Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading train.csv\n",
    "data = pd.read_csv('train.csv')\n",
    "# show the actaul data\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first few rows\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Age         0\n",
       "Sex         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "Ticket      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the null values \n",
    "null_values = x_train.isnull().sum()\n",
    "null_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(null_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_non_numerical_data(df):\n",
    "    \n",
    "    columns = df.columns.values\n",
    "\n",
    "    for column in columns:\n",
    "        text_digit_vals = {}\n",
    "        def convert_to_int(val):10\n",
    "            return text_digit_vals[val]\n",
    "\n",
    "        #print(column,df[column].dtype)\n",
    "        if df[column].dtype != np.int64 and df[column].dtype != np.float64:\n",
    "            \n",
    "            column_contents = df[column].values.tolist()\n",
    "            #finding just the uniques\n",
    "            unique_elements = set(column_contents)\n",
    "            # great, found them. \n",
    "            x = 0\n",
    "            for unique in unique_elements:\n",
    "                if unique not in text_digit_vals:\n",
    "                    text_digit_vals[unique] = x\n",
    "                    x+=1\n",
    "            df[column] = list(map(convert_to_int,df[column]))\n",
    "\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\had\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  Sex  SibSp  Parch     Fare  Embarked  Ticket\n",
       "0       3  22.0    1      1      0   7.2500         1     491\n",
       "1       1  38.0    0      1      0  71.2833         2     432\n",
       "2       3  26.0    0      0      0   7.9250         1     344\n",
       "3       1  35.0    0      1      0  53.1000         1     159\n",
       "4       3  35.0    1      0      0   8.0500         1     543"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_target = data['Survived']\n",
    "# Y_target.reshape(len(Y_target),1)\n",
    "x_train = data[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare','Embarked', 'Ticket']]\n",
    "\n",
    "x_train = handle_non_numerical_data(x_train)\n",
    "x_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\had\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>2</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>1</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>1</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>1</td>\n",
       "      <td>623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.045771</td>\n",
       "      <td>1</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>2</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015127</td>\n",
       "      <td>0</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass   Age  Sex  SibSp  Parch      Fare  Embarked  Ticket\n",
       "0         3  22.0    1      1      0  0.014151         1     491\n",
       "1         1  38.0    0      1      0  0.139136         2     432\n",
       "2         3  26.0    0      0      0  0.015469         1     344\n",
       "3         1  35.0    0      1      0  0.103644         1     159\n",
       "4         3  35.0    1      0      0  0.015713         1     543\n",
       "..      ...   ...  ...    ...    ...       ...       ...     ...\n",
       "886       2  27.0    1      0      0  0.025374         1     303\n",
       "887       1  19.0    0      0      0  0.058556         1     623\n",
       "888       3   NaN    0      1      2  0.045771         1     376\n",
       "889       1  26.0    1      0      0  0.058556         2     429\n",
       "890       3  32.0    1      0      0  0.015127         0     519\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare = pd.DataFrame(x_train['Fare'])\n",
    "# Normalizing\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "newfare = min_max_scaler.fit_transform(fare)\n",
    "x_train['Fare'] = newfare\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Age         0\n",
       "Sex         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "Ticket      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_values = x_train.isnull().sum()\n",
    "null_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(null_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NULL values 0\n",
      "   Pclass   Age  Sex  SibSp  Parch      Fare  Embarked  Ticket\n",
      "0       3  22.0    1      1      0  0.014151         1     491\n",
      "1       1  38.0    0      1      0  0.139136         2     432\n",
      "2       3  26.0    0      0      0  0.015469         1     344\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\had\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Fill the NAN values with the median values in the datasets\n",
    "x_train['Age'] = x_train['Age'].fillna(x_train['Age'].median())\n",
    "print(\"Number of NULL values\" , x_train['Age'].isnull().sum())\n",
    "print(x_train.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\had\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "c:\\users\\had\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
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       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row2_col7\" class=\"data row2 col7\" >0.0134262</td>\n",
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       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row3_col6\" class=\"data row3 col6\" >-0.0344958</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row3_col7\" class=\"data row3 col7\" >0.088173</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719level0_row4\" class=\"row_heading level0 row4\" >Parch</th>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col0\" class=\"data row4 col0\" >0.0184427</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col1\" class=\"data row4 col1\" >-0.172482</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col2\" class=\"data row4 col2\" >-0.245489</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col6\" class=\"data row4 col6\" >0.03135</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row4_col7\" class=\"data row4 col7\" >0.0670205</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719level0_row5\" class=\"row_heading level0 row5\" >Fare</th>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col0\" class=\"data row5 col0\" >-0.5495</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col1\" class=\"data row5 col1\" >0.0966884</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col2\" class=\"data row5 col2\" >-0.182333</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col6\" class=\"data row5 col6\" >0.273144</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row5_col7\" class=\"data row5 col7\" >0.0699173</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719level0_row6\" class=\"row_heading level0 row6\" >Embarked</th>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col0\" class=\"data row6 col0\" >-0.314684</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col1\" class=\"data row6 col1\" >0.0532067</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col2\" class=\"data row6 col2\" >-0.0338423</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col3\" class=\"data row6 col3\" >-0.0344958</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col4\" class=\"data row6 col4\" >0.03135</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col5\" class=\"data row6 col5\" >0.273144</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row6_col7\" class=\"data row6 col7\" >-0.0285876</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719level0_row7\" class=\"row_heading level0 row7\" >Ticket</th>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col0\" class=\"data row7 col0\" >-0.0319787</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col1\" class=\"data row7 col1\" >-0.0251609</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col2\" class=\"data row7 col2\" >0.0134262</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col3\" class=\"data row7 col3\" >0.088173</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col4\" class=\"data row7 col4\" >0.0670205</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col5\" class=\"data row7 col5\" >0.0699173</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col6\" class=\"data row7 col6\" >-0.0285876</td>\n",
       "                        <td id=\"T_64bda41e_fae9_11e9_aa48_f01faf54d719row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x65e3870>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train['Sex'] = x_train['Sex'].replace('male', 0)\n",
    "x_train['Sex'] = x_train['Sex'].replace('female', 1)\n",
    "# print(type(x_train))\n",
    "corr = x_train.corr()\n",
    "corr.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }</style><table id=\"T_65509964_fae9_11e9_8a65_f01faf54d719\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Pclass</th>        <th class=\"col_heading level0 col1\" >Age</th>        <th class=\"col_heading level0 col2\" >Sex</th>        <th class=\"col_heading level0 col3\" >SibSp</th>        <th class=\"col_heading level0 col4\" >Parch</th>        <th class=\"col_heading level0 col5\" >Fare</th>        <th class=\"col_heading level0 col6\" >Embarked</th>        <th class=\"col_heading level0 col7\" >Ticket</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row0\" class=\"row_heading level0 row0\" >Pclass</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col1\" class=\"data row0 col1\" >-0.339898</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col2\" class=\"data row0 col2\" >0.1319</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col3\" class=\"data row0 col3\" >0.0830814</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col4\" class=\"data row0 col4\" >0.0184427</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col5\" class=\"data row0 col5\" >-0.5495</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col6\" class=\"data row0 col6\" >-0.314684</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row0_col7\" class=\"data row0 col7\" >-0.0319787</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col0\" class=\"data row1 col0\" >-0.339898</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col2\" class=\"data row1 col2\" >0.0811625</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col3\" class=\"data row1 col3\" >-0.233296</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col4\" class=\"data row1 col4\" >-0.172482</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col5\" class=\"data row1 col5\" >0.0966884</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col6\" class=\"data row1 col6\" >0.0532067</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row1_col7\" class=\"data row1 col7\" >-0.0251609</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row2\" class=\"row_heading level0 row2\" >Sex</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col0\" class=\"data row2 col0\" >0.1319</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col1\" class=\"data row2 col1\" >0.0811625</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col3\" class=\"data row2 col3\" >-0.114631</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col4\" class=\"data row2 col4\" >-0.245489</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col5\" class=\"data row2 col5\" >-0.182333</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col6\" class=\"data row2 col6\" >-0.0338423</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row2_col7\" class=\"data row2 col7\" >0.0134262</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row3\" class=\"row_heading level0 row3\" >SibSp</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col0\" class=\"data row3 col0\" >0.0830814</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col1\" class=\"data row3 col1\" >-0.233296</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col2\" class=\"data row3 col2\" >-0.114631</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col6\" class=\"data row3 col6\" >-0.0344958</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row3_col7\" class=\"data row3 col7\" >0.088173</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row4\" class=\"row_heading level0 row4\" >Parch</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col0\" class=\"data row4 col0\" >0.0184427</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col1\" class=\"data row4 col1\" >-0.172482</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col2\" class=\"data row4 col2\" >-0.245489</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col6\" class=\"data row4 col6\" >0.03135</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row4_col7\" class=\"data row4 col7\" >0.0670205</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row5\" class=\"row_heading level0 row5\" >Fare</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col0\" class=\"data row5 col0\" >-0.5495</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col1\" class=\"data row5 col1\" >0.0966884</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col2\" class=\"data row5 col2\" >-0.182333</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col6\" class=\"data row5 col6\" >0.273144</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row5_col7\" class=\"data row5 col7\" >0.0699173</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row6\" class=\"row_heading level0 row6\" >Embarked</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col0\" class=\"data row6 col0\" >-0.314684</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col1\" class=\"data row6 col1\" >0.0532067</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col2\" class=\"data row6 col2\" >-0.0338423</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col3\" class=\"data row6 col3\" >-0.0344958</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col4\" class=\"data row6 col4\" >0.03135</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col5\" class=\"data row6 col5\" >0.273144</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row6_col7\" class=\"data row6 col7\" >-0.0285876</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_65509964_fae9_11e9_8a65_f01faf54d719level0_row7\" class=\"row_heading level0 row7\" >Ticket</th>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col0\" class=\"data row7 col0\" >-0.0319787</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col1\" class=\"data row7 col1\" >-0.0251609</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col2\" class=\"data row7 col2\" >0.0134262</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col3\" class=\"data row7 col3\" >0.088173</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col4\" class=\"data row7 col4\" >0.0670205</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col5\" class=\"data row7 col5\" >0.0699173</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col6\" class=\"data row7 col6\" >-0.0285876</td>\n",
       "                        <td id=\"T_65509964_fae9_11e9_8a65_f01faf54d719row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c84fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_corr(df,size=10):\n",
    "\n",
    "\n",
    "    corr = df.corr()\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    ax.matshow(corr)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns);\n",
    "# plot_corr(x_train)\n",
    "x_train.corr()\n",
    "corr.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing the data into train and test data set\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x_train, y_target, test_size = 0.4, random_state = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\had\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0\n",
      " 0 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 1 0 0 1 1 0 1 1 1 0 0\n",
      " 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0\n",
      " 0 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0\n",
      " 1 1 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 1\n",
      " 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1\n",
      " 1 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1\n",
      " 1 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0\n",
      " 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 0 1 1 0 1 0 1\n",
      " 0 0 0 1 0 0 1 0 1 1 1 0 0 1 0 0 1 1 1 0 1 0 1 0]\n",
      "Accuracy:  0.8179271708683473\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(X_test))\n",
    "print(\"Accuracy: \",clf.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
       "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
       "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "2          894       2         Myles, Mr. Thomas Francis    male  62.0      0   \n",
       "\n",
       "   Parch  Ticket    Fare Cabin Embarked  \n",
       "0      0  330911  7.8292   NaN        Q  \n",
       "1      0  363272  7.0000   NaN        S  \n",
       "2      0  240276  9.6875   NaN        Q  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Testing the model.\n",
    "test_data = pd.read_csv('test.csv')\n",
    "test_data.head(3)\n",
    "# test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass   Age  Sex  SibSp  Parch      Fare  Ticket  Embarked\n",
      "0       3  34.5    1      0      0  0.015282     358         0\n",
      "1       3  47.0    0      1      0  0.013663     122         1\n",
      "2       2  62.0    1      0      0  0.018909     317         0\n",
      "3       3  27.0    1      0      0  0.016908     191         1\n",
      "4       3  22.0    0      1      1  0.023984     224         1\n"
     ]
    }
   ],
   "source": [
    "### Preprocessing on the test data\n",
    "test_data = test_data[['Pclass', 'Age', 'Sex', 'SibSp', 'Parch', 'Fare', 'Ticket', 'Embarked']]\n",
    "test_data = handle_non_numerical_data(test_data)\n",
    "\n",
    "fare = pd.DataFrame(test_data['Fare'])\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "newfare = min_max_scaler.fit_transform(fare)\n",
    "test_data['Fare'] = newfare\n",
    "test_data['Fare'] = test_data['Fare'].fillna(test_data['Fare'].median())\n",
    "test_data['Age'] = test_data['Age'].fillna(test_data['Age'].median())\n",
    "test_data['Sex'] = test_data['Sex'].replace('male', 0)\n",
    "test_data['Sex'] = test_data['Sex'].replace('female', 1)\n",
    "print(test_data.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0\n",
      " 0 0 1 0 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1\n",
      " 1 1 0 1 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0\n",
      " 1 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1\n",
      " 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0\n",
      " 1 0 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1\n",
      " 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0\n",
      " 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0\n",
      " 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0 1 0 0 0 1 0\n",
      " 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0\n",
      " 1 0 0 0 1 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.8255813953488372\n",
      "Recall: 0.6859903381642513\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "predictions = cross_val_predict(clf, X_train, Y_train, cv=3)\n",
    "print(\"Precision:\", precision_score(Y_train, predictions))\n",
    "print(\"Recall:\",recall_score(Y_train, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# getting the probabilities of our predictions\n",
    "y_scores = clf.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.axis([0.3,0.8,0.8,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jTacnOSzJHyU5M8kN431nJHlckj9N8s2MRqKm1Vo7L6NlxCfue/mE199I8vhZ1AkAAHC3zeaephOSfL619prW2o1bdrbWrmutfTjJ0RmFp/93B9UIAAAwmNmEpv2TfHnC9h0Z3WeUZBSeknwkyTPuXmkAAADDm01ouinJrhO2f5BkzaQ2t2S0QAQAAMCCNpvQ9K0kD5qwfVGSY6vqwCSpqv2S/GaS79zt6gAAAAY2m9B0XpJjqmqf8fafJ9k3yder6stJNmT0vKW37ZgSAQAAhjOb0PSWjBZ7uCNJWmv/J6P7l65O8rCMnqH0ktban++oIgEAAIay3UuOt9Z+mORLk/adneTsHVUUAADAfDGbkSYAAIAlY7tDU1U9rao+XVVTro5XVQ8YH/8Pd788AACAYc1mpOm/JNmrtXbtVAdba9ckuWeS3787hQEAAMwHswlNhyZZP02bi8ftAAAAFrTZhKZ9ktw4TZubk9x7FtcGAACYV2YTmjYmOWyaNo9K8r1ZXBsAAGBemU1o+liSJ1fVb051cLwAxK8m+ejdKQwAAGA+2O7nNCV5dZL/mOQDVfWRJJ9Mck2SB2QUln49yfeT/OmOKhIAAGAos3m47TVVdXSSv0ryG0n+3fhQjb9enOQZ41X0AAAAFrTZjDSltXZpkkdW1WOSrM1ocYibklzYWrtwB9YHAAAwqFmFpi1aa19K8qUdVAsAAMC8M+vQVFW7J3lSkn+d5F6ttVeN9++RZO8k17fW7tghVQIAAAxkNqvnpapOTHJ1knVJXp/k9AmHH5bk2iQn3d3iAAAAhrbdoamq/m2Sc5L8KMnzkpw98fj4nqbLk5y4IwoEAAAY0mym5/1xkuuSrG2t3VhVK6doc1GSx92tygAAAOaB2UzPW5vkb1trN3babExy/9mVBAAAMH/MJjTtkuTWadqsnEEbAACAeW82oemyJEdu62BV7Zrk6CRfnW1RAAAA88VsQtO7kzyiql5fVVudP15u/M1J1iR5590vDwAAYFizWQjijCTHJHlRkmdmtIpequrTSR6eZL8k57TW3rujigQAABjKdo80tdbuaK39hyTPzWgVvYOSVEZT8jYlObW19ts7tEoAAICBbPdIU1VdnORTrbU/THJGVd0jyb2T/LC1dsuOLhAAAGBIs7mn6cFJbt+y0Vr7aWvtGoEJAABYjGYTmi5O8pAdXQgAAMB8NJvQ9CdJjquqp+3oYgAAAOab2aye98Qkn0vyvqp6cZL1GS0I0Sa1a621V93N+gAAAAY1m9B0+oTXjx7/mUpLIjQBAAAL2mxC09E7vAoAAIB5artDU2vtszujEAAAgPloNgtBAAAALBlCEwAAQIfQBAAA0CE0AQAAdAhNAAAAHUITAABAx+ChqaqOrarLq2pDVZ22jTZPq6pvVNWlVXX2XNcIAAAsXbN5uO0OU1XLkrw5yZOTbExyYVWta619Y0Kbg5P8UZLHt9ZurKr7DlMtAACwFA090nR4kg2ttStaa7clOSfJCZPa/Ockb26t3ZgkrbXr5rhGAABgCRs6NO2f5OoJ2xvH+yb6pSS/VFX/WFUXVNWxc1YdAACw5A06PS9JTbGvTdpenuTgJEclWZXk81V1aGvtpq0uVHVKklOS5IADDtjxlQIAAEvS0CNNG5OsnrC9Ksk1U7T529baz1trVya5PKMQtZXW2pmttbWttbUrV67caQUDAABLy9Ch6cIkB1fVQVW1W5KnJ1k3qc2HkxydJFW1IqPpelfMaZUAAMCSNWhoaq1tTnJqko8nuSzJua21S6vqlVV1/LjZx5PcUFXfSHJ+kpe01m4YpmIAAGCpqdYm30K08K1du7atX79+6DIAAIB5qqouaq2tnUnboafnAQAAzGtCEwAAQIfQBAAA0CE0AQAAdAhNAAAAHUITAABAh9AEAADQITQBAAB0CE0AAAAdQhMAAECH0AQAANAhNAEAAHQITQAAAB1CEwAAQIfQBAAA0CE0AQAAdAhNAAAAHUITAABAh9AEAADQITQBAAB0CE0AAAAdQhMAAECH0AQAANAhNAEAAHQITQAAAB1CEwAAQIfQBAAA0CE0AQAAdAhNAAAAHUITAABAh9AEAADQITQBAAB0CE0AAAAdQhMAAECH0AQAANAhNAEAAHQITQAAAB1CEwAAQIfQBAAA0CE0AQAAdAhNAAAAHUITAABAx+ChqaqOrarLq2pDVZ3WaXdiVbWqWjuX9QEAAEvboKGpqpYleXOS45IckuSkqjpkinZ7JXl+ki/NbYUAAMBSN/RI0+FJNrTWrmit3ZbknCQnTNHuVUlem+Rnc1kcAADA0KFp/yRXT9jeON53p6p6ZJLVrbWP9C5UVadU1fqqWr9p06YdXykAALAkDR2aaop97c6DVbskeUOSF093odbama21ta21tStXrtyBJQIAAEvZ0KFpY5LVE7ZXJblmwvZeSQ5N8pmq+k6SI5KssxgEAAAwV4YOTRcmObiqDqqq3ZI8Pcm6LQdbaze31la01ta01tYkuSDJ8a219cOUCwAALDWDhqbW2uYkpyb5eJLLkpzbWru0ql5ZVccPWRsAAECSLB+6gNbaeUnOm7Tv5dtoe9Rc1AQAALDF0NPzAAAA5jWhCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAAIAOoQkAAKBDaAIAAOgQmgAAADqEJgAAgA6hCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAAIAOoQkAAKBDaAIAAOgQmgAAADqEJgAAgA6hCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAAIAOoQkAAKBDaAIAAOgQmgAAADqEJgAAgA6hCQAAoENoAgAA6BCaAAAAOoQmAACAjsFDU1UdW1WXV9WGqjptiuMvqqpvVNXXqurTVXXgEHUCAABL06ChqaqWJXlzkuOSHJLkpKo6ZFKzS5Ksba09PMkHk7x2bqsEAACWsqFHmg5PsqG1dkVr7bYk5yQ5YWKD1tr5rbWfjDcvSLJqjmsEAACWsKFD0/5Jrp6wvXG8b1tOTvKxnVoRAADABMsHfv+aYl+bsmHVM5KsTXLkNo6fkuSUJDnggAN2VH0AAMASN/RI08Ykqydsr0pyzeRGVfWkJC9Lcnxr7dapLtRaO7O1tra1tnblypU7pVgAAGDpGTo0XZjk4Ko6qKp2S/L0JOsmNqiqRyZ5W0aB6boBagQAAJawQUNTa21zklOTfDzJZUnOba1dWlWvrKrjx81el2TPJB+oqq9U1bptXA4AAGCHG/qeprTWzkty3qR9L5/w+klzXhQAAMDY0NPzAAAA5jWhCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAAIAOoQkAAKBDaAIAAOgQmgAAADqEJgAAgA6hCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAAIAOoQkAAKBDaAIAAOgQmgAAADqEJgAAgA6hCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAAIAOoQkAAKBDaAIAAOgQmgAAADqEJgAAgA6hCQAAoENoAgAA6BCaAAAAOoQmAACADqEJAACgY/DQVFXHVtXlVbWhqk6b4vjuVfX+8fEvVdWaua8SAABYqgYNTVW1LMmbkxyX5JAkJ1XVIZOanZzkxtbag5O8Iclr5rZKAABgKRt6pOnwJBtaa1e01m5Lck6SEya1OSHJX45ffzDJMVVVc1gjAACwhA0dmvZPcvWE7Y3jfVO2aa1tTnJzkv3mpDoAAGDJWz7w+081YtRm0SZVdUqSU8abt1bV1+9mbew8K5JcP3QRdOmj+U3/zG/6Z37TP/Ob/pnfFlv/HDjThkOHpo1JVk/YXpXkmm202VhVy5Psk+QHky/UWjszyZlJUlXrW2trd0rF3G36Z/7TR/Ob/pnf9M/8pn/mN/0zvy3l/hl6et6FSQ6uqoOqarckT0+yblKbdUmePX59YpK/b63dZaQJAABgZxh0pKm1trmqTk3y8STLkryrtXZpVb0yyfrW2rok70zy3qrakNEI09OHqxgAAFhqhp6el9baeUnOm7Tv5RNe/yzJb23nZc/cAaWx8+if+U8fzW/6Z37TP/Ob/pnf9M/8tmT7p8x0AwAA2Lah72kCAACY1xZ0aKqqY6vq8qraUFWnTXF896p6//j4l6pqzdxXuXTNoH9eVFXfqKqvVdWnq2rGyz5y903XPxPanVhVraqW5Go5Q5lJ/1TV08b/hi6tqrPnusalbgbf4w6oqvOr6pLx97mnDFHnUlRV76qq67b1+JEa+Ytx332tqh411zUuZTPon98Z98vXquoLVfWIua5xqZuujya0e3RV3V5VJ85VbUNZsKGpqpYleXOS45IckuSkqjpkUrOTk9zYWntwkjckec3cVrl0zbB/LkmytrX28CQfTPLaua1y6Zph/6Sq9kry/CRfmtsKl7aZ9E9VHZzkj5I8vrX20CQvmPNCl7AZ/hv64yTnttYemdEiRm+Z2yqXtLOSHNs5flySg8d/Tkny1jmoiV84K/3+uTLJkeOfD16VJXwfzYDOSr+PtnwffE1GC7otegs2NCU5PMmG1toVrbXbkpyT5IRJbU5I8pfj1x9MckxVTfWwXHa8afuntXZ+a+0n480LMnpOF3NjJv9+ktF/Vq9N8rO5LI4Z9c9/TvLm1tqNSdJau26Oa1zqZtJHLcne49f75K7PIWQnaa19LlM803GCE5K8p41ckGTfqrr/3FTHdP3TWvvClu9t8fPBIGbwbyhJnpfkQ0mWxP8/Czk07Z/k6gnbG8f7pmzTWtuc5OYk+81JdcykfyY6OcnHdmpFTDRt/1TVI5Osbq19ZC4LI8nM/v38UpJfqqp/rKoLqqr7G0F2uJn00elJnlFVGzNaJfZ5c1MaM7C9/0cxHD8fzENVtX+SpyY5Y+ha5srgS47fDVONGE1eCnAmbdg5Zvx3X1XPSLI2yZE7tSIm6vZPVe2S0ZTW58xVQWxlJv9+lmc0teiojH4L+/mqOrS1dtNOro2RmfTRSUnOaq39z6p6bEbPHDy0tXbHzi+Pafj5YAGoqqMzCk1PGLoW7uJ/JXlpa+32pTKJayGHpo1JVk/YXpW7Tn3Y0mZjVS3PaHrEdEON7Bgz6Z9U1ZOSvCyjucu3zlFtTN8/eyU5NMlnxt8M75dkXVUd31pbP2dVLl0z/f52QWvt50murKrLMwpRF85NiUveTPro5IzvCWitfbGq9kiyIktkKss8N6P/oxhOVT08yTuSHNdau2HoeriLtUnOGf+MsCLJU6pqc2vtw8OWtfMs5Ol5FyY5uKoOqqrdMrrJdt2kNuuSPHv8+sQkf988mGquTNs/4+lfb0tyvPsx5ly3f1prN7fWVrTW1rTW1mQ0p1xgmjsz+f724SRHJ0lVrchout4Vc1rl0jaTProqyTFJUlUPSbJHkk1zWiXbsi7Js8ar6B2R5ObW2rVDF8VIVR2Q5H8neWZr7VtD18NdtdYOmvAzwgeTPHcxB6ZkAY80tdY2V9WpGa3YsSzJu1prl1bVK5Osb62tS/LOjKZDbMhohOnpw1W8tMywf16XZM8kHxj/puKq1trxgxW9hMywfxjIDPvn40l+taq+keT2JC/x29i5M8M+enGSt1fVCzOa+vUcv7ibG1X1voymrq4Y31P2iiS7Jklr7YyM7jF7SpINSX6S5HeHqXRpmkH/vDyje9DfMv75YHNrzWMv5tAM+mjJKd+/AQAAtm0hT88DAADY6YQmAACADqEJAACgQ2gCAADoEJoAAAA6hCYAFr2qOqqqWlWdPotz14zPPWvHVwbAQiA0AQAAdHhOEwCLXlXdM8kBSa5vrV2/nefumuRBSW5urV27M+oDYH4TmgAAADpMzwPgbpl4v1BVPamqvlBVP6mq71fVW6tq3wlt77w/qKoeUVUfq6qbqurGSdc8qao+X1U/rKofV9WXqupp23j/e1fVn1XVZVX106q6YVzDc6eqcdK5h1fV31bVv1TVrVX1var6bFWdNFXNU7z3MVX5RIoeAAAE3ElEQVT1yfFn+GlVfaWqnldVu0xqN/Hv6IiqOr+qbqmqH1TVX1fVyu39ewdg7ghNAOwoj0vy0SQbk/x5kn9O8vtJPllVu01qe3CSf0iyW5Izk/zNlgNV9YYkZye5f5K/TvKuJCuSvL+qXjzxIlV1/yQXJvmjJDcmeWOS9yXZnOS/94qtqsPGNTwxyceT/M8kH0myd5LfnO7DVtXvJPlEkkcnOTfJm5LskeQvkrx7G6cdnuT8JD9KckaSy5P8dpJ1VVXTvScAw1g+dAEALBpPTvKs1tp7t+yoqncm+b0k/zWjILXF45L8P621V0+8QFUdl+QFST6Q5BmttdvG+1+a5NNJXl1V57TW/mV8ylszut/oJa2110+61v7T1Ps7SXZNcnRr7auTzt2vd2JV7TN+71uSrG2tbRjv/+Mkn0ryrKo6t7X20UmnHpfkxNbah8btd0nyyST/NskRSb44Tc0ADMBIEwA7yjeT/NWkfa9IcnuSZ0zaf22S1+eunpvkjiT/dUtgSpLW2k+S/H8ZhZzfTO4cZTo+yT8l+f8nX2hCsJrOj6c494Zpzvn3SfZKcsaWwDQ+79YkLxtvPnuK8z6zJTCN29+R5D3jzbUzrBeAOWakCYAd5R/bpNWFWmsbq+q7SR4+qe1XW2s/n+Iahye5OcnzppittuW+n389/npYkkryqXH42F4fSPLfknypqs7OaITo8621H8zg3EeMv352imP/mNH0wEdMceySKfZtCXf7TnEMgHlAaAJgR9m0jf3XJXlgVe0+ad9U7pPR/02v6LzPvcZf9xl/vWbGFU7QWvtiVT0po5Gh/5Lk1CR3VNUnkrygtXZ55/S9x1+/P8V1b6+qGya0meiHU+zbPP66bMbFAzCnhCYAdpRtrQB33yS3tdZunTB6tK3nXfwwyQ9bawfN4P1uGn99wMxL3Fpr7fwk51fVnkmekOS3kvxuko9W1SETpwhOUWeS/KvJB6pqWZL9klwx27oAmF/c0wTAjvK4ySvAVdWqjB4q+7UZXuPLSQ4c3680nYsyCl/HTF7ie3u11m5prf2f1trJGa2E96AkD+mcsmXhiF+Z4thjM/ql5FfuTk0AzB9CEwA7ykNy1wUf/iSjAPHXM7zGmzK6T+kdVbXX5INVdUhV3TdJWmvfy2ip8ocnedEUbbur51XVEya/xzj0bRkxu7Vz+oczWjb896vqgRPO3y3Jn4433zPViQAsPKbnAbCjfDKjsPMbSTZkNArz+CTrk7xlJhdorX20ql6X5CVJ/nl8f9E1Se6X5GFJHpXRSM6We6Kem1Foel1VPTWj5y7dI8mhSdYkeWC27b9nNEr19xlNpbs9yZEZrWL3sdbaNzt13jx+eO57klxUVe/PaMrebyT55STvnWK5cQAWKKEJgB3lC0lem+RVSf5dRkt5vy3JaZ17g+6itfaHVfX5JH+Q5NeT7JnRggvfzCgk/dOEtt+vqsOTnJbRUuQvyGgE6PJxLT1vzSjoHJHkmIwWZLgyo1Grt86gzr+qqmszerDuSUl2T/KtjFbke9MMPy4AC0BNWh0WALZLVR2V5Pwkf9JaO33YagBgx3NPEwAAQIfQBAAA0CE0AQAAdLinCQAAoMNIEwAAQIfQBAAA0CE0AQAAdAhNAAAAHUITAABAh9AEAADQ8X8BcGx/Iq51N6kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[293,  34],\n",
       "       [ 64, 143]], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "predictions = cross_val_predict(clf, X_train, Y_train, cv=3)\n",
    "confusion_matrix(Y_train, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "True positive: 293 (We predicted a positive result and it was positive)\n",
    "True negative: 143 (We predicted a negative result and it was negative)\n",
    "False positive: 34 (We predicted a positive result and it was negative)\n",
    "False negative: 64 (We predicted a negative result and it was positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}