXdi80aNBADz30kO6//36tWLHijBskAKC6O99VS47jKCcnx9U6ri2klZWVKTc3V9df\nf72+//57tWzZ0uh1PQDgJltXLbkWuvv27VNGRoa/B0J+fr7xa8oB4D+Na3O6vi5f27dv14EDB3T8\n+HG3Hg0ANYZroRsYGKixY8fqiiuu0PPPP68jR4649WgAqDFcC13HcVRQUKCSkhKVlJToxIkTbj0a\nAGoMV0K3qKhIjz32mDZs2KB+/fqpT58+6tWrlxuPBoAa5aIX0pYvX64lS5aoTp06mjp1KmELAL/i\noke669atU0ZGhlJTU5WSkuLG1wQANdZFh67X65XX61VwcDBX5gDA/8LV6xZMnlcGgJrgoud0e/To\nofDwcDmOoy1btvhvc5CkOXPmXPQXCAA1yUWHrq2jcwBQE7h+GzAA4MK4QhcALCJ0AcAiQhcALCJ0\nAcAiQhcALPpv1Q1HVfvBy6sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2087e26f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare 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  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(train['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embark=pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</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  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  \n",
       "4      0            373450   8.0500        S     1  0  1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train.drop('PassengerId',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=train.drop('Survived',axis=1)\n",
    "y=train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\softs\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.91      0.85       163\n",
      "          1       0.82      0.65      0.73       104\n",
      "\n",
      "avg / total       0.81      0.81      0.80       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148,  15],\n",
       "       [ 36,  68]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
No results found