Titanic_LR-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/narsingrao/Documents/Satish_ML'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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      ],
      "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1305</td>\n",
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       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
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       "      <td>39.0</td>\n",
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       "      <td>PC 17758</td>\n",
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       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>417</th>\n",
       "      <td>1309</td>\n",
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       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
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       "      <td>2668</td>\n",
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       "      <td>C</td>\n",
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      "text/plain": [
       "     PassengerId  Pclass                          Name     Sex   Age  SibSp  \\\n",
       "413         1305       3            Spector, Mr. Woolf    male   NaN      0   \n",
       "414         1306       1  Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "415         1307       3  Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "416         1308       3           Ware, Mr. Frederick    male   NaN      0   \n",
       "417         1309       3      Peter, Master. Michael J    male   NaN      1   \n",
       "\n",
       "     Parch              Ticket      Fare Cabin Embarked  \n",
       "413      0           A.5. 3236    8.0500   NaN        S  \n",
       "414      0            PC 17758  108.9000  C105        C  \n",
       "415      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416      0              359309    8.0500   NaN        S  \n",
       "417      1                2668   22.3583   NaN        C  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()\n",
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "<p>891 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a0b3d90>"
      ]
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     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "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(), yticklabels=False, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a0b3e50>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a83c890>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', hue='Sex', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19f40a50>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', hue='Sex', data=train, palette = 'RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b037a90>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', hue='Pclass', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ad9b910>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(),kde=False, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aeecc10>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Age'].plot.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1afe1e90>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEECAYAAADOJIhPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAXqUlEQVR4nO3dfVBU5+H28euwhFV5kRLjH8SXAV+mOI11yEab/hCTp6nYziQmUxTUB01Mk05GSclURUlc0BqBccLY6KgxM2kaNb7bxKczrRPxhYIpdmzRSommNjFRqKNiJuzGLrBnnz98wlOUlyVlWfD+fv5ib84erxPNufY+e/ZeKxAIBAQAMFJEuAMAAMKHEgAAg1ECAGAwSgAADEYJAIDBIsMdoKdqamrkdDrDHQMABhSfz6dJkybdMT7gSsDpdColJSXcMQBgQKmrq+twnMtBAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgsJB9YviNN97QkSNH1NLSojlz5mjy5Mlavny5LMvSuHHjVFhYqIiICG3cuFHHjh1TZGSkCgoKNHHixB7/Wb4Wv5z3OEJwFP+d/poLAL4WkhKorq7WX//6V+3cuVM3b97UW2+9peLiYuXl5WnKlClyu90qLy9XYmKiTp48qb1796qhoUG5ubnav39/j/885z0OPbj0nRAcyX/n1Lr54Y4AAF0KSQlUVlZq/PjxWrRokTwej5YtW6Y9e/Zo8uTJkqT09HRVVVUpKSlJaWlpsixLiYmJ8vv9amxsVEJCQqf79vl8d6yB0Z/XEupsvQ4A6A9CUgI3btxQfX29tmzZokuXLumFF15QIBCQZVmSpOjoaDU1Ncnj8Sg+Pr7teV+Pd1UCA20BuYGUFcDdq7MXpCEpgfj4eCUnJysqKkrJyclyOp3617/+1fZ7r9eruLg4xcTEyOv1thuPjY0NRSQAQAdCcnfQgw8+qD/+8Y8KBAK6cuWKbt68qYcffljV1dWSpIqKCrlcLqWmpqqyslK2bau+vl62bXc5CwAA9K6QzAQeffRR/fnPf1ZmZqYCgYDcbrdGjBihlStXqqysTMnJycrIyJDD4ZDL5VJWVpZs25bb7Q5FHABAJ6xAIBAId4ieqKur6/A6O3cHAUDnOjt38mExADAYJQAABqMEAMBglAAAGIwSAACDUQIAYDBKAAAMRgkAgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABosM1Y6ffPJJxcbGSpJGjBihrKwsvfrqq3I4HEpLS9PixYtl27aKiop07tw5RUVFac2aNRo9enSoIgEAbhOSEvD5fJKkbdu2tY3NnDlTGzZs0MiRI/X888+rtrZWly9fVnNzs3bv3q2amhqVlJRo8+bNoYgEAOhASErgo48+0s2bN7Vw4UK1trYqNzdXzc3NGjVqlCQpLS1NH374oa5evaqpU6dKkiZNmqSzZ892u2+fz6e6urp2YykpKb1/EL3k9qwA0J+EpAQGDRqkZ599VrNmzdKnn36q5557TnFxcW2/j46O1ueffy6Px6OYmJi2cYfDodbWVkVGdh7L6XT265P+7QZSVgB3r85ekIakBJKSkjR69GhZlqWkpCTFxsbqiy++aPu91+tVXFyc/v3vf8vr9baN27bdZQEAAHpXSO4O2rdvn0pKSiRJV65c0c2bNzVkyBB99tlnCgQCqqyslMvlUmpqqioqKiRJNTU1Gj9+fCjiAAA6EZKX3ZmZmVqxYoXmzJkjy7K0du1aRUREaMmSJfL7/UpLS9N3v/tdPfDAA6qqqlJ2drYCgYDWrl0bijgAgE6EpASioqL02muv3TG+Z8+edo8jIiK0evXqUEQAAASBD4sBgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBglAAAGIwSAACDUQIAYDBKAAAMRgkAgMEoAQAwWMhK4Pr165o2bZouXLigixcvas6cOZo7d64KCwtl27YkaePGjcrMzFR2drbOnDkTqigAgE6EpARaWlrkdrs1aNAgSVJxcbHy8vL07rvvKhAIqLy8XLW1tTp58qT27t2rsrIyrVq1KhRRAABdCEkJlJaWKjs7W8OHD5ck1dbWavLkyZKk9PR0nThxQqdOnVJaWposy1JiYqL8fr8aGxtDEQcA0InI3t7hgQMHlJCQoKlTp2rr1q2SpEAgIMuyJEnR0dFqamqSx+NRfHx82/O+Hk9ISOhy/z6fT3V1de3GUlJSevkoes/tWQGgP+n1Eti/f78sy9KHH36ouro65efnt3uF7/V6FRcXp5iYGHm93nbjsbGx3e7f6XT265P+7QZSVgB3r85ekPb65aAdO3Zo+/bt2rZtm1JSUlRaWqr09HRVV1dLkioqKuRyuZSamqrKykrZtq36+nrZtt3tLAAA0Lt6fSbQkfz8fK1cuVJlZWVKTk5WRkaGHA6HXC6XsrKyZNu23G53X0QBAPwHKxAIBMIdoifq6uo6vMTy4NJ3wpCma6fWzQ93BACQ1Pm5kw+LAYDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBgQZXA3r172z1+553+t1gbAKDnulxK+ne/+52OHDmi6upq/elPf5Ik+f1+ffzxx5o/nxUyAWCg67IEpk6dqvvuu09ffPGFsrKyJEkREREaOXJkn4QDAIRWlyUwdOhQTZkyRVOmTNH169fl8/kk3ZoNAAAGvqC+WWzVqlU6fvy4hg8f3val8bt27Qp1NgBAiAVVAqdPn9bhw4cVEcHNRABwNwnqrD569Oi2S0EAgLtHUDOBhoYGPfrooxo9erQkcTkIAO4SQZXAa6+9FuocAIAwCKoEfvvb394xtnjx4l4PAwDoW0GVwLBhwyRJgUBAf//732XbdkhDAQD6RlAlkJ2d3e7xT3/605CEAQD0raBK4JNPPmn7+erVq2poaAhZIABA3wmqBNxud9vPTqdTy5YtC1kgAEDfCaoEtm3bphs3bujzzz/XiBEjlJCQ0OX2fr9fr7zyij755BM5HA4VFxcrEAho+fLlsixL48aNU2FhoSIiIrRx40YdO3ZMkZGRKigo0MSJE3vlwAAA3QuqBH7/+99r/fr1GjNmjD7++GMtXrxYM2fO7HT7o0ePSpJ27dql6urqthLIy8vTlClT5Ha7VV5ersTERJ08eVJ79+5VQ0ODcnNztX///t45MgBAt4IqgbffflsHDhxQdHS0PB6PFixY0GUJPPbYY3rkkUckSfX19Ro2bJiOHTumyZMnS5LS09NVVVWlpKQkpaWlybIsJSYmyu/3q7GxsduZBgCgdwRVApZlKTo6WpIUExMjp9PZ/Y4jI5Wfn68PPvhAr7/+uo4ePSrLsiRJ0dHRampqksfjUXx8fNtzvh7vqgR8Pp/q6urajaWkpARzGGFxe1YA6E+CKoFRo0appKRELpdLp06d0qhRo4LaeWlpqZYsWaLZs2e3W3vI6/UqLi5OMTEx8nq97cZjY2O73KfT6ezXJ/3bDaSsAO5enb0gDWoBudmzZ2vo0KE6ceKEDhw4oHnz5nW5/Xvvvac33nhDkjR48GBZlqXvfOc7qq6uliRVVFTI5XIpNTVVlZWVsm1b9fX1sm2bS0EA0IeCmgmUlJSopKREY8eO1TPPPKPly5drx44dnW4/ffp0rVixQvPmzVNra6sKCgo0ZswYrVy5UmVlZUpOTlZGRoYcDodcLpeysrJk23a7W1EBAKEXVAlERkZq7NixkqSRI0d2+70CQ4YM0a9+9as7xrdv337HWG5urnJzc4OJAQDoZUGVQGJiosrKyjRp0iSdOXNGw4cPD3UuAEAfCOo9geLiYiUkJOj48eNKSEhQcXFxqHMBAPpAUDMBp9Opp59+OsRRAAB9jS8NBgCDUQIAYDBKAAAMRgkAgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBgkb29w5aWFhUUFOjy5ctqbm7WCy+8oLFjx2r58uWyLEvjxo1TYWGhIiIitHHjRh07dkyRkZEqKCjQxIkTeztOvxdo9cmKdIY7Rof6czYAvaPXS+DgwYOKj4/XunXrdOPGDT311FP69re/rby8PE2ZMkVut1vl5eVKTEzUyZMntXfvXjU0NCg3N1f79+/v7Tj9nhXp1GerHwh3jA6Ncv8t3BEAhFivl8CMGTOUkZHR9tjhcKi2tlaTJ0+WJKWnp6uqqkpJSUlKS0uTZVlKTEyU3+9XY2OjEhISejsSAKATvV4C0dHRkiSPx6MXX3xReXl5Ki0tlWVZbb9vamqSx+NRfHx8u+c1NTV1WwI+n091dXXtxlJSUnr5KHrP7Vlv15+zS93nBzCw9XoJSFJDQ4MWLVqkuXPn6vHHH9e6devafuf1ehUXF6eYmBh5vd5247Gxsd3u2+l09vsT538aSFk7MtDzA7ilsxd0vX530LVr17Rw4UItXbpUmZmZkqQJEyaourpaklRRUSGXy6XU1FRVVlbKtm3V19fLtm0uBQFAH+v1mcCWLVv05ZdfatOmTdq0aZMk6eWXX9aaNWtUVlam5ORkZWRkyOFwyOVyKSsrS7Zty+1293YUAEA3rEAgEAh3iJ6oq6vr8BLFg0vfCUOarp1aNz+o7bg7CECodXbu5MNiAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBglAAAGIwSAACDUQIAYDBKAAAMRgkAgMEoAQAwGCUAAAajBADAYJQAABiMEgAAg1ECAGAwSgAADBayEjh9+rRycnIkSRcvXtScOXM0d+5cFRYWyrZtSdLGjRuVmZmp7OxsnTlzJlRRAACdCEkJvPnmm3rllVfk8/kkScXFxcrLy9O7776rQCCg8vJy1dbW6uTJk9q7d6/Kysq0atWqUEQBAHQhJCUwatQobdiwoe1xbW2tJk+eLElKT0/XiRMndOrUKaWlpcmyLCUmJsrv96uxsTEUcQAAnYgMxU4zMjJ06dKltseBQECWZUmSoqOj1dTUJI/Ho/j4+LZtvh5PSEjoct8+n091dXXtxlJSUnoxfe+6Pevt+nN2qfv8AAa2kJTA7SIi/v+Ew+v1Ki4uTjExMfJ6ve3GY2Nju92X0+ns9yfO/zSQsnZkoOcHcEtnL+j65O6gCRMmqLq6WpJUUVEhl8ul1NRUVVZWyrZt1dfXy7btbmcBAIDe1Sczgfz8fK1cuVJlZWVKTk5WRkaGHA6HXC6XsrKyZNu23G53X0QBAPyHkJXAiBEjtGfPHklSUlKStm/ffsc2ubm5ys3NDVUEAEA3+LAYABiMEgAAg1ECAGAwSgAADEYJAIDBKAEAMBglAAAGowQAwGCUAAAYjBIAAINRAgBgMEoAAAxGCQCAwSgBADAYJQAABqMEAMBglAAAGIwSAACDUQIAYDBKAP8VX6sv3BE61Z+zAf1FyL5oHmZwRjr1Pxv+J9wxOlSVWxXuCEC/x0wAAAxGCQCAwSgBGM329c/3DYLN1driD3GSb6a/5sKdeE8ARotwOnU8fVq4Y9xhWsXxoLaLvMehjb/4PyFO03OLX3s83BEQJGYCAGCwsM8EbNtWUVGRzp07p6ioKK1Zs0ajR48OdywA6Jbd6ldEpCPcMe7Qk1xhL4HDhw+rublZu3fvVk1NjUpKSrR58+ZwxwLQB1qbmxUZFRXuGHcINldEpEN1rx7pg0Q9k/Ly/wp627CXwKlTpzR16lRJ0qRJk3T27NkwJwLQVyKjovTq/84Md4w7vLx9X7gj9BkrEAgEwhng5Zdf1vTp0zVt2q035x555BEdPnxYkZEd91NNTY2cTmdfRgSAAc/n82nSpEl3jId9JhATEyOv19v22LbtTgtAUocHAQD4ZsJ+d1BqaqoqKiok3XqVP378+DAnAgBzhP1y0Nd3B50/f16BQEBr167VmDFjwhkJAIwR9hIAAIRP2C8HAQDChxIAAINRAgBgMEpAt96cdrvdysrKUk5Oji5evBjuSN/I6dOnlZOTE+4YPdbS0qKlS5dq7ty5yszMVHl5ebgj9Yjf79eKFSuUnZ2tefPm6bPPPgt3pB67fv26pk2bpgsXLoQ7yjfy5JNPKicnRzk5OVqxYkW44wStpaVFv/jFL5Sdna25c+eG5b9/2D8n0B/cDUtXvPnmmzp48KAGDx4c7ig9dvDgQcXHx2vdunW6ceOGnnrqKf3gBz8Id6ygHT16VJK0a9cuVVdXq7i4eED9+2lpaZHb7dagQYPCHeUb8f2/Zbe3bdsW5iQ9d/z4cbW2tmrXrl2qqqrS+vXrtWHDhj7NwExAd8fSFaNGjerzfzy9ZcaMGfr5z3/e9tjh6H8LcnXlscce0y9/+UtJUn19vYYNGxbmRD1TWlqq7OxsDR8+PNxRvpGPPvpIN2/e1MKFCzV//nzV1NSEO1LQkpKS5Pf7Zdu2PB5Plx+UDRVmApI8Ho9iYmLaHjscDrW2toblL+SbysjI0KVLl8Id4xuJjo6WdOvv4cUXX1ReXl6YE/VcZGSk8vPz9cEHH+j1118Pd5ygHThwQAkJCZo6daq2bt0a7jjfyKBBg/Tss89q1qxZ+vTTT/Xcc8/pD3/4w4D4/3fIkCG6fPmyfvSjH+nGjRvasmVLn2dgJqCeL12B3tfQ0KD58+dr5syZevzxgfmFJKWlpTp06JBWrlypr776KtxxgrJ//36dOHFCOTk5qqurU35+vq5evRruWD2SlJSkJ554QpZlKSkpSfHx8QPmGN5++22lpaXp0KFDev/997V8+fK2y1t9hTOdbi1dcfToUf34xz9m6YowuHbtmhYuXCi3262HH3443HF67L333tOVK1f0s5/9TIMHD5ZlWQPmktaOHTvafs7JyVFRUZHuu+++MCbquX379un8+fMqKirSlStX5PF4BswxxMXF6Z577pEkDR06VK2trfL7+/arOSkBST/84Q9VVVWl7OzstqUr0He2bNmiL7/8Ups2bdKmTZsk3Xqje6C8UTl9+nStWLFC8+bNU2trqwoKCljptg9lZmZqxYoVmjNnjizL0tq1awfMTP7pp59WQUGB5s6dq5aWFr300ksaMmRIn2Zg2QgAMBjvCQCAwSgBADAYJQAABqMEAMBglAAAGGxg3EcFhMHWrVt14sQJRUREyLIsvfTSS3r//ff1zDPPaP/+/Ro2bJjmzJnT7jlnzpzR+vXrFQgEZNu2pk2bpoULF4bpCIDuUQJAB/7xj3/oyJEj2rlzpyzLavs07cGDB7t83urVq1VaWqoxY8aopaVF2dnZ+t73vqcJEyb0UXKgZ7gcBHQgISFB9fX12rdvn65cuaKUlBTt27dPOTk5bcv9Hj58WPPnz9fs2bN15swZSVJiYqJ27Nihs2fPKiIiQjt37tSECRN04MABLVq0SAsWLNATTzyhQ4cOhfPwgDaUANCBhIQEbd68WX/5y1+UlZWlGTNmtC0Z/bX7779f77zzjl599VUVFhZKktauXat7771XRUVF+v73v6/S0lI1NzdLkr766iv9+te/1ltvvaWSkhK1trb2+XEBt+NyENCBixcvKiYmRsXFxZKkv/3tb3r++efbLRP90EMPSZLGjRunq1evyufzqba2VosWLdKiRYt048YNFRQUaPfu3YqOjtZDDz2kiIgIDRs2THFxcWpsbBywyzfj7sFMAOjAuXPnVFRU1LaiY1JSkmJjY9stDPf1JaBz584pMTFRlmVp6dKlOn/+vCTpW9/6lu6//35FRUVJkmprayXdWjDP4/Ho3nvv7ctDAjrETADowPTp03XhwgXNmjVLQ4YMUSAQ0LJly/Sb3/ymbZtLly5p/vz5am5u1urVqxUVFaX169fL7XbL7/fLsiw98MAD+slPfqKDBw/q2rVrWrBggZqamlRYWDhgVhrF3Y0F5IA+cODAAf3zn//UkiVLwh0FaIfLQQBgMGYCAGAwZgIAYDBKAAAMRgkAgMEoAQAwGCUAAAb7vyaZC4tke9qxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1afc8550>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b2a5850>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ad50a50>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(bins=40, figsize=(10,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac72b10>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='Pclass', y='Age', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>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>13.00</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>30.00</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>23.45</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>30.00</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>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Pclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Pclass'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    \n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        \n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "        \n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        \n",
    "        else:\n",
    "            return 24\n",
    "        \n",
    "    else:\n",
    "        return Age\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age', 'Pclass']].apply(impute_age, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].isnull().nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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",
       "5     24.0\n",
       "6     54.0\n",
       "7      2.0\n",
       "8     27.0\n",
       "9     14.0\n",
       "10     4.0\n",
       "11    58.0\n",
       "12    20.0\n",
       "13    39.0\n",
       "14    14.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b441510>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train['Cabin'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b4412d0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bd61bd0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "886       0     1\n",
       "887       1     0\n",
       "888       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(train['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "embark = pd.get_dummies(train['Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Q', 'S', 'C'], dtype=object)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Embarked.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex', 'Embarked', 'Name', 'Ticket'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, sex, embark], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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",
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       "      <th>0</th>\n",
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       "      <td>7.2500</td>\n",
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       "      <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",
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       "      <td>8.0500</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   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"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('PassengerId', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age  SibSp  Parch   Fare  male  Q  S\n",
       "886         0       2  27.0      0      0  13.00     1  0  1\n",
       "887         1       1  19.0      0      0  30.00     0  0  1\n",
       "888         0       3  24.0      1      2  23.45     0  0  1\n",
       "889         1       1  26.0      0      0  30.00     1  0  0\n",
       "890         0       3  32.0      0      0   7.75     1  1  0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Survived', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.head()\n",
    "\n",
    "NewPclass = pd.get_dummies(train['Pclass'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('Pclass', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, NewPclass], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0         0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1         1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2         1  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3         1  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4         0  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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",
      "    accuracy                           0.81       267\n",
      "   macro avg       0.81      0.78      0.79       267\n",
      "weighted avg       0.81      0.81      0.80       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148,  15],\n",
       "       [ 36,  68]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "pygments_lexer": "ipython3",
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 },
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