titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "titanic project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv('titanic_train.csv')\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "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(train.isnull()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "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(train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "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": [
    "sns.countplot(train['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "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(train['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Embarked', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Fare', ylabel='Density'>"
      ]
     },
     "execution_count": 9,
     "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.distplot(train['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "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.countplot(x='Survived',data=train,hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "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=train,hue='Pclass',palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Density'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oT7TXw+yCVLbXNNPa0e12OMYEnSUOM2pqm3wT4xMirMcBMG9iOl09yubqRrdDMSboLHGYUVPT2IYAOcmR1eMAyEuNJzcljrI9NlxlIp8lDjNqaptayUqKJSYqMn/sSovSqW1qY9v+JrdDMSaoIvO/YBOSahpbI+LBv4HMdhY+fHxjlduhGBNUQ0ocIvKkiHxCRCzRmGFpae+iua2L3DBe2HAw8TFeTp2QzFOb99PWaZPkJnINNRGsAq4CdonIHSIyLYgxmQjU+8R4JPc4wDdcdbSti2e31bodijFBM6TEoap/V9WrgbnAHuB5EfmHiFwvItED1RORJSKyU0QqROS2fq6LiNzlXN8qInOd8wUi8pKI7BCRchH5ml+ddBF5XkR2OV/TAm20GX01TW0AEd3jAN/Ch4XpCTZcZSLakIeeRCQD+ALwJeBt4Of4EsnzA5T3AncDS4EZwHIRmdGn2FKgxHmtxNezAegCvqmq04GFwC1+dW8DXlDVEuAF59iEuJrGVlITosNyj/FAeES4vDSfdZWH2XvIniQ3kWmocxx/Al4DEoBPqeqnVfVxVf0KkDhAtflAhapWqmoH8BiwrE+ZZcBD6rMOSBWRXFWtVdW3AFT1KLADyPOr86Dz/YPAZ4bSBuOu2qbWiHx+oz+XzSvAI/BEmfU6TGQaao/jV6o6Q1V/oKq1ACISC6CqpQPUyQP8/8up5p+//IdcRkSKgDnAeudUTm8Mztfs/j5cRFaKSJmIlNXX1w/SPBNMx9q7ONTSQW6EPTE+kPEpcXx8ahZ/3FRNl+0OaCLQUBPHf/dz7s1B6vS3il3f5UNPWEZEEoEnga+rakDbrKnqfapaqqqlWVlZgVQ1I2xHbTNKZD4xPpArzijgYHM7r7xnf7SYyHPCAWcRGY+vBxAvInP45y/6ZHzDVidSDRT4HecDNUMt40y6Pwk8rKp/8itzsHc4S0RygbpB4jAu691aNdLvqPK3eFoOmYkxPL6xivOn57gdjjEjarAex8XAj/D9Qv8J8GPn9a/AdwepuxEoEZFiEYkBrgRW9ymzGljh3F21EGhyEoIAvwZ2qOpP+qlznfP9dcDTg8RhXFZe00RCjJfkuMieGPcXE+Xhs3PzefHdOuqPtrsdjjEj6oSJQ1UfVNXzgC+o6nl+r0/36QX0V7cLuBVYi29y+wlVLReRm0TkJqfYGqASqADuB252zi8CrgUWi8hm53WJc+0O4EIR2QVc6BybEFZe08yElHh8fw+MHZeXFtDVo/zprWq3QzFmRA02VHWNqv4eKBKRf+17vZ/eQN/ra/AlB/9z9/h9r8At/dR7nf7nP1DVQ8D5J/pcEzo6unp47+BRFk7KcDuUUTclO5F5E9N4fGMVK8+ZNOYSp4lcgw1VjXO+JgJJ/byMOaFddUfp7NYxNTHu74ozCqhsOMaG3bZqrokcJ+xxqOq9ztfbRyccE2m2OxPjY+VW3L4+OSuX7z+znd+v38eCMdjrMpFpqA8A3ikiySISLSIviEiDiFwT7OBM+CuvaSY+2ktmYqzbobgiISaKz88r4LlttdQdbXM7HGNGxFCf47jIeY7ik/huoZ0KfCtoUZmIsb2mmem5SXjG8Pj+1QsL6exWnrD1q0yEGGri6F3I8BLgUVW1AVszqJ4eZXttM6dOSHE7FFdNzkrkrCmZPLJ+nz1JbiLCUBPHX0TkXaAUeEFEsgDrd5sT2nf4OC3tXZw6IdntUFx3zcKJ1DS18eK79ryqCX9DXVb9NuBMoFRVO4FjfHTBQmM+pPeJ8bHe4wC4YHo245Pj+N26vW6HYsxJC2RHv+nAFSKyArgMuCg4IZlIUV7TRJRHmDp+oAWUx44or4erFhTy2q4GKutb3A7HmJMy1Luqfodv6ZGzgDOc10Cr4hoD+HocU7ITiY3yuh1KSLhyfgExXg+/eWOP26EYc1KGunhQKTDDedLbmCEpr2nm41NtZeJe2UlxLJs9gT9squJfL5xK2rgYt0MyZliGOlS1DRgfzEBMZKlrbqOhpd0mxvu44exi2jp7eGTDPrdDMWbYhpo4MoHtIrJWRFb3voIZmAlvvRPjM/NsYtzftPHJnF2SyW//sYf2rm63wzFmWIY6VPW9YAZhIs+2/U0AzLAex0d8+exJrHhgA3/ZUstl8/LdDseYgA31dtxXgD1AtPP9RuCtIMZlwty2miaKM8eRGDt29uAYqrNLMjklJ4lfvVaJTRuacDTUu6q+DPwRuNc5lQc8FaSYTATYtr/Z5jcGICLccHYx7x44ymu7GtwOx5iADXWO4xZ8mys1A6jqLiA7WEGZ8NZ4vIP9ja02v3ECy2ZPYHxyHL94cZf1OkzYGWriaFfVjt4DEYkC7Kfd9OuDiXF7YnxAsVFe/uXcyWzcc4R1lbb0mwkvQ00cr4jId4F4EbkQ+APwl+CFZcJZ78S4DVWd2BVnFJCdFMtdL+xyOxRjAjLUxHEbUA+8A9yIbzvY/whWUCa8ldc0k5cabw+4DSIu2suNH5/Mm5WHbIdAE1aGdMuLqvaIyFPAU6paH9yQTLjbVtM0pnsbj6wf+sN9V80vZNXL7/OLF3fxuxsWBDEqY0bOCXsc4vM9EWkA3gV2iki9iPzn6IRnws2x9i52NxyzFXGHKD7Gy43nTOK1XQ1s2mu9DhMeBhuq+jq+u6nOUNUMVU0HFgCLROQbg725iCwRkZ0iUiEit/VzXUTkLuf6VhGZ63ftARGpE5Ftfep8T0T2i8hm53XJUBpqRseO2mZUYWbe2O1xBOrqhYVkJcVyx7Pv2h1WJiwMljhWAMtVdXfvCVWtBK5xrg1IRLzA3cBSYAawXERm9Cm2FChxXiuBVX7XfgssGeDtf6qqs53XmkHaYEZR78S43Yo7dAkxUXzjgqls3HOEteUH3Q7HmEENljiiVfUjTyg58xzR/ZT3Nx+oUNVK51bex/jo5k/LgIfUZx2QKiK5zme8CljfPcxsq2kmMzGG7KRYt0MJK5eX5jMlO5EfPvcunba9rAlxgyWOjmFeA9/T5VV+x9XOuUDL9OdWZ2jrARFJ66+AiKwUkTIRKauvt/n80VJe49tjXETcDiWsRHk9/NvSaexuOBbQ5LoxbhgscZwuIs39vI4Cpw1St7/fHH0HcIdSpq9VwGRgNlAL/Li/Qqp6n6qWqmppVpbtCTEa2jq72XXwqM1vDNPiadmcOSmDn/39PZrbOt0Ox5gBnTBxqKpXVZP7eSWp6mBDVdVAgd9xPlAzjDJ9Yzqoqt2q2gPcj29IzISAHbXNdPUop9n8xrCICP/+iekcOd7Jz/9uDwWa0BXInuOB2giUiEixiMQAVwJ99/BYDaxw7q5aCDSpau2J3rR3DsRxKb5NpkwI2FzVCMDsgn5HD80QzMxLYfn8Qn7zxu4PbjQwJtQELXGoahdwK7AW2AE8oarlInKTiNzkFFsDVAIV+HoPN/fWF5FHgTeBU0SkWkRucC7dKSLviMhW4Dxg0NuCzejYXNXI+OQ4xqfEuR1KWLttyTTSx8Xy3T+/Q3eP3Z5rQk9QN0twbpVd0+fcPX7fK76Vd/uru3yA89eOZIzhJNBJ06sWFAYpkv5trmpkdkHqqH5mJEpJiOY/PzWDrz76Ng+9uYfrFxW7HZIxHxLMoSozhhxqaWfvoePMLkx1O5SI8KlZuZwzNYsfrd1JbVOr2+EY8yGWOMyI2FLdCGA9jhEiIvz3spl0q/LtP26lx4asTAixxGFGxOZ9jXgEu6NqBBVmJPDvl0zntV0N/PYfe9wOx5gPWOIwI+Ltqkam5iQxzvYYH1HXLJzI+dOyueO5d3n3QLPb4RgDWOIwI6CnR9lS1cicQrsNd6SJCD+8bBbJcdF87dHNtHV2ux2SMZY4zMnbfegYzW1dzLH5jaDITIzlR5+fxc6DR/n+M9vdDscYSxzm5G3e1whgd1QF0bmnZHPjOZN4eP0+niirGryCMUFkicOctLerjpAYG8XkrES3Q4lo37r4FBZNyeA/ntrGVucuNmPcYInDnLTNVY3Myk/B67EVcYMpyuvhF8vnkpUYy02/20RDS7vbIZkxyhKHOSltnd28W3vUnt8YJenjYrj32nkcOtbBv/x+k02WG1fYvZPmpGypaqSrR+2OqmEa7t4bl87J47GNVVx+75tcXlqAZ4D9T0Z72RkzNljiMCdlw+7DiMAZRZY4RtOs/FQaj3fyXPkB0hJiuPjU8W6HZMYQSxzmpKzffZhTcpJITYhxO5Qx5+ySTA4f6+CV9+pJS4hhfnG62yGZMcLmOMywdXb3sGnvERZOynA7lDFJRPjU6ROYmpPI6i37ee/gUbdDMmOEJQ4zbO/sb6K1s5sF9peua7weYfkZheQkx/HIhn3UNNpKuib4LHGYYVtfeRiAMyxxuCo22suKM4uIj/by0Jt7aDze4XZIJsJZ4jDDtq7yEFOyE8lMjHU7lDEvJT6a684sor2rh4fe3Gu36ZqgssRhhqW9q5v1uw9x1pRMt0MxjvEpcVy1oJC6o208smGfbTtrgsbuqjLDcudzO2nr7KFHddjPIpiRV5KdxKVz8njyrf08tXk/1ywsRAZ4xsOY4bIehxmWXQeP4hWhOHOc26GYPuZNTOe8U7LZtPcId79U4XY4JgJZj8MMy666FgozEoiN8rodiunHBdOzaTzewY/+9h75aQl8Zk6e2yGZCBLUHoeILBGRnSJSISK39XNdROQu5/pWEZnrd+0BEakTkW196qSLyPMissv5ao8sj7L6o+3UNrVRkm2r4YYqEeHSuXmcOSmDb/1xCxt2H3Y7JBNBgpY4RMQL3A0sBWYAy0VkRp9iS4ES57USWOV37bfAkn7e+jbgBVUtAV5wjs0oemlnHQBTc5JcjsScSJTHwz3XzqMgLYGbH95EbZM942FGRjCHquYDFapaCSAijwHLAP8tzJYBD6mqAutEJFVEclW1VlVfFZGift53GXCu8/2DwMvAd4LTBNOfv5UfIDU+mtyUOLdDMYP469ZaPn36BFa98j6fv+dNvnz2JKK9A/+9aIsimqEI5lBVHuC/VVm1cy7QMn3lqGotgPM1u79CIrJSRMpEpKy+vj6gwM3AjrV38equBqZPSLa7dcJEdnIcn59XQPWRVp7eXIPv7zRjhi+YiaO/3yp9f2KHUmZYVPU+VS1V1dKsrKyReEsDvPpePR1dPZyam+x2KCYAMyYks3haNm/tO8K6ykNuh2PCXDATRzVQ4HecD9QMo0xfB0UkF8D5WneScZoArC0/QFpCNBMz7DbccLN4WjbTxifx13dq2d1wzO1wTBgLZuLYCJSISLGIxABXAqv7lFkNrHDurloINPUOQ53AauA65/vrgKdHMmgzsOMdXTy//SAXzRhv28SGIY8Il5cWkD4ulkc27KOptdPtkEyYClriUNUu4FZgLbADeEJVy0XkJhG5ySm2BqgEKoD7gZt764vIo8CbwCkiUi0iNziX7gAuFJFdwIXOsRkFz207wLGObj43L9/tUMwwxUV7uWZBIZ1dPTxmy5KYYQrqA4CqugZfcvA/d4/f9wrcMkDd5QOcPwScP4JhmiF68q1qCtLjKZ2YRkVdi9vhmGHKTo7j0rl5PL6xirXlB7jktFy3QzJhxpYcMUNS09jKP94/xGfn5OOxYaqwd3p+KgsnZfB6RQPv7G9yOxwTZixxmCH53bq9AFxmw1QR45LTxlOQFs+f3qqm4Wi72+GYMGKJwwzqWHsXD6/by8UzxlOQnuB2OGaERHk8LJ9fiNcjPLxhLx1dPW6HZMKEJQ4zqD+UVdHc1sWXzyl2OxQzwlITYriitIC65nae3rzfHg40Q2KJw5xQW2c397+2mzmFqcybaFvERqKSnCQWT8vm7apGHtlge6uYwVniMCf069d3s7+xlW9fPM3tUEwQnTctm5LsRG5fvZ2t1Y1uh2NCnCUOM6C65jZ++VIFF83I4czJGW6HY4Ko9+HAzMQY/uX3b9F4vMPtkEwIs8Rh+tXdo3zzD1vo6lH+7ZLpbodjRsG42Ch+ec086o628Y3HN9NjDweaAVjiiCCqysHmNrbXNLGlqpGXd9ZxsLltWO/1ixd38dquBm7/9Km2PewYMrsglf/85Axe2lnPL1+2bWdN/2zr2AjQ1dPDuvcPsX73YQ4d++cQw+NlvhXrizISuOS0XD45awLTc5NOuBy6qnL3SxX87O+7+OycPK44o2DAsiYyXbNwImV7j/CT599jdkEaZ5Vkuh2SCTGWOMLcgeY2Htuwj7qj7RRljOOcqVnkpsQRE+XhY5MzeWd/Ey/vrOPeVyv55cvvMylrHEtOHc+SmeM5LS/lQ0mksr6FHzz7Ls9vP8ilc/L44WWzbM+NMUhE+MFnT2N7TTNffext/vrVs8hNiXc7LBNCLHGEsYq6Fh5ev5eYKA8rFk5kWp89MuYXpzO/OJ0bzirmUEs7z5Uf4Nl3DnyQRDLGxTAlO5HYaC81ja1U1LWQEOPlO0umceM5k2xpkTEsISaKVdfMY9n/vM7Khzbx+I0LSYixXxfGx34SwtTeQ8d46M09ZCbGsuLMiaQmxJywfEZiLFcvmMjVCybSeLyDv++oY13lIXY3HKOttZOCtHiuKC3g07MnkJNsW8IamJKdyF3L5/Dlh8r4yiNvc++184g6wbazZuywxBGGGo6289Cbe0mJj+aLZxWTGBvYP2NqQgyXzcu3dafMoM6fnsPtnz6V//10Obf/ZTv/texUG740ljjCTWd3D49u3IcIXL8o8KRhTKCuPbOI6sZW7n2lktzUOG4+d4rbIRmX2W+dMPPstgPUNrWx4syJpI878fCUMSPlOxdP40BTG3c+t5PYKC83nGXrlo1lljjCSGVDC+sqD7FocgbTxicPXsGYEeLxCD/+/Ol0dPXw/We2E+URrvtYkdthGZfYTFeYaOvs5qm395OWEM2FM8a7HY4Zg6K8Hu5aPoeLZuTwf1aX8+A/9rgdknGJJY4wce8rlTS0dPCZ2XnERNk/m3FHtNfD/1w1lwum+5LHj9butKXYxyD7DRQGahpbWfVKBTPzUijJSXI7HDPGxUR5uOeauVxRWsD/vFTB//rDVjq7bROoscTmOMLAD597lx6FpafaEJUJrkfWD30/jjs+dxq5qXH87O+7ONDcyi+Wz7UbNsaIoPY4RGSJiOwUkQoRua2f6yIidznXt4rI3MHqisj3RGS/iGx2XpcEsw1u27a/iac31/Dls4tJs/8oTQgREb5+wVT+32Wz2LjnCJ/6xetsqWp0OywzCoLW4xARL3A3cCFQDWwUkdWqut2v2FKgxHktAFYBC4ZQ96eq+qNgxR5KfvL8e6TER3PjxyfzzJbaoH1OIH9pGuPv86UFnDI+iX/5/Vt8/p43+c9PzeDqBYX2oGAEC2aPYz5QoaqVqtoBPAYs61NmGfCQ+qwDUkUkd4h1I96mvUd48d06bvz4JJLjot0Ox5gBzcpP5ZmvnMXCyRn8x1Pb+PJDm2hoaXc7LBMkwZzjyAOq/I6r8fUqBiuTN4S6t4rICqAM+KaqHun74SKyElgJUFhYOMwmuOtHa3eSmRjLF4Z5v7z1IsxoShsXw2+/cAYPvLGbO9fuZMnPXuXOy2axeFqO26GZERbMHkd//dS+9+0NVOZEdVcBk4HZQC3w4/4+XFXvU9VSVS3NysoaUsCh5I2KBt6sPMQt5022VUlN2PB4hC+dPYm/3HoWmYmxfPG3ZXz3z+9wvKPL7dDMCArmb6RqwH8XoHygZohlYgaqq6oHe0+KyP3AMyMXcmhQVe5cu5MJKXFctSA8e0sm8g3Wo71qfiHP7zjIo+v3sXbbAe6/rpS5hWmjFJ0JpmD2ODYCJSJSLCIxwJXA6j5lVgMrnLurFgJNqlp7orrOHEivS4FtQWyDK/6+o44tVY189fwSYqO8bodjzLBEeT0snZnLDWcX063KZav+wU/+ttOe+YgAQetxqGqXiNwKrAW8wAOqWi4iNznX7wHWAJcAFcBx4PoT1XXe+k4RmY1v6GoPcGOw2uCGnh7lx3/bSVFGAp+zZc9NBJiUmchXF5ewvbaZu16s4KWd9fz0itOZkm0Ps4YrGQvLBZSWlmpZWZnbYQzJ6i01fPXRt/n5lbNZNjvvQ9dsstuEs6sWFPLctgN898/vcKy9i+8smcYXPlZkO02GMBHZpKqlfc/bkiMhpLO7h58+/x6n5CTxyVkT3A7HmBG3ZOZ4nvv62Zw1JZP/emY71z6wnprGVrfDMgGy23VCyJObqtndcIz7V5Titb/CTITx7zEvnpZNclw0f32nlsU/fplL5+RzWl7Kh8rbjSGhy3ocIaKts5ufv7CLOYWpXDA92+1wjAkqEeGM4nS+sngKWYmxPLphH396q5qOLps4DweWOELE79ftpbapjW9dfIot1WDGjIzEWFaeM5mPT81i094j3P1SBbVNNnQV6ixxhICW9i5++fL7nDUlk49NznQ7HGNGldcjXHzqeL54VjFtXd388uX3+cf7DbbPRwizxBEC7n+1ksPHOvjWxae4HYoxrpmc5btttyQ7kWe21nLDg2UcsvWuQpIlDpdVHznOPa+8zydm5XJ6Qarb4RjjqnGxUVy7cCKfmpXL6xUNLP35a7xR0eB2WKYPSxwu+79/3YFHhH+/ZLrboRgTEkSEMydn8tTNi0iKi+KaX6/njmfftSfOQ4glDhe9vquBZ7cd4JbzJjMhNd7tcIwJKTMmJPPMV87myjMKueeV97nsnjfZe+iY22EZLHG4prO7h+/9pZzC9AS+dPYkt8MxJiTFx3j5wWdP45dXz2V3fQtLfvYa977yvvU+XGaJwyW/eWM3FXUt/J9PzSAu2hYyNOZELjktl+e+fg6LpmTyg2ff5VO/eJ1Nez+yDY8ZJZY4XFBR18KP//YeF0zP5vzptsmNMUMxITWeX11Xyr3XzqPxeCefW/UPbn54E5X1LW6HNubYkiOjrKu7h2/+YQvxMV7+v0tPczscY8LOxaeOZ9GUTH71WiX3vVrJ2vKDXF6az8pzJlOcOc7t8MYESxyj7P+t3cmWqkb+56o5ZCfHuR2OMWEpMTaKr18wlasXTOQXL+7isQ1VPLahium5yZw1JZOJGQknXIHB1sE6OZY4RtFz22q599VKrllYaKvfGjMCspJi+a9lM7l18RS+/cetrK88zPbaZrKSYimdmMacwjQSY+3X3Eiz/0dHydv7jvD1xzdzekEq//uTM9wOx5iQF+j+MxfNGM+5U7PZWt1I2d4jPLvtAGvLDzApK5HT8lI4NTeZBEsiI8L+XxwFOw8c5YYHy8hOiuNXK0ptO1hjgiQmykNpUTqlRekcbG5jc1Uj2/Y38ee39/P05v1MzkpkxoRkFk/LZnyKDRUPlyWOINta3ciKBzYQ4/Xw4Bfnk5UU63ZIxowJOclxXHzqeC6akUNtUxvv7G/inf1NPL25hqc31zAzL5nzp+VwwfQcZuYl26rUAbDEEURPb97Pd57cSmZiLA9/aQETM+yOD2NGm4gwITWeCanxXDQjh7qj7cRGe3hhRx13vbiLn7+wi5zkWM6dms1ZJZl8bHIGGYn2B96JWOIIgiPHOvj+M9v509v7mV+Uzt1Xz7WehjEhQETISY7jqgWF3HzuFA61tPPSznpe2HGQNdtqebysCoAZucksmpLBvIlpnF6QSm6KLQnkzxLHCGpq7eT36/Zy7yvvc7yjm68unsJXzi8h2mvPWRoTijISY7lsXj6Xzcunq7uHbTXNvFHRwOu7GnjwH3u5/7XdAOQkx3J6fioz81KYlDWOSZmJFGeOIz5mbM5XBjVxiMgS4OeAF/iVqt7R57o41y8BjgNfUNW3TlRXRNKBx4EiYA9wuaq6tvZAc1snb75/iLXbDrBmWy1tnT2cPy2bby+Zxinjk9wKyxgToCivh9kFqcwuSOWW86bQ3tXN9ppmtlQ1sqW6iS1Vjfxt+8EP1ZmQEseE1Hiyk2PJTor759ekWNLHxZASH01KQjRJsVERNYcStMQhIl7gbuBCoBrYKCKrVXW7X7GlQInzWgCsAhYMUvc24AVVvUNEbnOOvxOMNrR1dnPkeAdNrZ00Hu+kqbWTpuOd7Dt8nPfrW3i/voVddS2oQnJcFJfOyePqBROZmZcSjHCMMSMkkFt9Y6K8nFGUzhlF6XR09XDoWDv1R9tpaGmnoaWDQ8c62HPoOEfbOmkfYM90j0ByfLQvkfTzGhcbRVy0l4QY3ys+2ktCTBTxMf88F+31EOURvB4hyuPB65UPjr0ieDyjl5iC2eOYD1SoaiWAiDwGLAP8E8cy4CH17RG5TkRSRSQXX29ioLrLgHOd+g8CLxOkxHH7X7bz6IaP/oB5PcLE9AQmZY3jktNymV/s+6GyISljIltMlIfclPgB5zw6uno42tbJgkkZHD7WQXOr8wdnn1djayfVR1o/OO7uOfltcj0CHhFEQBCc/3H/ilLOmZp10u/vL5iJIw+o8juuxterGKxM3iB1c1S1FkBVa0Uku78PF5GVwErnsEVEdg6nEQOpBF4ayTccXCYwVrZCs7ZGJmurCz7+f0+q+sT+TgYzcfTXb+qbVgcqM5S6J6Sq9wH3BVInlIlImaqWuh3HaLC2RiZra+QI5thKNVDgd5wP1AyxzInqHnSGs3C+1o1gzMYYYwYRzMSxESgRkWIRiQGuBFb3KbMaWCE+C4EmZxjqRHVXA9c5318HPB3ENhhjjOkjaENVqtolIrcCa/HdUvuAqpaLyE3O9XuANfhuxa3Adzvu9Seq67z1HcATInIDsA/4fLDaEGIiZthtCKytkcnaGiHEd0OTMcYYMzR2/6gxxpiAWOIwxhgTEEscIU5ElojIThGpcJ6UjxgiUiAiL4nIDhEpF5GvOefTReR5EdnlfE1zO9aRIiJeEXlbRJ5xjiOyrc7DvH8UkXedf98zI7it33B+freJyKMiEhepbe1liSOE+S29shSYASwXkUjaPrAL+KaqTgcWArc47etdVqYEeME5jhRfA3b4HUdqW38OPKeq04DT8bU54toqInnAV4FSVZ2J72aeK4nAtvqzxBHaPli2RVU7gN6lVyKCqtb2Lmqpqkfx/XLJw9fGB51iDwKfcSXAESYi+cAngF/5nY64topIMnAO8GsAVe1Q1UYisK2OKCBeRKKABHzPnEVqWwFLHKFuoCVZIo6IFAFzgPX0WVYG6HdZmTD0M+DbgP9KeJHY1klAPfAbZ1juVyIyjghsq6ruB36E79GAWnzPov2NCGyrP0scoe2kl14JByKSCDwJfF1Vm92OJxhE5JNAnapucjuWURAFzAVWqeoc4BgRNlTTy5m7WAYUAxOAcSJyjbtRBZ8ljtA2lGVbwpqIRONLGg+r6p+c05G4rMwi4NMisgffkONiEfk9kdnWaqBaVdc7x3/El0gisa0XALtVtV5VO4E/AR8jMtv6AUscoW0oy7aELWcjr18DO1T1J36XIm5ZGVX9N1XNV9UifP+OL6rqNURmWw8AVSJyinPqfHxbIkRcW/ENUS0UkQTn5/l8fHN1kdjWD9iT4yFORC7BNzbeu/TKyS2SHEJE5CzgNeAd/jnu/1188xxPAIU4y8qo6mFXggwCETkX+F+q+kkRySAC2yois/HdBBCDbxeC6/H9oRqJbb0duALfXYJvA18CEonAtvayxGGMMSYgNlRljDEmIJY4jDHGBMQShzHGmIBY4jDGGBMQSxzGGGMCYonDmCATkUtFREVkmtuxGDMSLHEYE3zLgdfxPfhnTNizxGFMEDnrcC0CbsBJHCLiEZFfOns4PCMia0TkMufaPBF5RUQ2icja3mUrjAklljiMCa7P4NuX4j3gsIjMBT4LFAGn4XvK+Ez4YN2uXwCXqeo84AEgYlYKMJEjyu0AjIlwy/EtGQO+xQ2XA9HAH1S1BzggIi85108BZgLP+5Y9wotvqW5jQoolDmOCxFmHajEwU0QUXyJQ4M8DVQHKVfXMUQrRmGGxoSpjgucy4CFVnaiqRapaAOwGGoDPOXMdOcC5TvmdQJaIfDB0JSKnuhG4MSdiicOY4FnOR3sXT+Lb8Kca2Abci2814CZne+DLgB+KyBZgM769HYwJKbY6rjEuEJFEVW1xhrM2AIucfSyMCXk2x2GMO54RkVR8+1V835KGCSfW4zDGGBMQm+MwxhgTEEscxhhjAmKJwxhjTEAscRhjjAmIJQ5jjDEB+f8BubzG3FGMwxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "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": [
    "train['Age'].hist(bins=30,alpha=.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "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='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 17,
     "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=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_age(cols):\n",
    "    Age =cols[0]\n",
    "    Pclass=cols[1]\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",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].isnull().sum()"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Pclass\n",
       "0    22.0       3\n",
       "1    38.0       1\n",
       "2    26.0       3\n",
       "3    35.0       1\n",
       "4    35.0       3\n",
       "..    ...     ...\n",
       "886  27.0       2\n",
       "887  19.0       1\n",
       "888   NaN       3\n",
       "889  26.0       1\n",
       "890  32.0       3\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Age','Pclass']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train[['Age','Pclass']].apply(input_age,axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.0\n",
       "1      38.0\n",
       "2      26.0\n",
       "3      35.0\n",
       "4      35.0\n",
       "       ... \n",
       "886    27.0\n",
       "887    19.0\n",
       "888    24.0\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "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(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now going to drop this cabin column\n"
   ]
  },
  {
   "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>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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "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.heatmap(train.isnull(),yticklabels=False,cmap='viridis',cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          891 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(train['Sex'],drop_first=True)\n",
    "embark=pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0              1         0       3  22.0      1      0   7.2500\n",
       "1              2         1       1  38.0      1      0  71.2833\n",
       "2              3         1       3  26.0      0      0   7.9250\n",
       "3              4         1       1  35.0      1      0  53.1000\n",
       "4              5         0       3  35.0      0      0   8.0500\n",
       "..           ...       ...     ...   ...    ...    ...      ...\n",
       "886          887         0       2  27.0      0      0  13.0000\n",
       "887          888         1       1  19.0      0      0  30.0000\n",
       "888          889         0       3  24.0      1      2  23.4500\n",
       "889          890         1       1  26.0      0      0  30.0000\n",
       "890          891         0       3  32.0      0      0   7.7500\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>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",
       "    </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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0              1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1              2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2              3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3              4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4              5         0       3  35.0      0      0   8.0500     1  0  1\n",
       "..           ...       ...     ...   ...    ...    ...      ...   ... .. ..\n",
       "886          887         0       2  27.0      0      0  13.0000     1  0  1\n",
       "887          888         1       1  19.0      0      0  30.0000     0  0  1\n",
       "888          889         0       3  24.0      1      2  23.4500     0  0  1\n",
       "889          890         1       1  26.0      0      0  30.0000     1  0  0\n",
       "890          891         0       3  32.0      0      0   7.7500     1  1  0\n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# build a logistic regrassion model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train.drop('Survived',axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Survived'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(train.drop('Survived',axis=1),\n",
    "                                               train['Survived'],test_size=0.3,\n",
    "                                               random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=train.drop('Survived',axis=1)\n",
    "y=train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,\n",
    "                                               random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# training and rediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "520    1\n",
       "510    1\n",
       "446    1\n",
       "2      1\n",
       "691    1\n",
       "      ..\n",
       "575    0\n",
       "838    1\n",
       "337    1\n",
       "523    1\n",
       "863    0\n",
       "Name: Survived, Length: 623, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: 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",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "lr = LogisticRegression()\n",
    "lr.fit(x_train, y_train)\n",
    "lr_predict = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[134  20]\n",
      " [ 42  72]]\n",
      "76.86567164179104\n"
     ]
    }
   ],
   "source": [
    "lr_conf_matrix = confusion_matrix(y_test, lr_predict)\n",
    "lr_acc_score = accuracy_score(y_test, lr_predict)\n",
    "print(lr_conf_matrix)\n",
    "print(lr_acc_score*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)\n",
    "dt_predict = dt.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[126  28]\n",
      " [ 42  72]]\n",
      "73.88059701492537\n"
     ]
    }
   ],
   "source": [
    "dt_conf_matrix = confusion_matrix(y_test, dt_predict)\n",
    "dt_acc_score = accuracy_score(y_test, dt_predict)\n",
    "print(dt_conf_matrix)\n",
    "print(dt_acc_score*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 NaiveBayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train,y_train)\n",
    "nb_predict=nb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[129  25]\n",
      " [ 38  76]]\n",
      "76.49253731343283\n"
     ]
    }
   ],
   "source": [
    "nb_conf_matrix = confusion_matrix(y_test, nb_predict)\n",
    "nb_acc_score = accuracy_score(y_test, nb_predict)\n",
    "print(nb_conf_matrix)\n",
    "print(nb_acc_score*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Random Forests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train, y_train)\n",
    "rf_predict=rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[141  13]\n",
      " [ 34  80]]\n",
      "82.46268656716418\n"
     ]
    }
   ],
   "source": [
    "rf_conf_matrix = confusion_matrix(y_test, rf_predict)\n",
    "rf_acc_score = accuracy_score(y_test, rf_predict)\n",
    "print(rf_conf_matrix)\n",
    "print(rf_acc_score*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 SVM Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[141  13]\n",
      " [ 34  80]]\n",
      "82.46268656716418\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "lin_svc = SVC()\n",
    "lin_svc.fit(x_train, y_train)\n",
    "lin_svc=rf.predict(x_test)\n",
    "\n",
    "lin_svc_conf_matrix = confusion_matrix(y_test, rf_predict)\n",
    "lin_svc_acc_score = accuracy_score(y_test, rf_predict)\n",
    "print(lin_svc_conf_matrix)\n",
    "print(lin_svc_acc_score*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[141  13]\n",
      " [ 34  80]]\n",
      "82.46268656716418\n"
     ]
    }
   ],
   "source": [
    "rbf_svc = SVC(kernel='linear')\n",
    "rbf_svc.fit(x_train, y_train)\n",
    "rbf_svc=rf.predict(x_test)\n",
    "\n",
    "rbf_svc_conf_matrix = confusion_matrix(y_test, rf_predict)\n",
    "rbf_svc_acc_score = accuracy_score(y_test, rf_predict)\n",
    "print(rbf_svc_conf_matrix)\n",
    "print(rbf_svc_acc_score*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename='redwine.pkl'\n",
    "pickle.dump(lr,open(filename,\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orignal</th>\n",
       "      <th>predicted</th>\n",
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       "      <th>46</th>\n",
       "      <td>0</td>\n",
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       "      <th>47</th>\n",
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       "      <th>48</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>49</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    orignal  predicted\n",
       "0         0          0\n",
       "1         1          1\n",
       "2         0          0\n",
       "3         1          0\n",
       "4         0          0\n",
       "5         0          0\n",
       "6         1          0\n",
       "7         0          0\n",
       "8         1          1\n",
       "9         1          1\n",
       "10        0          0\n",
       "11        0          0\n",
       "12        1          1\n",
       "13        1          0\n",
       "14        1          0\n",
       "15        0          0\n",
       "16        1          0\n",
       "17        0          0\n",
       "18        0          0\n",
       "19        1          1\n",
       "20        0          0\n",
       "21        0          0\n",
       "22        1          1\n",
       "23        1          1\n",
       "24        0          0\n",
       "25        0          0\n",
       "26        0          0\n",
       "27        0          0\n",
       "28        0          0\n",
       "29        0          0\n",
       "30        1          1\n",
       "31        1          1\n",
       "32        1          1\n",
       "33        1          0\n",
       "34        0          0\n",
       "35        1          1\n",
       "36        0          0\n",
       "37        0          0\n",
       "38        0          0\n",
       "39        1          0\n",
       "40        1          0\n",
       "41        1          1\n",
       "42        1          1\n",
       "43        0          0\n",
       "44        0          0\n",
       "45        1          0\n",
       "46        0          0\n",
       "47        0          0\n",
       "48        0          0\n",
       "49        0          0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array(y_test)\n",
    "predicted=np.array(lr.predict(x_test))\n",
    "df_com=pd.DataFrame({\"orignal\":a,\"predicted\":predicted},index=range(len(a)))\n",
    "df_com.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 52,
     "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.distplot(df_com)"
   ]
  },
  {
   "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",
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