titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# linear algebra\n",
    "import numpy as np \n",
    "\n",
    "# data processing\n",
    "import pandas as pd \n",
    "\n",
    "# data visualization\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "# Algorithms\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv(\"test.csv\")\n",
    "train_df = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "    <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",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>S</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total     %\n",
       "Cabin          687  77.1\n",
       "Age            177  19.9\n",
       "Embarked         2   0.2\n",
       "Fare             0   0.0\n",
       "Ticket           0   0.0\n",
       "Parch            0   0.0\n",
       "SibSp            0   0.0\n",
       "Sex              0   0.0\n",
       "Name             0   0.0\n",
       "Pclass           0   0.0\n",
       "Survived         0   0.0\n",
       "PassengerId      0   0.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_nulls_by_column = train_df.isnull().sum().sort_values(ascending=False)\n",
    "percent_of_nulls = train_df.isnull().sum()/train_df.isnull().count()*100\n",
    "percent_rounded = (round(percent_of_nulls, 1)).sort_values(ascending=False)\n",
    "missing_data = pd.concat([number_of_nulls_by_column, percent_rounded], axis=1, keys=['Total', '%'])\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = 'survived'\n",
    "not_survived = 'not survived'\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))\n",
    "women = train_df[train_df['Sex']=='female']\n",
    "men = train_df[train_df['Sex']=='male']\n",
    "ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False)\n",
    "ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[0], kde =False)\n",
    "ax.legend()\n",
    "ax.set_title('Female')\n",
    "ax = sns.distplot(men[men['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[1], kde = False)\n",
    "ax = sns.distplot(men[men['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[1], kde = False)\n",
    "ax.legend()\n",
    "_ = ax.set_title('Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1257b4ba8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 588.9x972 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FacetGrid = sns.FacetGrid(train_df, row='Embarked', height=4.5, aspect=1.6)\n",
    "FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None,  order=None, hue_order=None )\n",
    "FacetGrid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1244d2ba8>"
      ]
     },
     "execution_count": 68,
     "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.barplot(x='Pclass', y='Survived', data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    537\n",
       "0    354\n",
       "Name: not_alone, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [train_df, test_df]\n",
    "for dataset in data:\n",
    "    dataset['relatives'] = dataset['SibSp'] + dataset['Parch']\n",
    "    dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0\n",
    "    dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1\n",
    "    dataset['not_alone'] = dataset['not_alone'].astype(int)\n",
    "train_df['not_alone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sns.factorplot('relatives','Survived', \n",
    "                      data=train_df, aspect = 2.5, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "deck = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"U\": 8}\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].fillna(\"U0\")\n",
    "    dataset['Deck'] = dataset['Cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
    "    dataset['Deck'] = dataset['Deck'].map(deck)\n",
    "    dataset['Deck'] = dataset['Deck'].fillna(0)\n",
    "    dataset['Deck'] = dataset['Deck'].astype(int)\n",
    "# we can now drop the cabin feature\n",
    "train_df = train_df.drop(['Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    mean = train_df[\"Age\"].mean()\n",
    "    std = test_df[\"Age\"].std()\n",
    "    is_null = dataset[\"Age\"].isnull().sum()\n",
    "    # compute random numbers between the mean, std and is_null\n",
    "    rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n",
    "    # fill NaN values in Age column with random values generated\n",
    "    age_slice = dataset[\"Age\"].copy()\n",
    "    age_slice[np.isnan(age_slice)] = rand_age\n",
    "    dataset[\"Age\"] = age_slice\n",
    "    dataset[\"Age\"] = train_df[\"Age\"].astype(int)\n",
    "train_df[\"Age\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     889\n",
       "unique      3\n",
       "top         S\n",
       "freq      644\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Embarked'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_value = 'S'\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(common_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\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          891 non-null int64\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",
      "Embarked     891 non-null object\n",
      "relatives    891 non-null int64\n",
      "not_alone    891 non-null int64\n",
      "Deck         891 non-null int64\n",
      "dtypes: float64(1), int64(8), object(4)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Fare'] = dataset['Fare'].fillna(0)\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "\n",
    "for dataset in data:\n",
    "    # extract titles\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "    # replace titles with a more common title or as Rare\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\\\n",
    "                                            'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    # convert titles into numbers\n",
    "    dataset['Title'] = dataset['Title'].map(titles)\n",
    "    # filling NaN with 0, to get safe\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "train_df = train_df.drop(['Name'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "genders = {\"male\": 0, \"female\": 1}\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(genders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        891\n",
       "unique       681\n",
       "top       347082\n",
       "freq           7\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Ticket'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['Ticket'], axis=1)\n",
    "test_df = test_df.drop(['Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "ports = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    167\n",
       "4    162\n",
       "5    146\n",
       "3    145\n",
       "2    109\n",
       "1     94\n",
       "0     68\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [train_df, test_df]\n",
    "for dataset in data:\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "    dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n",
    "    dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n",
    "    dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n",
    "    dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n",
    "    dataset.loc[ dataset['Age'] > 66, 'Age'] = 6\n",
    "\n",
    "train_df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  relatives  \\\n",
       "0         0       3    0    2      1      0     7         0          1   \n",
       "1         1       1    1    5      1      0    71         1          1   \n",
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       "7         0       3    0    0      3      1    21         0          4   \n",
       "8         1       3    1    3      0      2    11         0          2   \n",
       "9         1       2    1    1      1      0    30         1          1   \n",
       "\n",
       "   not_alone  Deck  Title  \n",
       "0          0     8      1  \n",
       "1          0     3      3  \n",
       "2          1     8      2  \n",
       "3          0     3      3  \n",
       "4          1     8      1  \n",
       "5          1     8      1  \n",
       "6          1     5      1  \n",
       "7          0     8      4  \n",
       "8          0     8      3  \n",
       "9          0     8      3  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare'] <= 99), 'Fare']   = 3\n",
    "    dataset.loc[(dataset['Fare'] > 99) & (dataset['Fare'] <= 250), 'Fare']   = 4\n",
    "    dataset.loc[ dataset['Fare'] > 250, 'Fare'] = 5\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "for dataset in data:\n",
    "    dataset['Age_Class']= dataset['Age']* dataset['Pclass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  relatives  \\\n",
       "0         0       3    0    2      1      0     0         0          1   \n",
       "1         1       1    1    5      1      0     3         1          1   \n",
       "2         1       3    1    3      0      0     0         0          0   \n",
       "3         1       1    1    5      1      0     3         0          1   \n",
       "4         0       3    0    5      0      0     1         0          0   \n",
       "5         0       3    0    1      0      0     1         2          0   \n",
       "6         0       1    0    6      0      0     3         0          0   \n",
       "7         0       3    0    0      3      1     2         0          4   \n",
       "8         1       3    1    3      0      2     1         0          2   \n",
       "9         1       2    1    1      1      0     2         1          1   \n",
       "\n",
       "   not_alone  Deck  Title  Age_Class  Fare_Per_Person  \n",
       "0          0     8      1          6                0  \n",
       "1          0     3      3          5                1  \n",
       "2          1     8      2          9                0  \n",
       "3          0     3      3          5                1  \n",
       "4          1     8      1         15                1  \n",
       "5          1     8      1          3                1  \n",
       "6          1     5      1          6                3  \n",
       "7          0     8      4          0                0  \n",
       "8          0     8      3          9                0  \n",
       "9          0     8      3          2                1  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in data:\n",
    "    dataset['Fare_Per_Person'] = dataset['Fare']/(dataset['relatives']+1)\n",
    "    dataset['Fare_Per_Person'] = dataset['Fare_Per_Person'].astype(int)\n",
    "# Let's take a last look at the training set, before we start training the models.\n",
    "train_df.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielgene/jusbrasil/churn-detectors/venv/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "sgd = linear_model.SGDClassifier(max_iter=5, tol=None)\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "\n",
    "sgd.score(X_train, Y_train)\n",
    "\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielgene/jusbrasil/churn-detectors/venv/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = logreg.predict(X_test)\n",
    "\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielgene/jusbrasil/churn-detectors/venv/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:183: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If max_iter is set but tol is left unset, the default value for tol in 0.19 and 0.20 will be None (which is equivalent to -infinity, so it has no effect) but will change in 0.21 to 1e-3. Specify tol to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "perceptron = Perceptron(max_iter=5)\n",
    "perceptron.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielgene/jusbrasil/churn-detectors/venv/lib/python3.7/site-packages/sklearn/svm/base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_tree = DecisionTreeClassifier() \n",
    "decision_tree.fit(X_train, Y_train)  \n",
    "Y_pred = decision_tree.predict(X_test)  \n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "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>Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92.48</th>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92.48</th>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86.76</th>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81.82</th>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81.48</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.34</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.00</th>\n",
       "      <td>Naive Bayes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.55</th>\n",
       "      <td>Perceptron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model\n",
       "Score                            \n",
       "92.48               Random Forest\n",
       "92.48               Decision Tree\n",
       "86.76                         KNN\n",
       "81.82         Logistic Regression\n",
       "81.48     Support Vector Machines\n",
       "78.34  Stochastic Gradient Decent\n",
       "78.00                 Naive Bayes\n",
       "77.55                  Perceptron"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_linear_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_decision_tree]})\n",
    "result_df = results.sort_values(by='Score', ascending=False)\n",
    "result_df = result_df.set_index('Score')\n",
    "result_df.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.76666667 0.84444444 0.76404494 0.84269663 0.88764045 0.84269663\n",
      " 0.80898876 0.7752809  0.80898876 0.84090909]\n",
      "Mean: 0.8182357280671887\n",
      "Standard Deviation: 0.03850558939188987\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "scores = cross_val_score(rf, X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
    "print(\"Scores:\", scores)\n",
    "print(\"Mean:\", scores.mean())\n",
    "print(\"Standard Deviation:\", scores.std())"
   ]
  },
  {
   "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>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Class</th>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deck</th>\n",
       "      <td>0.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relatives</th>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_Per_Person</th>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_alone</th>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 importance\n",
       "feature                    \n",
       "Title                 0.209\n",
       "Sex                   0.159\n",
       "Age_Class             0.095\n",
       "Deck                  0.087\n",
       "Age                   0.074\n",
       "Pclass                0.073\n",
       "Fare                  0.070\n",
       "relatives             0.060\n",
       "Embarked              0.053\n",
       "Fare_Per_Person       0.043\n",
       "SibSp                 0.040\n",
       "Parch                 0.026\n",
       "not_alone             0.010"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame({'feature':X_train.columns,'importance':np.round(random_forest.feature_importances_,3)})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "importances.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1211b38d0>"
      ]
     },
     "execution_count": 105,
     "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": [
    "importances.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df  = train_df.drop(\"not_alone\", axis=1)\n",
    "test_df  = test_df.drop(\"not_alone\", axis=1)\n",
    "\n",
    "train_df  = train_df.drop(\"Parch\", axis=1)\n",
    "test_df  = test_df.drop(\"Parch\", axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92.48 %\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100, oob_score = True)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "print(round(acc_random_forest,2,), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oob score: 81.26 %\n"
     ]
    }
   ],
   "source": [
    "print(\"oob score:\", round(random_forest.oob_score_, 4)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gabrielgene/jusbrasil/churn-detectors/venv/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "param_grid = { \"criterion\" : [\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35], \"n_estimators\": [100, 400, 700, 1000, 1500]}\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "rf = RandomForestClassifier(n_estimators=100, max_features='auto', oob_score=True, random_state=1, n_jobs=-1)\n",
    "clf = GridSearchCV(estimator=rf, param_grid=param_grid, n_jobs=-1)\n",
    "clf.fit(X_train, Y_train)\n",
    "clf.bestparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestClassifier(criterion = \"gini\", \n",
    "                                       min_samples_leaf = 1, \n",
    "                                       min_samples_split = 10,   \n",
    "                                       n_estimators=100, \n",
    "                                       max_features='auto', \n",
    "                                       oob_score=True, \n",
    "                                       random_state=1, \n",
    "                                       n_jobs=-1)\n",
    "\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "\n",
    "print(\"oob score:\", round(random_forest.oob_score_, 4)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "predictions = cross_val_predict(random_forest, X_train, Y_train, cv=3)\n",
    "confusion_matrix(Y_train, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "print(\"Precision:\", precision_score(Y_train, predictions))\n",
    "print(\"Recall:\",recall_score(Y_train, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(Y_train, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# getting the probabilities of our predictions\n",
    "y_scores = random_forest.predict_proba(X_train)\n",
    "y_scores = y_scores[:,1]\n",
    "\n",
    "precision, recall, threshold = precision_recall_curve(Y_train, y_scores)\n",
    "def plot_precision_and_recall(precision, recall, threshold):\n",
    "    plt.plot(threshold, precision[:-1], \"r-\", label=\"precision\", linewidth=5)\n",
    "    plt.plot(threshold, recall[:-1], \"b\", label=\"recall\", linewidth=5)\n",
    "    plt.xlabel(\"threshold\", fontsize=19)\n",
    "    plt.legend(loc=\"upper right\", fontsize=19)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plot_precision_and_recall(precision, recall, threshold)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_precision_vs_recall(precision, recall):\n",
    "    plt.plot(recall, precision, \"g--\", linewidth=2.5)\n",
    "    plt.ylabel(\"recall\", fontsize=19)\n",
    "    plt.xlabel(\"precision\", fontsize=19)\n",
    "    plt.axis([0, 1.5, 0, 1.5])\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plot_precision_vs_recall(precision, recall)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "# compute true positive rate and false positive rate\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(Y_train, y_scores)\n",
    "# plotting them against each other\n",
    "def plot_roc_curve(false_positive_rate, true_positive_rate, label=None):\n",
    "    plt.plot(false_positive_rate, true_positive_rate, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'r', linewidth=4)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate (FPR)', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate (TPR)', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plot_roc_curve(false_positive_rate, true_positive_rate)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "r_a_score = roc_auc_score(Y_train, y_scores)\n",
    "print(\"ROC-AUC-Score:\", r_a_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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