Titanic (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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",
       "    <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",
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       "    </tr>\n",
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       "      <td>4</td>\n",
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       "      <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",
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       "    </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": 6,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <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",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "    <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",
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       "      <td>E46</td>\n",
       "      <td>S</td>\n",
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       "    <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",
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       "      <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",
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       "    <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first few rows\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the null values \n",
    "null_values = data.isnull().sum()\n",
    "null_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 12,
   "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):\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\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>3</td>\n",
       "      <td>315</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>68</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>3</td>\n",
       "      <td>620</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>3</td>\n",
       "      <td>669</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>3</td>\n",
       "      <td>30</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         3     315\n",
       "1       1  38.0    0      1      0  71.2833         2      68\n",
       "2       3  26.0    0      0      0   7.9250         3     620\n",
       "3       1  35.0    0      1      0  53.1000         3     669\n",
       "4       3  35.0    1      0      0   8.0500         3      30"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\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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       "      <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",
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       "    <tr>\n",
       "      <td>...</td>\n",
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       "    </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         3     315\n",
       "1         1  38.0    0      1      0  0.139136         2      68\n",
       "2         3  26.0    0      0      0  0.015469         3     620\n",
       "3         1  35.0    0      1      0  0.103644         3     669\n",
       "4         3  35.0    1      0      0  0.015713         3      30\n",
       "..      ...   ...  ...    ...    ...       ...       ...     ...\n",
       "886       2  27.0    1      0      0  0.025374         3      67\n",
       "887       1  19.0    0      0      0  0.058556         3     420\n",
       "888       3   NaN    0      1      2  0.045771         3     558\n",
       "889       1  26.0    1      0      0  0.058556         2     399\n",
       "890       3  32.0    1      0      0  0.015127         1     544\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Age         177\n",
       "Sex           0\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      0\n",
       "Ticket        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_values = x_train.isnull().sum()\n",
    "null_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "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": 17,
   "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         3     315\n",
      "1       1  38.0    0      1      0  0.139136         2      68\n",
      "2       3  26.0    0      0      0  0.015469         3     620\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\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'].mean())\n",
    "print(\"Number of NULL values\" , x_train['Age'].isnull().sum())\n",
    "print(x_train.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\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\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\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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       "            background-color:  #fff7fb;\n",
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       "            background-color:  #76aad0;\n",
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       "            background-color:  #e8e4f0;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f8f1f8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f8f1f8;\n",
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       "            background-color:  #b9c6e0;\n",
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       "            color:  #000000;\n",
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       "            background-color:  #fff7fb;\n",
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       "            background-color:  #d4d4e8;\n",
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       "            background-color:  #c0c9e2;\n",
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       "            background-color:  #d2d3e7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #cccfe5;\n",
       "            color:  #000000;\n",
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       "            background-color:  #bcc7e1;\n",
       "            color:  #000000;\n",
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       "            background-color:  #023858;\n",
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       "            background-color:  #f2ecf5;\n",
       "            color:  #000000;\n",
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       "            background-color:  #97b7d7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #d5d5e8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #d5d5e8;\n",
       "            color:  #000000;\n",
       "        }    #T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col3 {\n",
       "            background-color:  #d9d8ea;\n",
       "            color:  #000000;\n",
       "        }    #T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col4 {\n",
       "            background-color:  #e0deed;\n",
       "            color:  #000000;\n",
       "        }    #T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col5 {\n",
       "            background-color:  #bfc9e1;\n",
       "            color:  #000000;\n",
       "        }    #T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col6 {\n",
       "            background-color:  #f4edf6;\n",
       "            color:  #000000;\n",
       "        }    #T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col7 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7\" ><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_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row0\" class=\"row_heading level0 row0\" >Pclass</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col1\" class=\"data row0 col1\" >-0.331339</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col2\" class=\"data row0 col2\" >0.1319</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col3\" class=\"data row0 col3\" >0.0830814</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col4\" class=\"data row0 col4\" >0.0184427</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col5\" class=\"data row0 col5\" >-0.5495</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col6\" class=\"data row0 col6\" >-0.028566</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row0_col7\" class=\"data row0 col7\" >0.0893423</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col0\" class=\"data row1 col0\" >-0.331339</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col2\" class=\"data row1 col2\" >0.0841534</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col3\" class=\"data row1 col3\" >-0.232625</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col4\" class=\"data row1 col4\" >-0.179191</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col5\" class=\"data row1 col5\" >0.0915661</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col6\" class=\"data row1 col6\" >-0.0235777</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row1_col7\" class=\"data row1 col7\" >-0.0260613</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row2\" class=\"row_heading level0 row2\" >Sex</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col0\" class=\"data row2 col0\" >0.1319</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col1\" class=\"data row2 col1\" >0.0841534</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col3\" class=\"data row2 col3\" >-0.114631</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col4\" class=\"data row2 col4\" >-0.245489</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col5\" class=\"data row2 col5\" >-0.182333</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col6\" class=\"data row2 col6\" >0.128526</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row2_col7\" class=\"data row2 col7\" >0.0404013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row3\" class=\"row_heading level0 row3\" >SibSp</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col0\" class=\"data row3 col0\" >0.0830814</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col1\" class=\"data row3 col1\" >-0.232625</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col2\" class=\"data row3 col2\" >-0.114631</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col6\" class=\"data row3 col6\" >0.0637943</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row3_col7\" class=\"data row3 col7\" >0.023189</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row4\" class=\"row_heading level0 row4\" >Parch</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col0\" class=\"data row4 col0\" >0.0184427</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col1\" class=\"data row4 col1\" >-0.179191</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col2\" class=\"data row4 col2\" >-0.245489</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col6\" class=\"data row4 col6\" >0.082144</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row4_col7\" class=\"data row4 col7\" >-0.0250445</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row5\" class=\"row_heading level0 row5\" >Fare</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col0\" class=\"data row5 col0\" >-0.5495</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col1\" class=\"data row5 col1\" >0.0915661</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col2\" class=\"data row5 col2\" >-0.182333</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col6\" class=\"data row5 col6\" >-0.0710096</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row5_col7\" class=\"data row5 col7\" >-0.0776145</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row6\" class=\"row_heading level0 row6\" >Embarked</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col0\" class=\"data row6 col0\" >-0.028566</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col1\" class=\"data row6 col1\" >-0.0235777</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col2\" class=\"data row6 col2\" >0.128526</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col3\" class=\"data row6 col3\" >0.0637943</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col4\" class=\"data row6 col4\" >0.082144</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col5\" class=\"data row6 col5\" >-0.0710096</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row6_col7\" class=\"data row6 col7\" >0.0118884</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7level0_row7\" class=\"row_heading level0 row7\" >Ticket</th>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col0\" class=\"data row7 col0\" >0.0893423</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col1\" class=\"data row7 col1\" >-0.0260613</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col2\" class=\"data row7 col2\" >0.0404013</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col3\" class=\"data row7 col3\" >0.023189</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col4\" class=\"data row7 col4\" >-0.0250445</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col5\" class=\"data row7 col5\" >-0.0776145</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col6\" class=\"data row7 col6\" >0.0118884</td>\n",
       "                        <td id=\"T_013cf7ac_006d_11ea_bd45_5c260a6e23e7row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f17e53fe80>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #f2ecf5;\n",
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       "            background-color:  #97b7d7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #d5d5e8;\n",
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       "            background-color:  #d5d5e8;\n",
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       "            background-color:  #d9d8ea;\n",
       "            color:  #000000;\n",
       "        }    #T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col4 {\n",
       "            background-color:  #e0deed;\n",
       "            color:  #000000;\n",
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       "            background-color:  #bfc9e1;\n",
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       "            background-color:  #f4edf6;\n",
       "            color:  #000000;\n",
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       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7\" ><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_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row0\" class=\"row_heading level0 row0\" >Pclass</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col1\" class=\"data row0 col1\" >-0.331339</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col2\" class=\"data row0 col2\" >0.1319</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col3\" class=\"data row0 col3\" >0.0830814</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col4\" class=\"data row0 col4\" >0.0184427</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col5\" class=\"data row0 col5\" >-0.5495</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col6\" class=\"data row0 col6\" >-0.028566</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row0_col7\" class=\"data row0 col7\" >0.0893423</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row1\" class=\"row_heading level0 row1\" >Age</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col0\" class=\"data row1 col0\" >-0.331339</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col2\" class=\"data row1 col2\" >0.0841534</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col3\" class=\"data row1 col3\" >-0.232625</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col4\" class=\"data row1 col4\" >-0.179191</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col5\" class=\"data row1 col5\" >0.0915661</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col6\" class=\"data row1 col6\" >-0.0235777</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row1_col7\" class=\"data row1 col7\" >-0.0260613</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row2\" class=\"row_heading level0 row2\" >Sex</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col0\" class=\"data row2 col0\" >0.1319</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col1\" class=\"data row2 col1\" >0.0841534</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col3\" class=\"data row2 col3\" >-0.114631</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col4\" class=\"data row2 col4\" >-0.245489</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col5\" class=\"data row2 col5\" >-0.182333</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col6\" class=\"data row2 col6\" >0.128526</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row2_col7\" class=\"data row2 col7\" >0.0404013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row3\" class=\"row_heading level0 row3\" >SibSp</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col0\" class=\"data row3 col0\" >0.0830814</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col1\" class=\"data row3 col1\" >-0.232625</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col2\" class=\"data row3 col2\" >-0.114631</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col6\" class=\"data row3 col6\" >0.0637943</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row3_col7\" class=\"data row3 col7\" >0.023189</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row4\" class=\"row_heading level0 row4\" >Parch</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col0\" class=\"data row4 col0\" >0.0184427</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col1\" class=\"data row4 col1\" >-0.179191</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col2\" class=\"data row4 col2\" >-0.245489</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col6\" class=\"data row4 col6\" >0.082144</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row4_col7\" class=\"data row4 col7\" >-0.0250445</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row5\" class=\"row_heading level0 row5\" >Fare</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col0\" class=\"data row5 col0\" >-0.5495</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col1\" class=\"data row5 col1\" >0.0915661</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col2\" class=\"data row5 col2\" >-0.182333</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col6\" class=\"data row5 col6\" >-0.0710096</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row5_col7\" class=\"data row5 col7\" >-0.0776145</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row6\" class=\"row_heading level0 row6\" >Embarked</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col0\" class=\"data row6 col0\" >-0.028566</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col1\" class=\"data row6 col1\" >-0.0235777</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col2\" class=\"data row6 col2\" >0.128526</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col3\" class=\"data row6 col3\" >0.0637943</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col4\" class=\"data row6 col4\" >0.082144</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col5\" class=\"data row6 col5\" >-0.0710096</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row6_col7\" class=\"data row6 col7\" >0.0118884</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7level0_row7\" class=\"row_heading level0 row7\" >Ticket</th>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col0\" class=\"data row7 col0\" >0.0893423</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col1\" class=\"data row7 col1\" >-0.0260613</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col2\" class=\"data row7 col2\" >0.0404013</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col3\" class=\"data row7 col3\" >0.023189</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col4\" class=\"data row7 col4\" >-0.0250445</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col5\" class=\"data row7 col5\" >-0.0776145</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col6\" class=\"data row7 col6\" >0.0118884</td>\n",
       "                        <td id=\"T_01638640_006d_11ea_bd6d_5c260a6e23e7row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f17e9feeb8>"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0\n",
      " 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0\n",
      " 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0\n",
      " 1 0 0 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 0 0 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1\n",
      " 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0\n",
      " 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0\n",
      " 0 0 0 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1\n",
      " 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 0]\n",
      "Accuracy:  0.803921568627451\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(X_test))\n",
    "print(\"Accuracy: \",clf.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "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": 24,
   "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     126         0\n",
      "1       3  47.0    0      1      0  0.013663     123         2\n",
      "2       2  62.0    1      0      0  0.018909     181         0\n",
      "3       3  27.0    1      0      0  0.016908     218         2\n",
      "4       3  22.0    0      1      1  0.023984      21         2\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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0\n",
      " 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1\n",
      " 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0\n",
      " 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0\n",
      " 1 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1\n",
      " 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1\n",
      " 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0\n",
      " 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0\n",
      " 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0\n",
      " 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 1 0 0 0\n",
      " 0 1 1 1 1 1 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7653631284916201\n",
      "Recall: 0.6618357487922706\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nV69eREVFMWvWLH7++WduvfXWgEp1RYWSIxEpcMGsPiUkwLZtmSdP27eX0NUnnRcSESkSWrVqxf3338/w4cOZPXs2kZGR3HbbbTz99NOZnt3x8uyzz9KmTRteeeUV5syZw59//kmNGjVo1KgRY8eO5aKLLjoxt0aNGixfvpynn36a6dOnM3r0aCpUqMB5553H/fffn+X3GThwIFFRUSxdupR58+ZRunRp6tevz/PPP8/AgQOzjfOWW26hVq1aPPXUU0yePJljx45x7rnn8uKLLzJo0KCgX2+oMbYI7QWNjo62cXFxhR2GiBSCrFaftmxxf/q1+pSdPK0+6byQiBRB69ato3HjxoUdRqFYsGABsbGxjBgxgkceeaSwwyn2gvlZM8astNZG5+b5tXIkIkVCXlaf0pKnkFx9qp1E3ZQtNDj4PZ33vMN562fqvJCIiEghUXIkIsVGxYrukZuzT36vPp08+1QaaAg05G560I2PeZCnuZRlwT+ZzguJiIjkCyVHIlJiFIXVp0/ozid0px3zeZCn6chcAtZ7dF5IRESkQCg5EhFJJ7erT2nJU36tPi0glgXE0pRVPFhhLNe330/YFZfrvJCISIhp164dRekMv2RNyZGISA5ku/p04AAJU79g2+RFbF24ha2JNdlKXbZQj63UZSt12U5tkoP853c1zeh18A3O/hHu7wJ9L4EI5UUiIiIFQsmRiEheJSTArFkwbRp88QUVjx2jIpDJ4hNJhLGTWieSpQ1lL2FCqX+w7VDVTL/FL7/AgAEwYgTccw/cdpt20omIiOS3otYHXkQkNPzxB0yaBF27wumnw623wsyZcOxYtreWJpkzqx7h8v7n0/uLvzIi4V427a/K229DdpVwd+6EIUNcpbvhwyG1kbmIiIjkAyVHIiLB2r8f3n4brrnGJUT9+sHs2a5RazCqVXPLP19+6bKc11+Hjh2hTBnKlHFP98MPMGMGxMRk/VR//AFPPOH6J91xhzvzJCIiInmj5EhEJCv79sFbb8HVV0ONGvDXv8KcOa6ZUTCqV3d74L76yiVEr70GV12VaUGFUqWge3dYuhS+/trlTlk5cgRefhnOPtslVz/+mMPXJyIiIicoORIRyWjvXpgwATp1cgnR3/4Gn30WfEJUowYMHOiym507Ydw4uPJKV80hSMZAbCx88QXExUHPnln3cE1Kcrv8LrzwZHIlIiIiOaOCDCIiAHv2uP1s06a5pCanzYxq1oTrr3dZTOvWEBaWb6E1bw5Tp8KGDTByJEycmHWe9skn7tGuHTz4oFt9yiqxEhERESeolSNjTCdjzHpjzC/GmAc9xusZY+YZY9YYYxYYY+qkXo81xnyX7nHUGNM9dextY8yv6caa5O9LExHJxu7dbptbhw4uuUk7DxRsYlSrFgweDP/9L8THw5gx0LZtviZG6Z17rjum9OuvcO+9EBmZ9fwFC9ziV1pyVZDNa0VERIoDk13TKmNMGLAB6ADEAyuA3tban9LNmQbMttZONMa0B/5qrb01w/NUAX4B6lhrDxtj3k6958Ngg42OjrZxcXHBThcRCbRrF0yf7laIFiyAlJSc3V+79skVolat3CGhQrJvH7zyCrz4otsJmJ2zz4b774e+fSEiouDjE5GSY926dTTOrtymSD4I5mfNGLPSWhudm+cP5rd6DPCLtXaztfY4MAXolmHO+cC81K/ne4wD3AB8Zq09nJtARURy7fffYexYd4inVq2T54GCTYzq1IG77oLFi2HrVpeNtG5dqIkRQJUqrpz3li0upDPPzHp+Wq+k+vVh1Cg4eNCfOEVEpHC1a9cOk2F/9SOPPIIxhgULFhROUCEqmN/stYFt6f4en3otve+B61O/vg6oYIzJ2M3wJmByhmtPpm7Fe8EY4/k5pjFmgDEmzhgTt1sNPUQkWDt3ntzmdsYZcPvtOVspqlvXdVtdssRlHy+8UOgrRZmJjHTlvDdtQr2SRERE8iCY3/Jex3gz7sW7D2hrjFkNtAW2A0knnsCYWsBFwBfp7hkKNAJaAFWAB7y+ubV2vLU22lobXb169SDCFZESa/t2eOkluOIKt/1t8GD45hvIZvvwCfXqwX33uVJvv/0Gzz0Hl10WkgmRF/VKEhERyZtgfuPHA+k3a9QBdqSfYK3dYa3tYa1tCjycei0h3ZQbgRnW2sR09+y0zjHgLdz2PRGRnImPP7nNrU4duPNOWLgw+ITorLPcMsry5a7SwciR0LJlkS7vpl5JIiIiuRNMcrQCOMcYU98YE47bHjcz/QRjTDVjTNpzDQXezPAcvcmwpS51NQnjNkB2B37IefgiUiJt23Zym9uZZ548DxSsBg3ggQdcA6HNm+HZZ6FFiyKdEHlRryQRkYK3YMECjDE88sgjfP3117Rt25YKFSrQpIkrxJycnMzYsWOJjo4mMjKSChUqEBsby7x58zyfb/v27QwePJiGDRsSERHB6aefTvv27fnggw9OzElISODpp5+mTZs21KhRg4iICBo0aMDdd99NQkKC5/NKcLJNjqy1ScAg3Ja4dcBUa+2PxpjHjDHXpk5rB6w3xmwAagBPpt1vjDkLt/L03wxP/Z4xZi2wFqgGPJGnVyIixduWLW6b26WXnjwP9O23wd9/9tkwdCisXOkqEzz9tKtxXcwSosyklfP++Wfo399twcvKJ5+4HYVpyVWwC3EiIukZU3QeebVo0SI6depExYoV+de//sWVV16JtZYbb7yR22+/ncTERP7+979z8803s27dOjp27MjUqVNPeY4ff/yRpk2bMmbMGBo2bMg999xDjx49OHDgAOPGjTsxb926dTzyyCNERUXRq1cvBg0aRO3atRk9ejTt27fn+PHjeX9BJZW1tsg8mjdvbkWkBPn1V2tHjrQ2JsZa9/48Z49zz7X24YetXb3a2pSUwn41ISU+3tp777U2MjK4/ymbNrX2gw+sTUoq7MhFJBT99NNPntdz8093YT1ya/78+RZ3Ht++//77p4y9+uqrFrB33XWXTU5OPnF9z549tn79+rZq1ar28OHDJ643adLEAnbq1KkB3yc+Pv7E13/88Yfdt29fwJz//Oc/FrCTJk065Xrbtm0tGV7kiBEjLGDnz5+fo9db2DL7WUsPiLO5zDeKxiljESk50m9zq1//5HmgYJ13HgwbBt9/75ZJnngCmjQpMStEwapd25Xz3roVHnsMqmasL5rB6tXQq5erhPfGG3DsmD9xiogUFdHR0fTu3fuUa6+88grVqlVj5MiRlEpX3Kdq1arce++97N27l6+++gqApUuX8t1339GlSxd69uwZ8Py1a58sFl2xYkUqV64cMOef//wnQKZb9iR7pQs7ABERNm1yTVmnTYNVq3J+f+PG7kBNz55wwQVKhHIgrVfSPffAhAkuYdq2LfP5GzfCP/4B//63u+e226BCBf/iFREJVdHRp/YcPXz4MD/++CNnnXUWTzwReHpk48aNAKxfv56uXbsSFxcHQMfsquik+vzzz3nxxReJi4tj3759pKRrVbFz587cvowST8mRiBSOjRtPJkTffZfz+y+4wCVDN9zgvpY8SeuVNHAgvP8+PPMMrFuX+fy0XklPPgmDBrl71W1BREqy008//ZS/79+/H2stv/76K48++mim9x06dAjgRCGFM844I9vv9f7779OnTx8qVqxIp06dOOusszjttNMAePTRRzmm5f1cU3IkIv5Zvx4+/NAlRN9/n/P7L7zw5ApRdp1OJVfSeiXdeivMnAlPPZX1rsa0XknPPecKPdx3n6uXISICJauYi8mwayEqKgqAdu3aMX/+/Gzvr1SpEgA7duzIZiY88cQTlCtXjlWrVtGgQYMT1//3v/9lmYhJ9nTmSEQK1s8/w+OPw8UXQ6NGJ88DBevii93969bB2rVuP5cSowKXsVdShw5Zz0/rldSwoUuufvrJnzhFREJVhQoVaNSoEWvWrOHw4cPZzm/RogUAc+fOzXbu5s2bady48SmJEcC3OaniKp6UHIlI/vvpJ3j0UbfS07ixS2jWrg3+/iZN3H6t9etdIjVsmEusxHdpvZLmznW9km64IbheSRdc4JKrZcv8i1VEJNQMGjSIffv2ceedd3qW116+fPmJxCkmJoZmzZoxZ84cPvzww4C527dvP/H1mWeeycaNG9m9e/eJa7t27eKhhx4qgFdRsmhbnYjknbXw448nzxBldVglM82anTxDdPbZ+R+j5Fnz5u7/3g0bXEHBSZMgMTHz+Z984h7t2rkWUx06qFaGiJQs//rXv1i4cCFvvPEG8+bNIzY2lurVqxMfH8/KlSv5+eef2blzJ+XKlQPg3XffpV27dvTs2ZOOHTvSrFkzEhISWLVqFWXLlj2xPe+f//wn9913H82bN6dHjx78+eefzJ49m8suu4x1ufkdLCcoORKR3LHWrQalnSH6+eecP0fz5icTooYN8z9GKRDnnuvKeT/6KLzwAowbB6nniT0tWOAezZrBgw9Cjx4QFuZXtCIihccYw+TJk+ncuTMTJkzgww8/5NixY9SqVYuLL76YoUOHUq1atRPzGzduzMqVK3nyySeZM2cO8+fPp3Llylx44YUnynQD3H333ZQqVYrXXnuNcePGUatWLfr378+///1vIiIiCuOlFhvGFqGTctHR0TatzKGIFAJrYc2akytEGzbk/DlatDiZENWvn/8xiu/27YNXXoEXX4S9e7Off845cP/9ruiDfoeLFA/r1q2jsc6Dig+C+Vkzxqy01kZnOSkTOnMkIlmz1nUAfegh12A17TxQThKjli1dA53ffnOlz4YMUWJUjKT1StqyxSVIZ56Z9fy0Xkn167sfi4MH/YlTREQkO0qORCSQtbBypdsDdc45bj/UU0+5d7XBuuwyeP5594556VK4916oV6/gYpZCl9Yr6Zdf4O23sy8qmNYrqW5dl1ylO1csIiJSKJQciYhjLaxYAQ884AoiREe7TqCbNgX/HK1auUMoW7fCkiVw991qelMChYe7ct4//AAzZkBMTNbz03ol1avnkqutW/2JU0REJCMlRyIlmbUnt7k1aODexT77LGzeHNz9xkDr1m4vVXw8LF4Md92V/b4qKRHUK0lERIoaVasTKWmsdc1npk1zleZy+jG9MdCmjSuq0KMHnHFGwcQpxUZar6TYWLdb8+mn4aOP3I+il7ReSZMmQbdurgx4y5b+xiwiIiWTkiORkiAlxX18P22ae1e6bVvO7i9VCq64wlWY69EDatUqmDil2FOvJBERCWXaVidSXKWkwKJFbptb3bpw+eUwenTwiVGpUu6j/rFjYccOmD8fbr9diZHki7ReSb/+6mp1REZmPX/BAvi//3NH4aZNg+RkX8IUkRwoSu1hpGjy42dMfY5EipPkZFcIIW2FaMeOnN2flhD17AnXXQenn14wcYpkoF5JIkXbpk2bqFWrFuXKlSvsUKQYO3z4ML///jsNGjTIcl5e+hwpORIp6pKT3QpRWkL0++85uz8sDNq3dwlR9+5QvXrBxCkShEOHYMIE1/8omEXOM86Ae+6BAQOgQoWCj09EvCUkJPC///2PatWqUaFCBUqXLo3RHljJB9ZakpKSOHjwIHv27KFGjRpUrFgxy3uUHImUNMnJ8M03LiGaPh3+97+c3V+6NFx5pTtD1L07VKtWMHGK5NLx4zB5sqsmv25d9vMrV4ZBg2DwYOX3IoXl6NGj7N69m6NHj5KUlFTY4UgxUrp0aU477TSqV6/Oaaedlu18JUciJUFS0qkJ0a5dObu/dGm46iq3QtStG1StWjBxiuSjlBSYOdP1IF6+PPv5ZcvCP/7hzjGpxZaISMmk5EikuEpKcifRp01z3TR3787Z/WXKuPJeaQlR5coFEqZIQbPW/afw1FPw5ZfZzy9dGvr0ceeSzj+/wMMTEZEQouRIpDhJTHSV4dISomBOp6cXHg4dO7qEqGtXJURS7ATTKym97t3hwQfVK0lEpKRQciRS1CUmwrx5LiH6+GNXuisnwsOhUyd3hujaayGbg4oixUGwvZLSxMa6JEm9kkREijclRyJF0fHjpyZE+/fn7P6ICJcQpa0QRUUVTJwiIW77dnjhBRg3zlW7y06zZi5J6tHDFWsUEZHiRcmRSFFx7Ab0WrMAACAASURBVBh89ZVLiD75BP74I2f3n3YadO7sEqIuXZQQiaSjXkkiIgJKjkRC27FjMHeuS4hmzoSEhJzdX7YsXH212zLXpYuauYhkQ72SRERKNiVHIqHm6FH44gv48EOXEB04kLP7y5Z1iVDPni4xKl++YOIUKcbUK0lEpGRSciQSCo4ccQnRtGkwaxYcPJiz+8uVg2uucQlR584QGVkwcYqUMOqVJCJSsig5Eikshw/D55+7hGj2bPjzz5zdHxnpiin07OmKK5QrVzBxioh6JYmIlBBKjkT8dPgwfPqpS4jmzAmuPFZ65cu7cts33OASorJlCyZOEclUXJzbbqdeSSIixY+SI5GCduiQS4Q+/ND9efhwzu6vUMElRD17wv/9n6s6JyKFbv16GDlSvZJERIoTJUciBeHPP10iNG2aWyk6ciRn90dFQbduLiHq2FG1gkVC2Pbt8Pzz8Npr6pUkIlLUKTkSyU+//goPPOCKKhw9mrN7K1Z0+2969oSrrlJCJFLE7NsHY8bASy+pV5KISFGl5Egkv3z/PVxxRc5Kb1eqdGpCFB5ecPGJiC8OHYI33nC9kuLjs5+vXkkiIqFDyZFIfvj9d4iJCa5rZJUqJxOi9u2VEIkUU8ePw/vvu+INP/+c/Xz1ShIRKXx5SY5K5XcwIkXS0aNw3XVZJ0ZVq0L//q6X0e+/w4QJrtqcEiORYis8HP7yF/jxR5g+HVq0yHr+/v3w+ONQrx7ceSds3epLmCIikk+UHIlYC3//OyxdGjhWpYrbJzN3LuzcCa+/7oorlCnjf5wiUmhKlXKfnyxbBvPmuWp1WTlyxJ1batjQJVfr1vkSpoiI5JGSI5H//Mftm8moWjXXDOW119w7ISVEIiWeMW4n7dy5sGKFa1eWVUnvpCSYONE1kb3uOli+3L9YRUQk54JKjowxnYwx640xvxhjHvQYr2eMmWeMWWOMWWCMqZNuLNkY813qY2a66/WNMcuMMRuNMR8YY7Q3Sfz30UcwbFjg9fBwmDED6tf3PyYRKRKio12l/3Xr3OJzdp+ffPyxayLbvj18+WVwzWdFRMRf2SZHxpgw4BWgM3A+0NsYc36GaaOASdbai4HHgKfSjR2x1jZJfVyb7vozwAvW2nOA/cDf8/A6RHJu1SpXf9fL+PHQurW/8YhIkXTeea6y3a+/uop1kZFZz58/3+3ObdHC9ZVOTvYnThERyV4wK0cxwC/W2s3W2uPAFKBbhjnnA/NSv57vMX4KY4wB2gMfpl6aCHQPNmiRPNuxA7p29W7s+sAD0K+f/zGJSJFWuzY895wrwvDoo66GS1ZWrnQFLxs3dsnVsWP+xCkiIpkLJjmqDaQv4RWfei2974HrU7++DqhgjEn7tXCaMSbOGLPUGJOWAFUF/rDWJmXxnAAYYwak3h+3e/fuIMIVycbhw9Ctm0uQMurWzZ1BEhHJpSpV4N//hi1bYPRoqFMn6/kbN8I//gENGrjk6uBBf+IUEZFAwSRHXkdNM+6Uvg9oa4xZDbQFtgNpiU/d1DrjNwOjjTENg3xOd9Ha8dbaaGttdHU1jZC8SklxpaO8+mVdcgm8+64rSyUikkeRka6c96ZN8NZb0KhR1vN37ID77nNlwP/9b9izx584RUTkpGDeBcYDZ6b7ex3glI/crbU7rLU9rLVNgYdTryWkjaX+uRlYADQF9gCVjDGlM3tOkQLx2GPuBHVGNWrAzJlQvrz/MYlIsZbbXkl166pXkoiI34JJjlYA56RWlwsHbgJmpp9gjKlmjEl7rqHAm6nXKxtjItLmAJcDP1lrLe5s0g2p9/QDPsnrixHJ0pQp7iBARhERroxU3br+xyQiJYZ6JYmIhL5sk6PUc0GDgC+AdcBUa+2PxpjHjDFp1efaAeuNMRuAGsCTqdcbA3HGmO9xydDT1tqfUsceAO4xxvyCO4M0IZ9ek0igZcvcuwsvb74Jl17qazgiUnKpV5KISOgytgg1WoiOjrZxXmdFRLKybRvExMDvvweODR/uttqJiBSi9eth5EiYNAkSE7OfHxsLQ4fCVVdlnViJiJRExpiVqTUPckwnz6V4O3QIrr3WOzG64QZ45BHfQxIRyUi9kkREQoOSIym+UlLgllvgu+8Cx5o3d/tUVJlOREKIeiWJiBQuvTOU4mvYMFdoIaMzzoBPPoFy5fyPSUQkCOqVJCJSOJQcSfH0zjvw1FOB18uWdYlRbc+ewyIiIUW9kkRE/KXkSIqfxYuhf3/vsYkTITpX5/NERAqNeiWJiPhDyZEUL7/95mrdHj8eOPbYY25zvohIEaVeSSIiBUvJkRQfBw9C166we3fgWO/e7gySiEgxoF5JIiIFQ8mRFA/JyXDzzfDDD4FjLVvChAlqBiIixVJ0NEyb5laF/v53KFMm6/kff+z+WezQwZ1lEhGRk5QcSfHwwAMwe3bg9TPPdO8Eypb1PyYRER+l9UravDm4XklffQXNmsGMGf7EJyJSFCg5kqJvwgRXuzajcuVg5kyoWdP/mERECkmdOu6fxC1bsu+VdOAA9OjhKtwlJvoXo4hIqFJyJEXbf/8LAwcGXjcG3nsPmjTxPyYRkRBQtWrwvZKeew5iY2H7dv/iExEJRUqOpOjatAmuv977486nnoLu3f2PSUQkxGTslXTWWd7zFi+Gpk3ddjsRkZJKyZEUTQkJrjLd3r2BY337wv33+x+TiEgIS+uVtHo1dOvmPWf3bujY0fVISknxNTwRkZCg5EiKnqQk6NXLu2HH5ZfD+PGqTCcikolKlVwRhpEjISwscNxatx3v6qthzx7/4xMRKUxKjqToufde+OKLwOtnneV+40dE+B6SiEhRYowrwjB/PtSq5T3niy9cNbulS/2NTUSkMCk5kqJl3DjX7j2j8uVh1iyoXt3/mEREiqg2bdw2u/btvce3bYMrrnD/7Frrb2wiIoVByZEUHfPmwaBBgdeNgSlT4MIL/Y9JRKSIq1ED5s6FYcO8xxMTXUGHXr1c6W8RkeJMyZEUDRs2wA03QHJy4NioUdCli/8xiYgUE2FhrgjDp59ClSrec6ZNgxYtYO1af2MTEfGTkiMJffv3wzXXwB9/BI717w933+1/TCIixVDnzm6bXUyM9/iGDdCyJUyc6G9cIiJ+UXIkoS0xEXr2hI0bA8fatoVXXlFlOhGRfFS3LixcCIMHe48fOeJKgv/jH+5rEZHiRMmRhC5r4Y473FmjjBo2hI8+co07REQkX4WHuyIMU6a4ejde3ngDWrVyzWVFRIoLJUcSusaMcdXpMqpY0VWmq1rV/5hEREqQXr0gLg4uuMB7/LvvoHlz+Phjf+MSESkoSo4kNH3+Odx1V+D1sDCYOhUaN/Y/JhGREui882DZMrj1Vu/xhAS47jrXNykx0d/YRETym5IjCT3r1rmPK1NSAsdGj4aOHf2PSUSkBIuMdEUYxo/PvM/2c89BbCxs3+5vbCIi+UnJkYSWPXtcZTqvZhoDB8Ltt/sfk4iIYIwrwrBkCdSv7z1n8WJo2tT7qKiISFGg5EhCx/HjcP31sHlz4NiVV8KLL6oynYhIIWvWDFatgm7dvMd374YOHeCJJ7w3AIiIhDIlRxIarHUrQ998Ezh27rmu+2CZMv7HJSIiASpVghkzYORIdxQ0I2th+HDXn3vPHv/jExHJLSVHEhqefx7efDPweuXKMHu2+1NEREKGMa4Iw/z5UKuW95zPP3crTUuX+hubiEhuKTmSwjd7NgwZEni9dGn48EM45xz/YxIRkaC0aQOrV0P79t7j27bBFVfAyy+7FSURkVCm5EgK19q10Lu392/MMWMy/20rIiIho0YNmDsXhg3zHk9MdD29e/XyrrcjIhIqlBxJ4dm1C7p2hT//DBy780647Tb/YxIRkVwJC4PHH4dPP4UqVbznTJsGLVq4z8VEREKRkiMpHEePuq6BW7YEjnXqBKNG+R+TiIjkWefOrppdTIz3+IYN0LKl65skIhJqlByJ/6yFAQNcs4yMGjeGKVPceSMRESmS6tWDhQth8GDv8SNH4C9/cX2Tjh71NTQRkSwpORL/PfMMvPNO4PWqVWHWLKhY0f+YREQkX4WHw0svuc+7ypf3nvPGG3DZZbBpk7+xiYhkRsmR+GvGDBg6NPB6mTIwfTo0bOh/TCIiUmB69YIVK+CCC7zHv/sOmjeHjz/2Ny4RES9KjsQ/q1fDLbd4j40b52q9iohIsdOoESxbBrfe6j2ekOCOod53n6tsJyJSWJQciT927oRrr4XDhwPH7rsP/vY3/2MSERHfREa6Igzjx0NEhPec556D2FjYvt3f2ERE0ig5koJ35Ah07w7x8YFjXbvC00/7H5OIiPjOGFeEYckSqF/fe87ixdC0Kcyb529sIiKg5EgKmrVuVWj58sCxiy6C995zzTFERKTEaNbMlfvu1s17fPdu6NABnngCUlL8jU1ESragkiNjTCdjzHpjzC/GmAc9xusZY+YZY9YYYxYYY+qkXm9ijPnWGPNj6livdPe8bYz51RjzXeqjSf69LAkZjz/uShVldPrprjJdhQr+xyQiIoWuUiVXo+fZZ70/I7MWhg+HLl1gzx7/4xORkinb5MgYEwa8AnQGzgd6G2POzzBtFDDJWnsx8BjwVOr1w0Bfa+0FQCdgtDGmUrr7hlhrm6Q+vsvja5FQM3UqjBgReD083P1GrFfP/5hERCRkGANDhsD8+VCrlveczz93K03Llvkbm4iUTMGsHMUAv1hrN1trjwNTgIwL4ecDabuD56eNW2s3WGs3pn69A9gFVM+PwCXErVgB/fp5j02YAK1a+RuPiIiErDZtXEHT9u29x7dtc3NeftmtKImIFJRgkqPawLZ0f49PvZbe98D1qV9fB1QwxlRNP8EYEwOEA+lbvT2Zut3uBWOMZ+0aY8wAY0ycMSZu9+7dQYQrhW77dreR3Kvt+UMPZV7OW0RESqwaNWDuXBg2zHs8MRHuuMP1TTpwwN/YRKTkCCY5Mh7XMn5ucx/Q1hizGmgLbAeSTjyBMbWAd4C/WmvTjlYOBRoBLYAqwANe39xaO95aG22tja5eXYtOIe/QIVeye+fOwLHrrnNnkERERDyEhblfE3PmQJUq3nOmTYMWLWDtWn9jE5GSIZjkKB44M93f6wA70k+w1u6w1vaw1jYFHk69lgBgjIkC5gDDrLVL092z0zrHgLdw2/ekKEtJcVvpVq0KHGvaFN55B0qpQKKIiGTt6qvdr5KYTN4ZbNgALVvCpEn+xiUixV8w71RXAOcYY+obY8KBm4CZ6ScYY6oZY9KeayjwZur1cGAGrljDtAz31Er90wDdgR/y8kIkBIwYAR99FHi9Zk2YOdN1ABQREQlCvXqwcCEMHuw9fuSI+zxuwADvXdwiIrmRbXJkrU0CBgFfAOuAqdbaH40xjxljrk2d1g5Yb4zZANQAnky9fiNwBfAXj5Ld7xlj1gJrgWrAE/n1oqQQvPeea0iR0WmnwSefQJ06/sckIiJFWng4vPSS6whRvrz3nNdfh8sug02bvMdFRHLC2CJU9iU6OtrGxcUVdhiS0bffQmwsHDsWODZlijs9KyIikgc//ww33AA//ug9XrEivP02dO/ua1giEoKMMSuttdG5uVcHQCRvtmxxv4m8EqMRI5QYiYhIvmjUyPU6uvVW7/GEBFf3Z8gQV9lORCQ3lBxJ7v35p6tMt2tX4FivXt4NYEVERHIpMhImToTx4yHCswEIjBrl+iVt3+5vbCJSPCg5ktxJToY+fWDNmsCxFi3grbdc63MREZF8ZAz84x+wZAnUr+89Z9EiVyR13jzvcRGRzCg5ktx56CFXgS6j2rXh44+hbFn/YxIRkRKjWTNYudJtYPCyezd06OBqBaWkeM8REclIyZHk3Ntvw7PPBl4vV84lTGec4XtIIiJS8lSu7D6Pe/ZZ10A2I2th+HDo0gX27vU/PhEpepQcSc4sWuSaSnh55x33UZ6IiIhPjHFFGObPh1q1vOd8/rnbZrdsmb+xiUjRo+RIgrd5sysF5FUG6MknoUcP/2MSEREB2rSB1atdZwkv27a5OS+/7FaURES8KDmS4Bw4AF27wp49gWN9+sDQof7HJCIikk6NGvDll/Dww97jiYlwxx2uoOqBA/7GJiJFg5IjyV5yMtx0E/z0U+DYZZfBG2+oMp2IiISEsDBXhGHOHKhSxXvOtGmusOratf7GJiKhT8mRZG/IEPjss8DrdevCjBlw2mn+xyQiIpKFq6+GVasgJsZ7fMMGaNkSJk3yNy4RCW1KjiRrr78OL7wQeD0yEmbNcnsYREREQlC9evDNNzBokPf4kSPQr5+rM3T0qL+xiUhoUnIkmZs/H/71r8DrxsDkyXDxxf7HJCIikgMREa4Iw5QpUL6895zXX4dWrWDTJn9jE5HQo+RIvG3cCNdfD0lJgWPPPuuKM4iIiBQRvXrBihVwwQXe46tXQ/Pmrm+SiJRcSo4k0B9/uORn//7Asb/+Fe691/+YRERE8qhRI9fr6JZbvMcTElzHiiFDvLtWiEjxp+RITpWUBDfeCOvXB461aQOvvqrKdCIiUmRFRroiDK+9BuHh3nNGjYL27WH7dn9jE5HCp+RITnXXXa5JREb168P06W7ztoiISBFmjCvC8O237tebl0WLoFkzmDfP39hEpHApOZKTXnnFPTKKioLZs6FaNf9jEhERKSDNmsHKlXDttd7ju3ZBx46ub1JKir+xiUjhUHIkzpdfwp13Bl4vVcqV+Dn/fP9jEhERKWCVK7siDM8+6xrIZpSSAsOHwzXXwN69/scnIv5SciTw88/QsyckJweOPf88dO7sf0wiIiI+McYVYfj6a6hVy3vOZ59B06auoIOIFF9Kjkq6vXvdx2EJCYFjAwbAHXf4H5OIiEghuOIKV9I7NtZ7fNs2V5vo5ZfBWn9jExF/KDkqyY4fhxtu8O561749jBmjynQiIlKi1Kjhdpo//LD3eGKi+9zwppvg4EF/YxORgqfkqKSyFgYNggULAsfOPhumTYMyZXwPS0REpLCFhbkiDHPmuDNJXqZOhRYt4Icf/I1NRAqWkqOS6sUX4fXXA69XrOgq01Wp4n9MIiIiIeTqq902uxYtvMfXr4eYGNc3SUSKByVHJdGnn8K99wZeDwuDDz+E887zPyYREZEQVK8eLFzoNlt4OXIE+vVzx3SPHvU3NhHJf0qOSpoff3Qbpb0aNrz8Mlx1lf8xiYiIhLCICPcrcsoUKF/ee87rr0OrVt7HeEWk6FByVJLs3g1du3qfIB00CAYO9D8mERGRIqJXL1ixAi64wHt89Wpo3tz1TRKRoknJUUlx7Bj06AG//ho41rEjvPCC/zGJiIgUMY0auV5Ht9ziPZ6QANdd5/omJSb6G5uI5J2So5LAWvjnP2HRosCxRo3ggw+gdGn/4xIRESmCIiNdEYbXXoPwcO85o0a5rhg7dvgbm4jkjZKjkmDUKHj77cDrVarArFlQqZLvIYmIiBRlxrgiDN9+C/Xre89ZtAiaNoV58/yNTURyT8lRcTdzJjzwQOD10qXho49cTyMRERHJlWbNYOVKuPZa7/Fdu9zu9See8K6FJCKhRclRcfb993DzzW5bXUavvgrt2vkekoiISHFTubIrwvDss64rRkYpKTB8OFxzDezd6398IhI8JUfF1e+/u8p0hw4Fjt19N/Tv739MIiIixZQxrgjD119DrVrecz77zG2zW7bM39hEJHhKjoqjo0ddqZxt2wLHrr4aRo70PyYREZES4IorXEnv2Fjv8W3boE0bGDPGe2OHiBQuJUfFjbXw97/D0qWBYxdcAJMne6/5i4iISL6oUQO+/BIefth7PDERBg92Pdm9Wg+KSOFRclTc/Oc/8P77gderVXOV6aKi/I9JRESkhAkLc0UYZs92Z5K8TJ0KLVrADz/4G5uIZE7JUXHy0UcwbFjg9fBwmDEj81qjIiIiUiC6dHHb7Fq08B5fvx5iYuCdd/yNS0S8KTkqLlauhFtv9R4bPx5at/Y3HhEREQGgXj1YuBAGDfIeP3IE+vZ1fZOOHvU3NhE5lZKj4mDHDtdg4ciRwLEHHoB+/fyPSURERE6IiICXX3ZHfyMjvee8/jq0agWbNvkbm4icFFRyZIzpZIxZb4z5xRjzoMd4PWPMPGPMGmPMAmNMnXRj/YwxG1Mf/dJdb26MWZv6nC8ZY0z+vKQS5vBh6NbNJUgZdevmziCJiIhISLjpJlixAs4/33t89Wpo3tz1TRIR/2WbHBljwoBXgM7A+UBvY0zG/6RHAZOstRcDjwFPpd5bBRgBtARigBHGmLRjia8CA4BzUh+d8vxqSpqUFPjLXyAuLnDskkvg3XehlBYHRUREQknjxrB8Odxyi/d4QoLryDFkiKtsJyL+Ceadcwzwi7V2s7X2ODAF6JZhzvnAvNSv56cb/z/gS2vtPmvtfuBLoJMxphYQZa391lprgUlA9zy+lpLn0Udh2rTA6zVqwMyZUL68/zGJiIhItiIjYdIkeO01VzfJy6hR0L699+YQESkYwSRHtYH03UTjU6+l9z1wferX1wEVjDFVs7i3durXWT2nZGXyZHjsscDrERFuLb5uXf9jEhERkaAZ44owLFmSeUHZRYugaVOYN897XETyVzDJkddZoIw9ne8D2hpjVgNtge1AUhb3BvOc7psbM8AYE2eMidu9e3cQ4ZYAy5bBX//qPfbmm3Dppf7GIyIiIrnWvLkrOnvttd7ju3ZBx46ub1JKir+xiZQ0wSRH8cCZ6f5eBzhlgddau8Na28Na2xR4OPVaQhb3xqd+nelzpnvu8dbaaGttdPXq1YMIt5jbtg26d4djxwLHhg+Hm2/2PyYRERHJk8qV3caPZ591DWQzSklxv+avuQb27vU/PpGSIpjkaAVwjjGmvjEmHLgJmJl+gjGmmjEm7bmGAm+mfv0F0NEYUzm1EENH4Atr7U7goDHm0tQqdX2BT/Lh9RRvf/7pPlb6/ffAsRtugEce8T0kERERyR/GuCIMX38NtWp5z/nsM2jWzG0iEZH8l21yZK1NAgbhEp11wFRr7Y/GmMeMMWkLwO2A9caYDUAN4MnUe/cBj+MSrBXAY6nXAAYCbwC/AJuAz/LrRRVLKSmuyet33wWONW8OEyeqMp2IiEgxcMUVsGoVxMZ6j2/dCm3awJgxYD0PJYhIbhlbhP6rio6OtnFeZatLgocegqeeCrx+xhmuHmht1bMQEREpTpKSYMSIrFsW3ngjvPEGVKjgX1wioc4Ys9JaG52be7XUUBS88453YlS2LHzyiRIjERGRYqh0aXjySZg9251J8jJ1KrRoAT/84G9sIsWVkqNQt3gx9O/vPTZxIkTnKikWERGRIqJLF1i92iVBXtavh5gY91mqiOSNkqNQ9ttvrkX28eOBY489Bj17+h6SiIiI+K9ePVi4EG6/3Xv8yBHo29f1TTp61N/YRIoTJUeh6uBB6NoVvHo79e4Nw4b5H5OIiIgUmogIV4Rh8mSIjPSe8/rr0KoVbNrkb2wixYWSo1CUnOz6FXltIG7ZEiZMcPU+RUREpMS56SZYsQLOP997fPVqV8j2EzVJEckxJUeh6IEH3OnLjM4803WIK1vW/5hEREQkZDRu7IrV9unjPZ6Q4HrG338/JCb6G5tIUabkKNRMmADPPRd4PTISZs6EmjX9j0lERERCTmSkK8IwbhyEh3vPGTkS2reHHTv8jU2kqFJyFEr++18YODDwujHw7rvQpIn/MYmIiEjIMgZuuw2WLIH69b3nLFoETZvC11/7G5tIUaTkKFRs2gQ9enivfT/1lFsbFxEREfHQvDmsXAnXXus9vmsXdOjg+ialpPgbm0hRouQoFCQkwDXXwL59gWN9+7oNwyIiIiJZqFzZHU1+5hkICwscT0lxxW6vuQb27vU/PpGiQMlRYUtKgl694OefA8cuvxzGj1dlOhEREQmKMe4z1a+/zvyY8mefQbNmsGyZv7GJFAVKjgrbvffCF18EXj/rLJgxwzU1EBEREcmBK65wJb1jY73Ht26FNm1c3yRr/Y1NJJQpOSpM48bBSy8FXi9fHmbNgurV/Y9JREREioWaNWHuXHjoIe/xxEQYPNj1lj940N/YREKVkqPC8tVXMGhQ4HVjYMoUuPBC/2MSERGRYqV0aVeEYfZsdybJywcfQIsW3r3nRUoaJUeFYcMG6NkTkpMDx0aNgi5d/I9JREREiq0uXWDVKpcEeVm/HmJiXN8kkZJMyZHf9u93ZWL++CNwrH9/uPtu/2MSERGRYu+ss2DhQrj9du/xI0dckdzbboOjR30NTSRkKDnyU2Ii3HADbNwYONa2LbzyiirTiYiISIGJiHBFGCZPhshI7znjx0OrVrB5s7+xiYQCJUd+sdadevRqT92wIXz0EYSH+x+XiIiIlDg33QQrVsD553uPr17tyn1/8om/cYkUNiVHfhkzBl57LfB6xYquMl3Vqv7HJCIiIiVW48awfDn06eM9npAA3bu7vkmJif7GJlJYlBz54fPP4a67Aq+HhcHUqe5fJxERERGfRUa6IgzjxmW+gWXkSLjyStixw9/YRAqDkqOC9tNP0KsXpKQEjo0eDR07+h+TiIiISCpjXBGGJUugfn3vOQsXQtOm3qcDRIoTJUcFac8e6NoVDhwIHBs4MPNyMSIiIiI+a94cVq50b1287NoFHTq4vklen/mKFAdKjgrK8eNw/fXepV6uvBJefFGV6URERCSkVK4MH38Mzzzjdv9nlJICw4a5riR79/ofn0hBU3JUEKx1K0PffBM4du65MG0alCnjf1wiIiIi2ShVyhVh+PprqFnTe85nn7lqdsuX+xubSEFTclQQnn8e3nwz8HrlyjB7tvtTREREJIRdcYUr6R0b6z2+dSu0bu0K8lrrb2wiBUXJUX6bPRuGDAm8Xro0fPghnHOO/zGJiIiI5ELNmjB3Ljz0kPd4mdFv6QAAIABJREFUYqJr49i7Nxw86G9sIgVByVF+WrvW/evg9fHJmDHQvr3/MYmIiIjkQenSrghDVptfPvgAWrSAH37wNzaR/KbkKL/s2uXKu/z5Z+DYnXe6GpkiIiIiRVSXLrBqlUuCvKxfDzExrm+SSFGl5Cg/HD0K110HW7YEjnXqBKNG+R+TiIiISD476yzX8yizbiRHjkDfvu4z4aNHfQ1NJF8oOcora2HAANc5LaPzz4cpU9x6tIiIiEgxEBHhTgu8/z5ERnrPGT8eWrXy7mgiEsqUHOXVM894rx9XrQqzZkHFiv7HJCIiIlLAeveGFSvcZ8FeVq925b4/+cTfuETyQslRXsyYAUOHBl4vUwamT4cGDfyPSURERMQnjRu7Xkd9+niPJyRA9+6ub1JSkr+xieSGkqPcWr0abrnFe2zcONccQERERKSYi4x0m2jGjYPwcO85I0fC9ddDSoq/sYnklJKj3Ni5E669Fg4fDhy77z7429/8j0lERESkkBjjijAsWeKKNniZOROeftrXsERyTMlRTh054taH4+MDx7p21X/1IiIiUmI1b+7KfXft6j0+fDgsWuRvTCI5oeQoJ6x1q0LLlweOXXQRvPcehIX5H5eIiIhIiKhcGT7+2H1ebMypYykprpDD3r2FE5tIdpQc5cTjj7vS3BmdfrqrTFehgv8xiYiIiISYUqXggQfcI6P4ePdZs7X+xyWSHSVHwZo6FUaMCLweHu6q1tWr539MIiIiIiHsscdcv6OMZs6El17yPx6R7Cg5CsaKFdCvn/fYhAne/9WLiIiIlHBlysDkyW6rXUZDhkBcnP8xiWQlqOTIGNPJGLPeGPOLMeZBj/G6xpj5xpjVxpg1xpirU6/3McZ8l+6RYoxpkjq2IPU508ZOz9+Xlk/i46FbNzh6NHDsoYcyL+ctIiIiItStC2++GXg9MRFuugkOHPA/JpHMZJscGWPCgFeAzsD5QG9jTMZeyMOAqdbapsBNwFgAa+171tom1tomwK3Ab9ba79Ld1ydt3Fq7Kx9eT/46dMglRjt3Bo5dd507gyQiIiIiWereHQYPDry+aZMrAa7zRxIqglk5igF+sdZuttYeB6YA3TLMsUBU6tcVgR0ez9MbmJzbQH2XkuK20q1aFTjWtKnrdlZKuxJFREREgjFypHsLldGUKe6UgkgoCObdfW1gW7q/x6deS+8R4BZjTDzwKeDx2QC9CEyO3krdUjfcmIzFHh1jzABjTJwxJm737t1BhJtPRvx/e/cfb1Vd53v89RGRU8dBxqDRATW8YmLZ1ULTmaHpYqmRmVkBJ6EwhZgRFEcdtJv5qzHRuZOKpoEiE8avqVGxLK88Rr0zjM0IaNahcUIroR9IYokXhl9+54+1qNNeG87mcM46Z+/zej4e53HO/nzXWuezfSw3+332Wt/v1fCNbxTrhxyS3UXY3FxeL5IkSXWuXz9YvBgOPLA4dtFF0Npafk9SpVrCUbXQUvnhZwswL6U0BBgNzI+I3x47It4NbE4p/aDNPuemlI4DRuZfE6r98pTS7JTSiJTSiEGDBtXQbif42tfgC18o1pua4MEHYciQcvqQJElqIMOGwVe+Uqxv2QJjxsDmzeX3JLVVSzhaBxzW5vEQipfNnQ8sAUgpPQk0AQPbjI+j4lOjlNLP8u+bgAVkl+91vyefhPPPrz42bx6c1DPalCRJqkef+ES2zlGl1auzT5Ck7lRLOHoKGBYRQyPiALKgs7RimxeBUwEiYjhZONqQP94P+DjZvUrktf0jYmD+c1/gTOAHdLef/jS7Y3Dr1uLY1VfD2LHl9yRJktRgbrsNhg8v1u+5J5v6W+ou7YajlNIOYCrwCPBDslnpWiPiuog4K9/sUmBSRHyP7BOiiSn9dt6R9wDrUkovtDlsP+CRiHgWeAb4GTCnU55RR732Gpx1FrxUZdK8sWOrLwArSZKkvdbcDEuWZHcsVJo8GdasKb8nCSBSHc2dOGLEiLSiK1YL27kTzjknm2ih0oknwhNPwBve0Pm/V5IkqRebMycLQ5Xe+U7413/NJnGQ9lZErEwpjejIvs5FDdlirtWC0eDB8MADBiNJkqQucMEF1e9aWLUKZswovx/JcDRvHtx0U7H+xjdmgemP/7j0liRJknqDCJg9G448sjh2663ZJMFSmXp3OPrnf67+WS5ki7y+853l9iNJktTL9O+frX/Ut29x7Lzz4MUXy+9JvVfvDUcvvAAf+Qhs314c+5u/ye5BkiRJUpcbMaL6hTyvvJJN/b1jR/k9qXfqneHo1VfhQx+Cl18ujo0fD1deWX5PkiRJvdjFF2dvzyotX+6kwSpP7wtHO3fCuHHZSmOVTjklmzYlovy+JEmSerEIuPdeGDKkOPbFL8Kjj5bfk3qf3heOLrsMvv3tYv3ww+H++6tPuC9JkqQu96Y3wYIFsF/FO9SUsot7fvnL7ulLvUfvCkezZ8MttxTrzc3w0EPwR39Ufk+SJEn6rZEj4dpri/WXXoIJE+D118vvSb1H7wlHjz0GF15YrEfAwoXwjneU35MkSZIKrrwSTj21WF+2DG68sfx+1Hv0jnD0ox/BRz9afaqTm26qfvefJEmSukWfPnDfffDmNxfHPv/5bJIGqSs0fjh65ZUs/LzySnHsvPPg0kvL70mSJEl7dMgh2bKTlXbuhJaW6pMOS/uqscPR9u0wZgw891xxbORIuPNOZ6aTJEnqoU47Da64olhfuxY+/elsogapMzV2OLrkkuzi1EpDh8I//iP061d+T5IkSarZdddlq61UWroUbrut/H7U2Bo3HN1xR/ZVqX9/+OY3YeDA8nuSJEnSXunbN5s76w//sDh2+eWwcmX5PalxNWY4evTRbJnlSvvtB4sWwbHHlt+TJEmSOuSII2Du3GJ9+3YYOxZefbX8ntSYGi8c/cd/wMc/nt2tV+nv/g4+8IHye5IkSdI+OftsmDatWH/+efjMZ7z/SJ2jscLRyy/DmWfCb35THJs8GS66qPyeJEmS1CluvhlOOKFYX7QI7rmn/H7UeBonHG3bBh/7WPbng0qjRsHttzsznSRJUh3r1w8WL4YDDyyOXXQRtLaW35MaS2OEo5Rg6lR4/PHi2FFHwT/8Q3Y3nyRJkurasGHwla8U61u2ZCu4bN5cfk9qHI0Rjm69FebMKdYPOiibme7gg8vvSZIkSV3iE5/I1jmqtHp19Tm5pFrVfzh6+GG49NJivU8f+PrX4a1vLb8nSZIkdanbboPhw4v1u+/Opv6WOqK+w9EPfgDjxsHrrxfHZs2C972v/J4kSZLU5ZqbYckSaGoqjk2eDGvWlN+T6l/9hqMNG+BDH4JNm4pjU6fCX/xF+T1JkiSpNG9/e3Z3RaXXXsvWP9q6tfyeVN/qMxxt3QrnnAM/+Ulx7LTT4EtfKr0lSZIklW/SpCwIVVq1CmbMKL8f1bf6C0cpwZQp8C//Uhw75phsfsf99y+/L0mSJJUuAmbPhiOPLI7deissXVp+T6pf9ReObr4Z5s0r1g8+GB56CAYMKL0lSZIkdZ/+/bO/j1dbuWXiRHjxxdJbUp2qr3D061/DFVcU6/vvD9/4RramkSRJknqdESNg5sxi/ZVXsqm/d+wovyfVn/oKRz/+cXZZXaU774T3vrf0diRJktRzTJ8OZ55ZrC9fDldfXX4/qj/1FY6qTdn9V38FF1xQfi+SJEnqUSKyuy+GDCmOffGLsGxZ6S2pztRXOKo0ejTcdFN3dyFJkqQe4k1vggULYL+Kd7kpwfjx8Mtfdk9fqg/1G47e9rZs+eM+fbq7E0mSJPUgI0fCtdcW6+vXw4QJ1S9GkqBew9HAgdnMdP37d3cnkiRJ6oGuvBJGjSrWly2DG28svx/Vh/oLRwccAPffD0OHdncnkiRJ6qH69IH77oNBg4pjn/98NkmDVKn+wtHs2fBnf9bdXUiSJKmHO/TQLCBV2rkTWlpg48bye1LPVl/h6JBD4FOf6u4uJEmSVCdOO636Mplr18J551VfJUa9V32Fo8GDu7sDSZIk1ZnrroNTTinWly6F224rvx/1XPUVjiRJkqS91LdvNsnxgAHFscsvh5Ury+9JPZPhSJIkSQ3viCPg3nuL9e3bYexYePXV8ntSz2M4kiRJUq9w9tkwbVqx/vzzMGWK9x+pxnAUEWdExHMRsSYiCre0RcThEfFYRDwdEc9GxOi8/paI2BIRz+Rfd7XZ510R8f38mLdFRHTe05IkSZKKbr4ZTjihWF+4EObOLb8f9SzthqOI6APcAXwAOBZoiYhjKzb7HLAkpXQCMA74cpux51NKx+dfU9rU7wQmA8PyrzM6/jQkSZKk9vXrB4sXw4EHFsemTYPW1vJ7Us9RyydHJwFrUkovpJS2AYuAD1dsk4D++c8HAT/f0wEj4lCgf0rpyZRSAr4KnL1XnUuSJEkdMGwY3HVXsb5lC4wZA5s3l9+TeoZawtFgYG2bx+vyWlvXAOMjYh3wMND2as6h+eV2T0TEyDbHXNfOMQGIiMkRsSIiVmzYsKGGdiVJkqQ9O/fcbJ2jSqtXw8UXl9+PeoZawlG1e4Eqb1drAeallIYAo4H5EbEf8Avg8Pxyu78CFkRE/xqPmRVTmp1SGpFSGjFo0KAa2pUkSZLaN2sWDB9erN99NyxaVH4/6n61hKN1wGFtHg+heNnc+cASgJTSk0ATMDCltDWl9HJeXwk8DxydH3NIO8eUJEmSukxzMyxZAk1NxbHJk2HNmvJ7UveqJRw9BQyLiKERcQDZhAtLK7Z5ETgVICKGk4WjDRExKJ/QgYg4kmzihRdSSr8ANkXEyfksdZ8EHuyUZyRJkiTV6O1vh1tvLdY3bcrWP9q6tfye1H3aDUcppR3AVOAR4Idks9K1RsR1EXFWvtmlwKSI+B6wEJiYT7TwHuDZvP51YEpKaWO+z18AdwNryD5R+nYnPi9JkiSpJpMmZRMxVFq1CmbMKL8fdZ9IdbTa1YgRI9KKFSu6uw1JkiQ1mN/8Bt75TnjhheLYgw/CWWcV6+qZImJlSmlER/ataRFYSZIkqZEddFA2CUPfvsWx886DtWuLdTUew5EkSZIEnHgizJxZrG/cCC0tsGNH+T2pXIYjSZIkKTd9Opx5ZrG+fDlcfXX5/ahchiNJkiQpFwH33guDBxfHvvhFWLas/J5UHsORJEmS1MbAgbBwIexX8U45JRg/Htav756+1PUMR5IkSVKFkSPh2muL9fXrYcIEeP318ntS1zMcSZIkSVVceSWMGlWsP/po9YkbVP8MR5IkSVIVffrAfffBoEHFsauuyiZpUGMxHEmSJEm7ceihMH9+sb5zZza998aN5fekrmM4kiRJkvbg9NNhxoxife3abIHYlMrvSV3DcCRJkiS14/rr4ZRTivWlS2HWrPL7UdcwHEmSJEnt6Ns3m957wIDi2GWXwcqV5fekzmc4kiRJkmpwxBHZArGVtm+HsWPh1VfL70mdy3AkSZIk1ejss2Hq1GL9+edhyhTvP6p3hiNJkiRpL9x8Mxx/fLG+cCHMnVt+P+o8hiNJkiRpLzQ1weLFcOCBxbFp06C1tfye1DkMR5IkSdJeOvpouOuuYn3LFhgzBjZvLr8n7TvDkSRJktQB556brXNUafVquPji8vvRvjMcSZIkSR00axYMH16s3303LFpUfj/aN4YjSZIkqYOam7P7j5qaimOTJ8OaNeX3pI4zHEmSJEn74Ljj4NZbi/VNm2DcONi6tfye1DGGI0mSJGkfTZqUTcRQaeVKmDGj/H7UMYYjSZIkaR9FwOzZcOSRxbFbb4WlS8vvSXvPcCRJkiR1goMOyiZh6Nu3OHbeebB2bfk9ae8YjiRJkqROcuKJMHNmsb5xI7S0wI4d5fek2hmOJEmSpE40fTqceWaxvnw5XHNN6e1oLxiOJEmSpE4UAffeC4MHF8duuAGWLSu/J9XGcCRJkiR1soEDYeFC2K/i3XZKMH48rF/fPX1pzwxHkiRJUhcYObL6ZXTr18OECfD666W3pHYYjiRJkqQu8tnPwqhRxfqjj1afuEHdy3AkSZIkdZE+feC++2DQoOLYVVdlkzSo5zAcSZIkSV3o0ENh/vxifefObHrvjRvL70nVGY4kSZKkLnb66TBjRrG+dm22QGxK5fekIsORJEmSVILrr4dTTinWly6FWbPK70dFhiNJkiSpBH37ZtN7DxhQHLv8cli1qvye9PsMR5IkSVJJjjgiWyC20rZtMHYsvPpq+T3pdwxHkiRJUonOPhumTi3W16yBKVO8/6g7GY4kSZKkkt18Mxx/fLG+cCHMnVt+P8rUFI4i4oyIeC4i1kTEFVXGD4+IxyLi6Yh4NiJG5/X3R8TKiPh+/n1Um30ez4/5TP715s57WpIkSVLP1dQEixdDc3NxbNo0aG0tvyfVEI4iog9wB/AB4FigJSKOrdjsc8CSlNIJwDjgy3n9V8CHUkrHAZ8CKmd4PzeldHz+9dI+PA9JkiSprhx9NHzlK8X6li3Z/UebN5ffU29XyydHJwFrUkovpJS2AYuAD1dsk4D++c8HAT8HSCk9nVL6eV5vBZoiot++ty1JkiTVv3PPzdY5qtTaCtOnl99Pb1dLOBoMrG3zeF1ea+saYHxErAMeBqZVOc5HgadTSlvb1O7NL6m7KiKi2i+PiMkRsSIiVmzYsKGGdiVJkqT6MWsWDB9erM+ZA4sWld9Pb1ZLOKoWWirn0GgB5qWUhgCjgfkR8dtjR8TbgJnAZ9rsc25+ud3I/GtCtV+eUpqdUhqRUhoxaNCgGtqVJEmS6kdzc3b/UVNTcWzy5GwWO5WjlnC0DjiszeMh5JfNtXE+sAQgpfQk0AQMBIiIIcD9wCdTSs/v2iGl9LP8+yZgAdnle5IkSVKvc9xxcMstxfqmTTBuHGzdWhxT56slHD0FDIuIoRFxANmEC0srtnkROBUgIoaThaMNETEA+BZwZUpp+a6NI2L/iNgVnvoCZwI/2NcnI0mSJNWryZNhzJhifeVKuKIwX7S6QrvhKKW0A5gKPAL8kGxWutaIuC4izso3uxSYFBHfAxYCE1NKKd/vKOCqiim7+wGPRMSzwDPAz4A5nf3kJEmSpHoRAbNnw5FHFsduuQWWVn48oU4XqY6W4B0xYkRasWJFd7chSZIkdZmnnoI//VPYvv336wcfDM88A4cdVn0/ZSJiZUppREf2rWkRWEmSJEnlOPFEuPHGYn3jRmhpgR07yu+ptzAcSZIkST3MJZfABz9YrC9fDtdcU3o7vYbhSJIkSephImDePBhcuboocMMNsGxZ6S31CoYjSZIkqQcaOBAWLoT9Kt6xpwTjx8P69d3TVyMzHEmSJEk91MiR1S+jW78eJkyA118vvaWGZjiSJEmSerDPfhZGjSrWH30UZs4sv59GZjiSJEmSerA+feC++2DQoOLYVVdlkzSocxiOJEmSpB7u0ENh/vxifefObHrvjRvL76kRGY4kSZKkOnD66TBjRrG+di2cd142UYP2jeFIkiRJqhPXXw+nnFKsL10Ks2aV30+jMRxJkiRJdaJv32x67wEDimOXXw6rVpXfUyMxHEmSJEl15Igj4J57ivVt22DsWNi0qfyeGoXhSJIkSaoz55wDU6cW62vWwGc+4/1HHWU4kiRJkurQzTfD8ccX6wsXwty55ffTCAxHkiRJUh1qaoLFi6G5uTg2bRq0tpbfU70zHEmSJEl16uij4a67ivUtW7L7jzZvLr+nemY4kiRJkurY+PEwcWKx3toK06eX3k5dMxxJkiRJde722+GYY4r1OXNg0aLy+6lXhiNJkiSpzjU3w5Il2X1IlSZPzmaxU/sMR5IkSVIDOO44uOWWYn3TJhg3DrZuLb+nemM4kiRJkhrE5Mnw8Y8X6ytXwhVXlN9PvTEcSZIkSQ0iIrvPaOjQ4tgtt8BDD5XfUz0xHEmSJEkN5KCDsvWP+vYtjk2cCGvXlt5S3TAcSZIkSQ3mxBPhxhuL9Y0boaUFduwov6d6YDiSJEmSGtAll8AHP1isL18O11xTejt1wXAkSZIkNaAImDcPBg8ujt1wAyxbVnpLPZ7hSJIkSWpQAwfCggWwX8W7/pRg/HhYv757+uqpDEeSJElSA3vPe6pfRrd+PUyYAK+/XnpLPZbhSJIkSWpwn/0sjBpVrD/6KMycWX4/PZXhSJIkSWpwffrAfffBoEHFsauuyiZpkOFIkiRJ6hUOPRS++tVifefObHrvjRvL76mnMRxJkiRJvcQZZ8Bf/3WxvnYtnH9+NlFDb2Y4kiRJknqRL3wBTj65WH/gAbj99vL76UkMR5IkSVIv0rcvLFoEAwYUxy67DFatKr+nnsJwJEmSJPUyRxwB99xTrG/bBmPHwqZN5ffUExiOJEmSpF7onHPgwguL9TVrYMqU3nn/keFIkiRJ6qX+9m/h+OOL9QUL4N57y++nuxmOJEmSpF6qqQkWL4bm5uLY1KmwenX5PXUnw5EkSZLUix19NNx1V7G+ZQuMGQObN5ffU3epKRxFxBkR8VxErImIK6qMHx4Rj0XE0xHxbESMbjN2Zb7fcxFxeq3HlCRJklSO8eNh4sRivbUVpk8vvZ1u0244iog+wB3AB4BjgZaIOLZis88BS1JKJwDjgC/n+x6bP34bcAbw5YjoU+MxJUmSJJXk9tvhmGOK9TlzskvveoNaPjk6CViTUnohpbQNWAR8uGKbBPTPfz4I+Hn+84eBRSmlrSmlHwNr8uPVckxJkiRJJWluzkJQU1NxbNIkeP758nsq2/41bDMYWNvm8Trg3RXbXAP834iYBjQD72uz73cr9h2c/9zeMQGIiMnA5Pzh1oj4QQ09S51hIPCr7m5CvYbnm8rmOacyeb7VuU2b4KijuruLmr21ozvWEo6iSq1y1vMWYF5K6f9ExCnA/Ih4+x72rfaJVdWZ1FNKs4HZABGxIqU0ooaepX3m+aYyeb6pbJ5zKpPnm8oUESs6um8t4WgdcFibx0P43WVzu5xPdk8RKaUnI6KJ7C8Ee9q3vWNKkiRJUmlquefoKWBYRAyNiAPIJlhYWrHNi8CpABExHGgCNuTbjYuIfhExFBgG/HuNx5QkSZKk0rT7yVFKaUdETAUeAfoAc1NKrRFxHbAipbQUuBSYExGXkF0eNzGllIDWiFgCrAZ2ABemlHYCVDtmDf3O3vunKHWY55vK5PmmsnnOqUyebypTh8+3yDKMJEmSJPVuNS0CK0mSJEmNznAkSZIkSfTQcBQRZ0TEcxGxJiKuqDI+JSK+HxHPRMS/RMSx3dGnGkN751ub7T4WESkinIpUHVbD69vEiNiQv749ExEXdEefagy1vL5FxJiIWB0RrRGxoOwe1ThqeH37UpvXtv+MiF93R59qHDWcc4dHxGMR8XREPBsRo9s9Zk+75ygi+gD/CbyfbCrwp4CWlNLqNtv0Tym9mv98FvCXKaUzuqNf1bdazrd8uz8AvgUcAExNKXV4/nz1XjW+vk0ERqSUpnZLk2oYNZ5vw4AlwKiU0isR8eaU0kvd0rDqWq3/nrbZfhpwQkrp0+V1qUZS42vcbODplNKd+YcpD6eU3rKn4/bET45OAtaklF5IKW0DFgEfbrvBrmCUa2Y3C8hKNWj3fMtdD9wE/FeZzanh1Hq+SZ2hlvNtEnBHSukVAIOR9sHevr61AAtL6UyNqpZzLgH9858PooZ1VXtiOBoMrG3zeF1e+z0RcWFEPE/2hvWiknpT42n3fIuIE4DDUkrfLLMxNaSaXt+Aj+Yf/389Ig6rMi7Vopbz7Wjg6IhYHhHfjQivwlBH1fr6RkQcAQwF/qmEvtS4ajnnrgHGR8Q64GFgWnsH7YnhKKrUCp8MpZTuSCn9D2AG8Lku70qNao/nW0TsB3yJbC0vaV/V8vr2EPCWlNI7gGXA33d5V2pUtZxv+5Mt0P5esr/k3x0RA7q4LzWmmt6/5cYBX9+19qXUQbWccy3AvJTSEGA0MD9/b7dbPTEcrQPa/qV0CHv+CGwRcHaXdqRG1t759gfA24HHI+InwMnAUidlUAe1+/qWUno5pbQ1fzgHeFdJvanx1PLv6TrgwZTS9pTSj4HnyMKStLf25v3bOLykTvuulnPufLL7KkkpPQk0AQP3dNCeGI6eAoZFxNCIOIDsf6ClbTfIbyDd5YPAj0rsT41lj+dbSuk3KaWBKaW35DfwfRc4ywkZ1EG1vL4d2ubhWcAPS+xPjaXd8w14APhfABExkOwyuxdK7VKNopbzjYh4K/CHwJMl96fGU8s59yJwKkBEDCcLRxv2dND9u6DRfZJS2hERU4FHgD7A3JRSa0RcB6xIKS0FpkbE+4DtwCvAp7qvY9WzGs83qVPUeL5dlM/CuQPYCEzstoZV12o83x4BTouI1cBO4PKU0svd17Xq1V78e9oCLEo9bbpk1Z0az7lLgTkRcQnZJXcT2zv3etxU3pIkSZLUHXriZXWSJEmSVDrDkSRJkiRhOJIkSZIkwHAkSZIkSYDhSJIkSZIAw5EkaS9ExOMR0aOnOY2I90ZEiohruuj41+THf29XbC9J6j6GI0nS7+nqcCFJUk9lOJIkSZIkDEeSJEmSBBiOJElt5JfSPZY/vDq/vC5V3mcUEQdExBci4sWI2BoRrRHRUuV48/L9j4qI/x0RayJie0RMb7PN4Ii4MyJ+mh/r5xExOyIOqXK80yJiWUSsj4j/yrf9TkS8fzfP5+SIeCwiXouIjRHxtYgYVGW7iIgpEbEqIjZHxG8i4p8i4vS9+G/3BxFxW0T8Mj/GkxFxaq37S5K63/7d3YAkqUd5HHgL8CngifxxNYuAE4Bvkv1b0gIsiIhfp5S+XWX7O4Dj8+1/DawDiIi35r9nYD72I+Ao4ALg/RFxYkrpV/m2ZwEPAr8A7gdeAQ4F/gQ4HXi04neeBMzI63cBfwp8AjgyIv4kpdQ28N0FTAZeAO4E3gCMBb4dEReklObu5r8DeW8TtQq8AAAD0ElEQVR9gG8BI4F/IwuYRwIP589PklQHDEeSpN9KKT0eEZCFo8dTStfsZtNDgONSSq8BRMTXyILUdKBaODoaOD6l9IuK+t8DA4CRKaUndxUj4iPAPwLXAX+ZlycC2/LjvNT2IBFxcJXf+QHgYymlb+Tb7EcWlEYBJwNP5vVRZMFoBfDnKaXNef1GYBVwe0Q8lFLasJv/FgCfJgtGi4GWXcErIj6ZP0dJUh3wsjpJUkdcuSsYAaSUngB+AozYzfZ/WxmMIuJdwLuBu9oGo/x495OFlTEVx9lOFpCo2H5jld/5+K5glG/zOvDV/GHbPifk3z+/Kxjl278I3Eb2KdLHd/O8djkXSMBVFZ9IzQf+o519JUk9hJ8cSZI64ukqtZ8Bh+9m+xVVaifl3w/fzbThbwTeFBED80vrFgMfAb4fEQvILl1bnlLatJc9QvZp1S7/M//+/6ps/0TFNrvzDuCllNKP2hZTSiki/hU4pp39JUk9gOFIkrTXUkqvVinvYPdXJLxUpbbrUriP5F+70wz8KqW0OCJ2AJcClwF/DWyLiAeA6VUu2dtdjwB92tT6A6+llP5/le3Xt9lmT/oDP9zNWLXnLknqgbysTpJUhlSltiu8TEwpxR6+fvrbg6T0jZTSnwCDyALVN8kuvVu8D729ChwYEW+sMvZHFb3u6RiFWfByb+5oY5KkchmOJEmVdubf++xxq3337/n3k/d2x5TSxpTSAymlj5LNDjcyIga0t99ufC///p4qYyPz78+0c4xngTdHxLC2xchmtzilg31JkkpmOJIkVdo1ucHgrvwlKaV/I7sX6YJq6wlFxBsi4t1tHo+KiH4V2/Qju39oB7+7ZG5vzc+/XxsRTW2OPQS4GNgCfL2dY3wNCOD6PBDtMgEY3sG+JEkl854jSVKl58jWEmqJiK3kkxiklL7QBb/rE2QTK3wnIh4j+xRnP7K1lv4c+C5wRr7t3wGDI+IJ4MdAX7L1jd5KNuPda3RASumfImIOMIlssocH+d06RwcDk9qZxhtgLvDJfJ+35M/lSOBssunDqy5SK0nqWQxHkqTfk1LaEREfA2aSffLRnA91ejhKKf0oIk4gm1zhLLIFXf+LbJHY+fxu6m2AG4GPAu8CRgObyRaNPR+Yt4+tfIZsdrvJwIVkU4avBGamlL5Tw/PYGRGjgRvIAtI7yILeaLJL8wxHklQH4veXY5AkSZKk3sl7jiRJkiQJw5EkSZIkAYYjSZIkSQIMR5IkSZIEGI4kSZIkCTAcSZIkSRJgOJIkSZIkwHAkSZIkSYDhSJIkSZIA+G8SEA/vi48w2gAAAABJRU5ErkJggg==\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": 28,
   "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": [
    "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[273,  54],\n",
       "       [ 66, 141]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "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": "markdown",
   "metadata": {},
   "source": [
    "### data v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "import seaborn as sns\n",
    "survived = 'survived'\n",
    "not_survived = 'not survived'\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))\n",
    "women = data[data['Sex']=='female']\n",
    "men = data[data['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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nemgeree armanonah\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f17eb0f748>"
      ]
     },
     "execution_count": 31,
     "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(data, row='Embarked', size=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": "markdown",
   "metadata": {},
   "source": [
    "#### Embarked seems to be correlated with survival, depending on the gender.\n",
    "Women on port Q and on port S have a higher chance of survival. The inverse is true, if they are at port C. Men have a high survival probability if they are on port C, but a low probability if they are on port Q or S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "sns.barplot('Pclass', 'Survived', data=data, color=\"darkturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "sns.barplot('Embarked', 'Survived', data=data, color=\"teal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "sns.barplot('Sex', 'Survived', data=data, color=\"aquamarine\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0\n",
      " 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0\n",
      " 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0\n",
      " 1 0 0 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 0 0 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1\n",
      " 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0\n",
      " 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0\n",
      " 0 0 0 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1\n",
      " 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 0]\n",
      "Accuracy:  0.803921568627451\n"
     ]
    }
   ],
   "source": [
    "print(clf.predict(X_test))\n",
    "print(\"Accuracy: \",clf.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
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   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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