main_notebook.ipynb

{
 "cells": [
  {
   "source": [
    "\n",
    "\n",
    "<center><img src=\"titanic.jpg\"/></center>\n",
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Titanic - Machine Learning from Disaster"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from scipy import stats\n",
    "import pandas_profiling as pp\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')\n",
    "#y_Test = pd.read_csv('gender_submission.csv') this is sample submission csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape ,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del train['Ticket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, the test data has Age column missing 177 values, Embarked column missing 2 values and Cabin columnn missing 687 values, also the test data has Age column missing 86 values, fare cabin missing 1 value and Cabin column missing 327 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  1           233\n",
       "        0            81\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Sex').Survived.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800px\"\n",
       "            src=\"ipynb_tmp\\profile_1013461908.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1953821e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.ProfileReport(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Sex   \n",
       "0         female     81\n",
       "          male      468\n",
       "1         female    233\n",
       "          male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(['Survived','Sex'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above we can say that women are more likely to survive then men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2 , figsize =(15,5))\n",
    "men = train['Survived'][train['Sex']=='male'].value_counts()\n",
    "female = train['Survived'][train['Sex'] =='female'].value_counts()\n",
    "label = ['Dead' , 'Survived']\n",
    "ax1.pie(men, explode=[0,0.2] ,labels =label ,autopct ='%1.1f%%',  shadow= True)\n",
    "ax1.set_title('Male')\n",
    "ax2.pie(female , explode=[0,0.2], labels =label[::-1], autopct='%1.1f%%',  shadow=True)\n",
    "ax2.set_title('Female')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pie chart, it's confermed more females survived then men."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1902177b00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=train.Sex , y=train.Age , hue =train.Survived)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_chart(feature):\n",
    "    sns.barplot(x=train['Sex'] , y=train[feature], hue=train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEJCAYAAAB2T0usAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAWLUlEQVR4nO3de5RcZZnv8W8n3SGQG7ceuUVYDskDCIcRL0QFBI5G5CYCSzg5XANEaGHaCRcBxbSzVGaUO4EcmzMdFeHoYpSZiSAQGfQQLh50nZGA8AzBBRyyHKeNkHRCLh1S54+qhEpMuquT2l1J5/tZi5V+97tr11PN7v71++5dbzWVSiUkSdu3YY0uQJLUeIaBJMkwkCQZBpIkDANJEoaBJAlobnQBm6u7u8d7YiVpgFpbxzRtbLsjA0mSYSBJKnCaKCKGA3cBAbwNnJ+ZL1f1nwR8BVgNdGXmXUXVIknqW5Ejg5MAMvOjlH/p37S2IyJagJuBycDHgGkRsUeBtUiS+lBYGGTmPwHTKs19gT9UdR8ILMjMNzJzFTAPOLKoWiRJfSv0bqLMXB0R3wU+A5xe1TUWWFzV7gHG9Xe8iOgAZgC0tbXR3t5ev2IlaTtW+K2lmXluRHwR+GVEHJSZy4AlwJiq3cYAb9ZwrA6gA8q3lnZ399S/YEkawlpbx2x0e5EXkM8G9snM64G3gDWULyQDvABMiIhdgaXAUcANRdUiSepbkReQfwy8LyL+N/Aw8AXg1IiYlpm9wPTK9qco3020sMBaJA1AV1cnZ555Cl1dnY0uZav19ttvc8cdt/I3f/N5vvCFNr7xja+ycuXKAR9n0aI/cuedt252HVOmnLbZj63WtK1+uI3vQJaKsWLFcs4/fwqlUommpmHMnn0PI0fu2OiytjpPPPE4Tz01jyuuuAaAmTNvYZ99xnPKKfX55VyrKVNO4957f1Tz/r4DWVJNent7WftHYqm0ht7e3gZXtHX6i794F/PnP8uTT85jxYoVXHzxpXzwg4czffpl6/ZZ+1f71KlnceWV7dxww9/R1nbhuv6LLjqH3/1uAdOnX8YvfvEYs2bdDpRHC+3tl7By5Uo6Or7EpZdOY/r0y/jjH7sBuP32m5g27Tyuv/5v6/Z6DANJ2gwTJkzk0kvbmTPnfk4//SS+9KUreeONP2103yVLFjN9+he54oqrGTNmDAsXvs5LLyXvfve+7LjjTgB85CNH8Mwzv6RUKvHoo4/wiU98kjlz7mfChInMnNnJWWedS1dXJy+99O8sXPg6nZ3f4bTTPlu317PNLlQnSY308ssL2H//iVx//Y2sXr2a73//O3R23klzcwsA1VPwzc0t7LnnXgAcd9wJzJ37EMuWLeO4405Yt09LSwsHH3wIzz77G37xi8f45jdvZtasmTz//LM89dQTAIwdO47XXnuFiRMPAGDixAPYYYcd6vJ6DANJ2gzPPPM0Cxcu5PLLv0hzczP77z+B7u7/5Le/fQ6Al17KdfsOG/bONP1HP3oUP/7xfaxe3csll1zGH/7wH+v6jjvuBO6+eza7797KqFGjGT9+PPvvP4FTTjmN1157lfnzf8M++7ybhx56AIBXX32FVatW1eX1GAaStBlOO+0Mbr31Bs47bwo77jiSnXfehauu+hKzZt3OtGnnMWHCRMaN2/nPHjdixAj23Xc/dtppJ4YNW3+m/qCDDua1117l0ku/AMDJJ5/KN77xVX72s4dZuXIll1/+RSIO4JBDDuWii85l3333WzfNtKW8m0jaCh33zfsb9txNq1cw7tkfrmsv/i9nUGoe2bB6HrrqMw177qHIu4kkSZtkGEiSDANJkmEgScIwkCRhGEiS8H0GkrTZ6n0LcC230a5Zs4Ybb/w7Fix4iZaWFq6++jr22Wf8Fj+3IwNJ6yk1DWftm3hKNFFqGt7QerS+xx//OatWreLb357NxRdfxsyZN9fluIaBpPUNb2FVawCwqnUiDG9pcEGq9uyz/8bhh38YgIMPPoQXX3yhLsd1mkjSn1k+fhLLx09qdBnaiGXLljFq1Oh17WHDhrF69Wqam7fs17kjA0nahowaNYq33nprXbtUKm1xEIBhIEnblEMOOZSnny4vaf3cc/N5z3v2r8txnSaSpG3IUUcdwzPP/JKLL55KqVTi2mtn1OW4hoEkbaZGrKg6bNgwrrzy2voft+5HlCRtcwwDSZJhIEkyDCRJGAaSJAwDSRLeWtpQXV2dPPLIg0yefDxTp05rdDmSBmj5rI/U9Xg7XvJkzfs+//xzzJp1GzNndtbluQsJg4hoAbqA/YAdgK9l5r9U9U8HLgC6K5s+l5lZRC1bqxUrljN37k8BmDv3IaZMOZuRI3dscFWStgX33PNdHn74wbr+zihqmugsYFFmHgl8Cpi5Qf9hwDmZeXTlv+0qCAB6e3splcoLBZdKa+jt7W1wRZK2FXvvvQ9f//q36nrMosLgPuC6qvbqDfrfD1wTEfMi4pqCapCkIenoo/9rXRanq1bINFFmLgWIiDHAPwJf3mCXHwB3AEuA+yPixMz8SX/HjYgOYAZAW1sb7e3t9Sx7UI0YsWa99m67jWbcuDENqkbaerW2br0/F6/V+XgDea0rV46ipWV43b4/hV1AjojxwP3AnZl5b9X2JuCWzFxcaT8AvA/oNwwyswPoAOju7il1d/fUv/BB0tOzdL32okVLWbXKm7ukDW3LP+cDNZDX+qc/LaO39+0Bf382FR5FXUB+F/AIcGlmPrpB91jguYg4EFgGHEv5YvOgqvdnlw5U0+oVjKtqf/b2Byg1j2xILY1YbEvS1qWokcG1wC7AdRGx9trBXcCozOyMiGuBx4CVwKOZ+WBBdUhSYQZyK2i97bnnXnR2fqduxyvqmkE7sMkJ/cy8G7i7iOeWJA2ck9SSJMNAkmQYNEypaTiltV/TRKlpeEPrkbR9MwwaZXgLq1oDgFWtE2F4S4MLkrQ9c6G6Blo+fhLLx09qdBmS5MhAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgG2kp0dXVy5pmn0NXV2ehSpO2SYaCGW7FiOXPn/hSAuXMfYsWK5Q2uSNr+GAZquN7eXkql8qc7lEpr6O3tbXBF0vbHMJAkGQaSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkoLmIg0ZEC9AF7AfsAHwtM/+lqv8k4CvAaqArM+8qog5J2lJdXZ088siDTJ58PFOnTmt0OYUpamRwFrAoM48EPgXMXNtRCYqbgcnAx4BpEbFHQXVI0mbbnpZKKWRkANwH/GNVe3XV1wcCCzLzDYCImAccWXmMGmD5rI809PlX9DYBre+0Z3+K5pZSQ2rZ8ZInG/K82jptbKmUkSN3bHBVxSgkDDJzKUBEjKEcCl+u6h4LLK5q9wDjajluRHQAMwDa2tpob2+vR7nSOq2tYxpdgjbQyP8nI0asWa+9226jGTduaJ4jRY0MiIjxwP3AnZl5b1XXEqD6uzkGeLOWY2ZmB9AB0N3dU+ru7qlLrdJanlNbn0b+P+npWbpee9GipaxatW3fd7OpcC3qAvK7gEeASzPz0Q26XwAmRMSuwFLgKOCGIuqQJNWmqJHBtcAuwHURcV1l213AqMzsjIjpwMOUL2B3ZebCguqQJNWgqGsG7cAmJ/Qzcw4wp4jnliQN3LY9+SVJqovCLiBLUj008tbnrem2Zyj21mdHBpIkw0CSZBhIkjAMJEkYBtoKNA+DJsoX5Zoo0exZKQ06f+zUcCOHlzhmr/JqkMfstZyRwxt3t4a0vfLWUm0Vzp6wlLMnLO1/R2kQrR21lmga8qPWIfzSJGnLbE+jVkcGktSH7WXU6shAkmQYSJIMA0kSNV4ziIi/BCYB9wLfBt4HXJKZvyqwNknSIKl1ZDC7su/JwERgOnBbUUVJkgZXrWEwMjPvBk4C7snMx4EdiitLkjSYag2DtyPiNOBE4CcR8Wng7eLKkiQNplrDYBpwAvD5zPw98N+ACwurSpI0qGoKg8ycD3wpM38UEUcCjwMvFVqZJGnQ1BQGETEL+FpEHET5jqLDgLuKLEySNHhqnSb6EOVpoc8C/5CZFwBRWFWSpEFVaxgMr+z7aeCnEbETMKqwqiRJg6rWMPge8Hvglcz8JfAroLOwqiRJg6rWC8g3AXtk5mcqm47KzFuKK0uSNJhqXY5iEnBNRIwGmoDhEbFvZu5XZHGSpMFR6zRRF/BPlMPjDuB14P6iipIkDa5aP9xmZWbOjoj9gDeAc4D5/T0oIg4H/j4zj95g+3TgAqC7sulzmZm1Fi1Jqq9aw2BFROwKJDApM/81Iob39YCIuAo4G1i2ke7DgHMy89cDqlaSVIhap4luAn4IzAHOjojngf5+kb8MnLqJvvdTvgYxLyKuqbEGSVJBar2b6D5gcmb2AB8AzgL+ez+P+RHQu4nuHwAXA8cCR0TEiTVXLEmquz6niSJiNlCqam+4y9SBPmFENAG3ZObiSvsByh+W85MaHtsBzABoa2ujvb19oE8v9am1dUyjS5A2qcjzs79rBj8v4DnHAs9FxIGUryccS/lupX5lZgfQAdDd3VPq7u4poDxtzzyntDWrx/m5qUDpc5ooM7+bmd8FfgyMrnz9M+AvgfsGUkBETImIaZURwbXAY5RXP30+Mx8cyLEkSfVV691E9/DOraQ9lEPkbuC0vh6Uma9Q/uxkMvPequ13Vx4vSdoK1BoG+2bmyQCZuQT4ckT8W3FlSZIGU623lpYi4pC1jYg4gE3fKSRJ2sbUOjK4HJgbEa9X2q2Uby+VJA0B/d1auhdwA/Be4AHgfwArgczMlcWXJ0kaDP2NDGZTvnB8D3A60JaZ5xdelSRpUPUXBntn5icBIuIRwIvGkjQE9XcBedXaLzKzt7otSRo6ar2baK1S/7tIkrY1/U0TvTciflfV3rvSbgJKmfme4kqTJA2W/sJg4qBUIUlqqD7DIDNfHaxCJEmNM9BrBpKkIcgwkCQZBpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkCg6DiDg8In6+ke0nRcQzEfFURFxUZA2SpP4VFgYRcRXwP4GRG2xvAW4GJgMfA6ZFxB5F1SFJ6l+RI4OXgVM3sv1AYEFmvpGZq4B5wJEF1iFJ6kdzUQfOzB9FxH4b6RoLLK5q9wDjajlmRHQAMwDa2tpob2/fwiql9bW2jml0CdImFXl+FhYGfVgCVL+iMcCbtTwwMzuADoDu7p5Sd3dPvWvTds5zSluzepyfmwqURoTBC8CEiNgVWAocBdzQgDokSRWDFgYRMQUYnZmdETEdeJjyNYuuzFw4WHVIkv5coWGQma8Akypf31u1fQ4wp8jnliTVzjedSZIMA0mSYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJApqLOnBEDAPuBA4FVgIXZuaCqv7pwAVAd2XT5zIzi6pHkrRphYUBcAowMjM/HBGTgBuBT1f1Hwack5m/LrAGSVINipwmOgJ4CCAznwY+sEH/+4FrImJeRFxTYB2SpH4UOTIYCyyuar8dEc2ZubrS/gFwB7AEuD8iTszMn/R1wIjoAGYAtLW10d7eXv+qtV1rbR3T6BKkTSry/CwyDJYA1ZUPWxsEEdEE3JKZiyvtB4D3AX2GQWZ2AB0A3d09pe7unvpXre2a55S2ZvU4PzcVKEVOEz0BHA9QuWYwv6pvLPBcRIyuBMOxgNcOJKlBihwZ3A98IiKeBJqA8yNiCjA6Mzsj4lrgMcp3Gj2amQ8WWIskqQ+FhUFmrgEu3mDzi1X9dwN3F/X8kqTa+aYzSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJQHNRB46IYcCdwKHASuDCzFxQ1X8S8BVgNdCVmXcVVYskqW9FjgxOAUZm5oeBq4Eb13ZERAtwMzAZ+BgwLSL2KLAWSVIfigyDI4CHADLzaeADVX0HAgsy843MXAXMA44ssBZJUh8KmyYCxgKLq9pvR0RzZq7eSF8PMK6/A0ZEBzCj0vxqZnZsbnG//tY5m/vQISfi6x1b8r1U/Xl+vsPzc3AUGQZLgDFV7WGVINhY3xjgzf4OWDkhOupUn94xA7+v2np5fg6CIqeJngCOB4iIScD8qr4XgAkRsWtEjACOAp4qsBZJUh+KHBncD3wiIp4EmoDzI2IKMDozOyNiOvAw5UDqysyFBdYiSepDYWGQmWuAizfY/GJV/xxgTlHPrwH5aqMLkPrg+TkImkqlUqNrkCQ1mO9AliQZBpIkw0CShGEgScIwkCRR7PsMtA2KiPOAAzLz6kbXoqEjIoYDDwKjgJMy8406Hfc/MtNFLuvAMJA0GPYEds/M9ze6EG2cYTCEVf7KPwnYkfIP463Ap4GDgSuA8cCpQAvlhQNP3eDxlwFTgBLwg8y8bbBq15DTSXkJmtmU1yLbrbL9rzNzfkQsAJ4EJgD/Snnhyg8BmZlnR8TBwE2Up7Z3rjzuybUHj4hDgNsor3awCJiamdWLYaofXjMY+sZk5vHA3wOXUP6FPw24gPIP5Mcz80jKgfDBtQ+KiIOAMygvRX4EcEpExCDXrqGjDfgt8J/Ao5l5DOXzcFalfz/gy5TXKftryh+MdThwRETsDLwXuDwzP045FM7f4Ph3AZ/PzKMpT0ddVeSLGYocGQx9/7fy75vAC5lZiog3gBHAKuB/RcRSYB/KgbDWwcC+wKOV9i7A/kAOStUaqg4Bjo2IMyrtXSr/LsrM1wAiYllm/rby9WJgJLAQuC4illMeWSzZ4LgHAndW/l5pAf690FcxBDkyGPo2td7ICOCUzDwDuIzyudBU1Z/A88Axlb+2vsP6K89Km+NF4ObKOfVZ4J7K9v7WxbkNmJGZ51I+D5s26E/gnMpxrwIeqFfB2wtHBtuv1cCyiPgV5c+o/j2w19rOzPxNRDwKzIuIHYD/Q/mvM2lLfB34h4iYRvlDrjpqfNz3gX+OiD8ArwO7b9B/CfC9yl1LUJ4G1QC4UJ0kyWkiSZJhIEnCMJAkYRhIkjAMJEl4a6k0YBFxOnAN5Z+fYcD3MvNbja1K2jKODKQBiIi9gRuByZl5KPBh4MyIOLmxlUlbxpGBNDC7U17uYCfKSygsjYhzgRUR8UHg5krfH4HPVf6dD1yQmY9GxMPAP2fmnY0pX9o433QmDVBEzAIupLzu02PAvcALwDOU1+p/LSI+CVyZmR+PiGMpL8h2G3BiZn6qQaVLm2QYSJuhMl00Gfgk5WXBr6e8Js6Cqt3GZuZ7KvvPorwc+AGZ+ftBLlfql9NE0gBExAnA6Mz8ITAbmB0RF1H+Rf+7zPyryn7DgXdVvm4CAnir8q9hoK2OF5ClgXkLuD4i9oN1v+j/Cnga2DUijqzsN5Xy9BGU1/JfSnkEcVdEjB7UiqUaOE0kDVDlgvGVvPP5Dw9X2odR/jS5kZTX2z8XWEP5E7w+lJn/LyJmAsMys23QC5f6YBhIkpwmkiQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJKA/w+YGToc349S0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1901fe5978>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=train['Sex'] , y=train['Fare'], hue=train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1900a06438>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=train['Sex'] , y=train['SibSp'], hue=train['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_graph(feature):\n",
    "    survived = train[train['Survived']==1][feature].value_counts()\n",
    "    dead = train[train['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived','Dead']\n",
    "    df.plot(kind='bar' ,figsize=(12,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_graph('Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_graph('SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_graph('Parch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_graph('Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matx = train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived       1.000000\n",
       "Fare           0.257307\n",
       "Parch          0.081629\n",
       "PassengerId   -0.005007\n",
       "SibSp         -0.035322\n",
       "Age           -0.077221\n",
       "Pclass        -0.338481\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matx['Survived'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['title'] = train.Name.str.extract('\\, ([A-Z][^ ]*\\.)',expand=False) \n",
    "test['title'] = test.Name.str.extract('\\, ([A-Z][^ ]*\\.)',expand=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr.          517\n",
       "Miss.        182\n",
       "Mrs.         125\n",
       "Master.       40\n",
       "Dr.            7\n",
       "Rev.           6\n",
       "Major.         2\n",
       "Col.           2\n",
       "Mlle.          2\n",
       "Ms.            1\n",
       "Lady.          1\n",
       "Sir.           1\n",
       "Capt.          1\n",
       "Jonkheer.      1\n",
       "Mme.           1\n",
       "Don.           1\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dict = dict(train['title'].value_counts())\n",
    "test_dict = dict(test['title'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "title1 = train['title'].tolist()\n",
    "title2 = test['title'].tolist()\n",
    "name_list = []\n",
    "name_list2 = []\n",
    "\n",
    "for x in title1:\n",
    "    name= train_dict.get(x)\n",
    "    if name is None or name <= 20:\n",
    "        name_list.append('other')\n",
    "    else:\n",
    "        name_list.append(x)\n",
    "\n",
    "for x in title2:\n",
    "    name = train_dict.get(x)\n",
    "    if name is None or name <= 20:\n",
    "        name_list2.append('other')\n",
    "    else:\n",
    "        name_list2.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['titles'] = name_list\n",
    "test['titles'] = name_list2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.replace({'Sex':{'male':0, 'female':1 }} ,inplace = True)\n",
    "test.replace({'Sex':{'male':0 ,'female':1}}, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
       "title            1\n",
       "titles           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "title            0\n",
       "titles           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
    "train[\"Age\"].fillna(train.groupby(\"titles\")[\"Age\"].transform(\"median\"), inplace=True)\n",
    "test[\"Age\"].fillna(test.groupby(\"titles\")[\"Age\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
       "      <th>title</th>\n",
       "      <th>titles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked, title, titles]\n",
       "Index: []"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Age'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
       "      <th>title</th>\n",
       "      <th>titles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked, title, titles]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['Age'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filling missing values in Embarked Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2 ,ax3) = plt.subplots(1,3, figsize= (15,5))\n",
    "class_1 = train['Embarked'][train['Pclass'] == 1].value_counts()\n",
    "class_2 = train['Embarked'][train['Pclass'] == 2].value_counts()\n",
    "class_3 = train['Embarked'][train['Pclass'] == 3].value_counts()\n",
    "t = train['Embarked'].value_counts()\n",
    "label = t.index.values\n",
    "ax1.set_title('1st Class')\n",
    "ax1.pie(class_1 ,labels = label , autopct='%1.1f%%')\n",
    "ax2.set_title('2nd Class')\n",
    "ax2.pie(class_2 ,labels= label ,autopct='%1.1f%%')\n",
    "ax3.set_title('3rd Class')\n",
    "ax3.pie(class_3 ,labels = label, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can say that from every class most of the people embarked from S port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Embarked'].fillna('S', inplace =True)\n",
    "test['Embarked'].fillna('S', inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['FamilySize'] = train['SibSp']+train['Parch']\n",
    "test['FamilySize'] = test['SibSp']+test['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0,15,30,45,60,100]\n",
    "labels = [1,2,3,4,5]\n",
    "Ages_1 = pd.cut(train['Age'],bins , labels = labels)\n",
    "Ages_2 = pd.cut(test['Age'], bins, labels = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_4 = pd.get_dummies(Ages_1)\n",
    "one_hot_5 = pd.get_dummies(Ages_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.join(one_hot_4)\n",
    "test = test.join(one_hot_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId',    'Survived',      'Pclass',        'Name',\n",
       "               'Sex',         'Age',       'SibSp',       'Parch',\n",
       "            'Ticket',        'Fare',       'Cabin',    'Embarked',\n",
       "             'title',      'titles',  'FamilySize',             1,\n",
       "                   2,             3,             4,             5],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId',      'Pclass',        'Name',         'Sex',\n",
       "               'Age',       'SibSp',       'Parch',      'Ticket',\n",
       "              'Fare',       'Cabin',    'Embarked',       'title',\n",
       "            'titles',  'FamilySize',             1,             2,\n",
       "                   3,             4,             5],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One Hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = train[['Pclass','Sex', 'Embarked', 'titles']]\n",
    "x2 = test[['Pclass','Sex', 'Embarked', 'titles']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot = OneHotEncoder(sparse= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat = one_hot.fit(x1)\n",
    "x_1 = feat.transform(x1)\n",
    "x_2 = feat.transform(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0_1', 'x0_2', 'x0_3', 'x1_0', 'x1_1', 'x2_C', 'x2_Q', 'x2_S',\n",
       "       'x3_Master.', 'x3_Miss.', 'x3_Mr.', 'x3_Mrs.', 'x3_other'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "column1 =  train[['FamilySize', 1, 2, 3, 4, 5]].values\n",
    "column2 =  test[['FamilySize', 1, 2, 3, 4, 5]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 6)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = np.column_stack((x_1,column1))\n",
    "Test = np.column_stack((x_2,column2))\n",
    "target = train.pop('Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 19) (418, 19)\n"
     ]
    }
   ],
   "source": [
    "print(final.shape, Test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mode Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from prettytable import PrettyTable\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "xTrain , xTest , yTrain , yTest = train_test_split(final, target , test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Navie Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.75555556 0.74157303 0.80898876 0.7752809  0.76404494 0.78651685\n",
      " 0.76404494 0.80898876 0.79775281 0.79775281]\n"
     ]
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, xTrain, yTrain, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal value of  alpha is 94.76\n"
     ]
    }
   ],
   "source": [
    "alpha = {}\n",
    "indx = 1\n",
    "a = 0\n",
    "MSE= []\n",
    "a = 10**-2\n",
    "while a < 10**2:\n",
    "    NB = MultinomialNB(alpha = a , class_prior=None , fit_prior=True)\n",
    "    scores = cross_val_score(NB, xTrain, yTrain, cv =k_fold , scoring='accuracy')\n",
    "    error = 1 - scores.mean()\n",
    "    MSE.append(error)\n",
    "    alpha[error] = a\n",
    "    a+=0.05\n",
    "    indx  += 1\n",
    "\n",
    "opt_alpha = alpha.get(min(MSE))\n",
    "print('The optimal value of  alpha is {}' .format(round(opt_alpha,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_alpha = 0.21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Naive Bayes with Optimal alpha\n",
    "multiNB = MultinomialNB(alpha =opt_alpha, class_prior=None , fit_prior=True)\n",
    "multiNB.fit(xTrain, yTrain)\n",
    "pred = multiNB.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = accuracy_score(yTest, pred) * 100\n",
    "prec = precision_score(yTest, pred)  *100\n",
    "f1 = f1_score(yTest, pred) *100\n",
    "conf = confusion_matrix(yTest,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 82.08955223880598 , Precision is 88.1578947368421 and F1 score is 73.62637362637363 \n",
      " confusion matrix is\n",
      " [[153   9]\n",
      " [ 39  67]]\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy is {} , Precision is {} and F1 score is {}' .format(acc, prec, f1) , '\\n confusion matrix is\\n {}' .format(conf)  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+----------+-----------+----------+\n",
      "| Optimal Alpha | Accuracy | Precision | f1 score |\n",
      "+---------------+----------+-----------+----------+\n",
      "|     94.76     | 82.08955 |  88.15789 | 73.62637 |\n",
      "+---------------+----------+-----------+----------+\n"
     ]
    }
   ],
   "source": [
    "table_b = PrettyTable()\n",
    "table_b.add_column('Optimal Alpha' , [round(opt_alpha,5)])\n",
    "table_b.add_column('Accuracy', [round(acc,5)])\n",
    "table_b.add_column('Precision', [round(prec,5)])\n",
    "table_b.add_column('f1 score', [round(f1,5)])\n",
    "print(table_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Labels = ['Dead' , 'Survived']\n",
    "plt.title('Confusion matrix of the classifier')\n",
    "sns.heatmap(conf, annot=True, cmap='RdBu', fmt=\".3f\", xticklabels=Labels, yticklabels=Labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": pred\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['Survived'][5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    256\n",
       "1    162\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    266\n",
       "1    152\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yTest.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv('submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Reggression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_dist =  {'C':stats.uniform(10**-3, 10**2)}\n",
    "clf = RandomizedSearchCV(LogisticRegression(penalty='l1', class_weight = 'balanced'), c_dist, n_iter=1000, scoring= 'accuracy', cv =k_fold, return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal value of  alpha is 99.96\n"
     ]
    }
   ],
   "source": [
    "alpha = {}\n",
    "indx = 1\n",
    "a = 0\n",
    "MSE= []\n",
    "a = 10**-2\n",
    "while a < 10**2:\n",
    "    clf = LogisticRegression(C=a, penalty='l1', class_weight = 'balanced', solver = 'liblinear' , max_iter =500)\n",
    "    scores = cross_val_score(clf, xTrain, yTrain, cv =k_fold , scoring='accuracy')\n",
    "    error = 1 - scores.mean()\n",
    "    MSE.append(error)\n",
    "    alpha[error] = a\n",
    "    a+=0.05\n",
    "    indx  += 1\n",
    "\n",
    "opt_alpha = alpha.get(min(MSE))\n",
    "print('The optimal value of  alpha is {}' .format(round(opt_alpha,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LogisticRegression' object has no attribute 'best_estimator_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-77-1362ea6b47eb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxTrain\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0myTrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxTrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myTrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'LogisticRegression' object has no attribute 'best_estimator_'"
     ]
    }
   ],
   "source": [
    "clf.fit(xTrain ,yTrain)\n",
    "print(clf.best_estimator_)\n",
    "print(clf.score(xTrain, yTrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(C=opt_alpha, penalty = 'l1', class_weight = 'balanced', solver = 'liblinear' , max_iter =500)\n",
    "clf.fit(xTrain, yTrain)\n",
    "pred= clf.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.050535</td>\n",
       "      <td>0.019468</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.9935</td>\n",
       "      <td>{'C': 55.993516199507624}</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825436</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.820449</td>\n",
       "      <td>0.822943</td>\n",
       "      <td>0.816708</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.812968</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.818805</td>\n",
       "      <td>0.004088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.028127</td>\n",
       "      <td>0.022963</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.0871</td>\n",
       "      <td>{'C': 73.08713090812498}</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825436</td>\n",
       "      <td>0.814214</td>\n",
       "      <td>0.820449</td>\n",
       "      <td>0.822943</td>\n",
       "      <td>0.816708</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.812968</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.818680</td>\n",
       "      <td>0.004206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.032623</td>\n",
       "      <td>0.010788</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>84.3525</td>\n",
       "      <td>{'C': 84.35245816530578}</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825436</td>\n",
       "      <td>0.814214</td>\n",
       "      <td>0.820449</td>\n",
       "      <td>0.822943</td>\n",
       "      <td>0.816708</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.812968</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.818680</td>\n",
       "      <td>0.004206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011567</td>\n",
       "      <td>0.007793</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>13.8339</td>\n",
       "      <td>{'C': 13.833930470903514}</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825436</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.820449</td>\n",
       "      <td>0.822943</td>\n",
       "      <td>0.816708</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.812968</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.818555</td>\n",
       "      <td>0.004207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.044299</td>\n",
       "      <td>0.023212</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>59.2585</td>\n",
       "      <td>{'C': 59.25852057546377}</td>\n",
       "      <td>0.877778</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>0.808989</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825436</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.820449</td>\n",
       "      <td>0.822943</td>\n",
       "      <td>0.816708</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.812968</td>\n",
       "      <td>0.817955</td>\n",
       "      <td>0.818805</td>\n",
       "      <td>0.004088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  param_C  \\\n",
       "0       0.050535      0.019468           0.0000        0.000000  55.9935   \n",
       "1       0.028127      0.022963           0.0000        0.000000  73.0871   \n",
       "2       0.032623      0.010788           0.0001        0.000300  84.3525   \n",
       "3       0.011567      0.007793           0.0001        0.000300  13.8339   \n",
       "4       0.044299      0.023212           0.0003        0.000459  59.2585   \n",
       "\n",
       "                      params  split0_test_score  split1_test_score  \\\n",
       "0  {'C': 55.993516199507624}           0.877778           0.786517   \n",
       "1   {'C': 73.08713090812498}           0.877778           0.786517   \n",
       "2   {'C': 84.35245816530578}           0.877778           0.786517   \n",
       "3  {'C': 13.833930470903514}           0.877778           0.786517   \n",
       "4   {'C': 59.25852057546377}           0.877778           0.786517   \n",
       "\n",
       "   split2_test_score  split3_test_score  ...  split2_train_score  \\\n",
       "0           0.808989           0.808989  ...            0.825436   \n",
       "1           0.808989           0.808989  ...            0.825436   \n",
       "2           0.808989           0.808989  ...            0.825436   \n",
       "3           0.808989           0.808989  ...            0.825436   \n",
       "4           0.808989           0.808989  ...            0.825436   \n",
       "\n",
       "   split3_train_score  split4_train_score  split5_train_score  \\\n",
       "0            0.815461            0.820449            0.822943   \n",
       "1            0.814214            0.820449            0.822943   \n",
       "2            0.814214            0.820449            0.822943   \n",
       "3            0.815461            0.820449            0.822943   \n",
       "4            0.815461            0.820449            0.822943   \n",
       "\n",
       "   split6_train_score  split7_train_score  split8_train_score  \\\n",
       "0            0.816708            0.817955            0.812968   \n",
       "1            0.816708            0.817955            0.812968   \n",
       "2            0.816708            0.817955            0.812968   \n",
       "3            0.816708            0.817955            0.812968   \n",
       "4            0.816708            0.817955            0.812968   \n",
       "\n",
       "   split9_train_score  mean_train_score  std_train_score  \n",
       "0            0.817955          0.818805         0.004088  \n",
       "1            0.817955          0.818680         0.004206  \n",
       "2            0.817955          0.818680         0.004206  \n",
       "3            0.815461          0.818555         0.004207  \n",
       "4            0.817955          0.818805         0.004088  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = clf.cv_results_ \n",
    "result_ = pd.DataFrame.from_dict(results)\n",
    "result_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result_' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-80-32fee2eaae6c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresult_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'param_C'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean_test_score\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtrain_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean_train_score\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_C\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'result_' is not defined"
     ]
    }
   ],
   "source": [
    "result_ = result_.sort_values('param_C')\n",
    "test_score = list(result_.mean_test_score)\n",
    "train_score = list(result_.mean_train_score)\n",
    "param = list(result_.param_C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "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.title('Train AUC vs Test AUC')\n",
    "plt.xlabel('Alpha Values')\n",
    "plt.ylabel('AUC score')\n",
    "plt.plot(param,test_score)\n",
    "plt.plot(param, train_score)\n",
    "plt.legend(['Test AUC', 'Train AUC'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_alpha=2.017451013883791 #3.0489778895550868\n",
    "log_r =LogisticRegression(C = opt_alpha, penalty='l1', class_weight = 'balanced')\n",
    "log_r.fit(xTrain, yTrain)\n",
    "pred= log_r.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "accMulti_bow= round(accuracy_score(yTest, pred) * 100, 3)\n",
    "precMulti_bow = round(precision_score(yTest, pred)  *100 ,3)\n",
    "f1Multi_bow = round(f1_score(yTest, pred) *100,3)\n",
    "confMulti_bow = confusion_matrix(yTest,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+----------+-----------+----------+\n",
      "| Optimal Alpha | Accuracy | Precision | f1 score |\n",
      "+---------------+----------+-----------+----------+\n",
      "|     99.96     |  77.239  |   67.717  |  73.82   |\n",
      "+---------------+----------+-----------+----------+\n"
     ]
    }
   ],
   "source": [
    "table_b = PrettyTable()\n",
    "table_b.add_column('Optimal Alpha' , [round(opt_alpha,5)])\n",
    "table_b.add_column('Accuracy', [accMulti_bow])\n",
    "table_b.add_column('Precision', [precMulti_bow])\n",
    "table_b.add_column('f1 score', [f1Multi_bow])\n",
    "print(table_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Bag of Words with L1 regularizer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix for Bag of Words with L1 regularizer')\n",
    "Labels = ['Negative' , 'Positive'] \n",
    "plt.figure(figsize=(5,4))\n",
    "sns.heatmap(confMulti_bow, annot=True, cmap='RdBu', fmt=\".3f\", xticklabels=Labels, yticklabels=Labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(10**-3,10,0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC( class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.arange(10**-3,10,0.05)\n",
    "gamma = np.arange(10**-3,1,0.01)\n",
    "param_grid ={'C': c, 'gamma': gamma, 'kernel': ['rbf', 'poly','linear']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.301, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=0.38099999999999995,\n",
      "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
      "    shrinking=True, tol=0.001, verbose=False)\n",
      "0.8587479935794543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alankar\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "clf = RandomizedSearchCV(svc, param_grid , scoring= 'accuracy', cv =k_fold, return_train_score = True,n_iter = 100, n_jobs=-1 )\n",
    "clf.fit(xTrain ,yTrain)\n",
    "print(clf.best_estimator_)\n",
    "print(clf.score(xTrain, yTrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC(C=1.301, cache_size=200, class_weight='balanced', coef0=0.0,\n",
    "    decision_function_shape='ovr', degree=3, gamma=0.38099999999999995,\n",
    "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
    "    shrinking=True, tol=0.001, verbose=False)\n",
    "clf.fit(xTrain, yTrain)\n",
    "pred= clf.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "accMulti_bow= round(accuracy_score(yTest, pred) * 100, 3)\n",
    "precMulti_bow = round(precision_score(yTest, pred)  *100 ,3)\n",
    "f1Multi_bow = round(f1_score(yTest, pred) *100,3)\n",
    "confMulti_bow = confusion_matrix(yTest,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+----------+-----------+----------+\n",
      "| Optimal Alpha | Accuracy | Precision | f1 score |\n",
      "+---------------+----------+-----------+----------+\n",
      "|       1       |  78.731  |   71.304  |  74.208  |\n",
      "+---------------+----------+-----------+----------+\n"
     ]
    }
   ],
   "source": [
    "table_b = PrettyTable()\n",
    "table_b.add_column('Optimal Alpha' , [round(1.301)])\n",
    "table_b.add_column('Accuracy', [accMulti_bow])\n",
    "table_b.add_column('Precision', [precMulti_bow])\n",
    "table_b.add_column('f1 score', [f1Multi_bow])\n",
    "print(table_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Bag of Words with L1 regularizer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix for Bag of Words with L1 regularizer')\n",
    "Labels = ['Negative' , 'Positive'] \n",
    "plt.figure(figsize=(5,4))\n",
    "sns.heatmap(confMulti_bow, annot=True, cmap='RdBu', fmt=\".3f\", xticklabels=Labels, yticklabels=Labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(class_weight = 'balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in range(10,1000,10)]\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "\n",
    "# Create the random grid\n",
    "grid = {       'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomizedSearchCV(dt, param_distributions=grid, n_iter=1000, scoring= 'accuracy', cv =k_fold, return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight='balanced', criterion='gini', max_depth=670,\n",
      "                       max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=4, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort=False,\n",
      "                       random_state=None, splitter='best')\n",
      "0.8426966292134831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alankar\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "clf.fit(xTrain ,yTrain)\n",
    "print(clf.best_estimator_)\n",
    "print(clf.score(xTrain, yTrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.006403</td>\n",
       "      <td>0.015214</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>auto</td>\n",
       "      <td>800</td>\n",
       "      <td>{'min_samples_split': 5, 'min_samples_leaf': 2...</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844643</td>\n",
       "      <td>0.819964</td>\n",
       "      <td>0.853832</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.843137</td>\n",
       "      <td>0.834225</td>\n",
       "      <td>0.846702</td>\n",
       "      <td>0.834225</td>\n",
       "      <td>0.843593</td>\n",
       "      <td>0.011445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>310</td>\n",
       "      <td>{'min_samples_split': 5, 'min_samples_leaf': 2...</td>\n",
       "      <td>0.746032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.837790</td>\n",
       "      <td>0.855615</td>\n",
       "      <td>0.844920</td>\n",
       "      <td>0.836007</td>\n",
       "      <td>0.846702</td>\n",
       "      <td>0.852050</td>\n",
       "      <td>0.846702</td>\n",
       "      <td>0.849300</td>\n",
       "      <td>0.008559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>760</td>\n",
       "      <td>{'min_samples_split': 5, 'min_samples_leaf': 2...</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.862500</td>\n",
       "      <td>0.843137</td>\n",
       "      <td>0.871658</td>\n",
       "      <td>0.844920</td>\n",
       "      <td>0.837790</td>\n",
       "      <td>0.844920</td>\n",
       "      <td>0.857398</td>\n",
       "      <td>0.853832</td>\n",
       "      <td>0.852865</td>\n",
       "      <td>0.010637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>110</td>\n",
       "      <td>{'min_samples_split': 10, 'min_samples_leaf': ...</td>\n",
       "      <td>0.682540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.816071</td>\n",
       "      <td>0.837790</td>\n",
       "      <td>0.843137</td>\n",
       "      <td>0.852050</td>\n",
       "      <td>0.803922</td>\n",
       "      <td>0.837790</td>\n",
       "      <td>0.819964</td>\n",
       "      <td>0.796791</td>\n",
       "      <td>0.825752</td>\n",
       "      <td>0.016687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003125</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>880</td>\n",
       "      <td>{'min_samples_split': 2, 'min_samples_leaf': 4...</td>\n",
       "      <td>0.698413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823214</td>\n",
       "      <td>0.809269</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.828877</td>\n",
       "      <td>0.828877</td>\n",
       "      <td>0.827094</td>\n",
       "      <td>0.791444</td>\n",
       "      <td>0.836007</td>\n",
       "      <td>0.826470</td>\n",
       "      <td>0.015472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.006403      0.015214         0.000501        0.000501   \n",
       "1       0.001201      0.000400         0.000200        0.000400   \n",
       "2       0.000705      0.000462         0.000000        0.000000   \n",
       "3       0.000000      0.000000         0.000000        0.000000   \n",
       "4       0.003125      0.006250         0.000000        0.000000   \n",
       "\n",
       "  param_min_samples_split param_min_samples_leaf param_max_features  \\\n",
       "0                       5                      2               auto   \n",
       "1                       5                      2               sqrt   \n",
       "2                       5                      2               sqrt   \n",
       "3                      10                      4               auto   \n",
       "4                       2                      4               auto   \n",
       "\n",
       "  param_max_depth                                             params  \\\n",
       "0             800  {'min_samples_split': 5, 'min_samples_leaf': 2...   \n",
       "1             310  {'min_samples_split': 5, 'min_samples_leaf': 2...   \n",
       "2             760  {'min_samples_split': 5, 'min_samples_leaf': 2...   \n",
       "3             110  {'min_samples_split': 10, 'min_samples_leaf': ...   \n",
       "4             880  {'min_samples_split': 2, 'min_samples_leaf': 4...   \n",
       "\n",
       "   split0_test_score  ...  split2_train_score  split3_train_score  \\\n",
       "0           0.714286  ...            0.844643            0.819964   \n",
       "1           0.746032  ...            0.857143            0.837790   \n",
       "2           0.714286  ...            0.862500            0.843137   \n",
       "3           0.682540  ...            0.816071            0.837790   \n",
       "4           0.698413  ...            0.823214            0.809269   \n",
       "\n",
       "   split4_train_score  split5_train_score  split6_train_score  \\\n",
       "0            0.853832            0.848485            0.843137   \n",
       "1            0.855615            0.844920            0.836007   \n",
       "2            0.871658            0.844920            0.837790   \n",
       "3            0.843137            0.852050            0.803922   \n",
       "4            0.848485            0.828877            0.828877   \n",
       "\n",
       "   split7_train_score  split8_train_score  split9_train_score  \\\n",
       "0            0.834225            0.846702            0.834225   \n",
       "1            0.846702            0.852050            0.846702   \n",
       "2            0.844920            0.857398            0.853832   \n",
       "3            0.837790            0.819964            0.796791   \n",
       "4            0.827094            0.791444            0.836007   \n",
       "\n",
       "   mean_train_score  std_train_score  \n",
       "0          0.843593         0.011445  \n",
       "1          0.849300         0.008559  \n",
       "2          0.852865         0.010637  \n",
       "3          0.825752         0.016687  \n",
       "4          0.826470         0.015472  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = clf.cv_results_ \n",
    "result_ = pd.DataFrame.from_dict(results)\n",
    "result_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_ = result_.sort_values('param_max_depth')\n",
    "test_score = list(result_.mean_test_score)\n",
    "train_score = list(result_.mean_train_score)\n",
    "param = list(result_.param_max_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M8Y2PPGKr7x7Hc4mtD5j/b7mXSHqMaG6G2OgT2vXExp4iNrsNClmi1vjEx54ksclcM/Gt91fWzGbneEZnthDJz5FcdwNR1zxsJUIi4UJy3Q1V98riFQUi+TnP35XvKVhsoCLKagNiE+uaej9SElkEgNKGWg+xsbVV98os/U6A2NSG6u9fS2TZKhQLRLONjVckt+uFufBCNKvvj9DoXmFHfOSRtoiZSgiJhA3R6edIjDxSdb/e5tPoh48qZNyguRADygXiPqH6RaQQ3OYW0yAS0fQo0fRo9f06xHyhIT7yqOPvIMexHiINEgjwzyE83+FnLIMgEtHsVNMHO1/1ta2mnQDJdTfWvF9v82n0ZFVroyuXqTOZAtpUotlJonPqttRvR3CcRGy0PpGIjdUWRe1MnARQEYFZiOTaxz3W4449392ZiUTBV8xQLfiadwFJCOLbHg6kHB2ERMKGjmf+XvO+5yQwjMbFTV6chIboQecZXcQmGucmIpbVVRDQETepuI1mTsfzgfj21ZU/igUixeCIbT00w3U1TSRqWc21Ca3g1iJtFjcBxEdCItF2RLLTJDauxIhWO6FHs5NqGWATk87r9K7DnQRKJJoROQXJSWgQifh4tT4CaFjGPl9wcBIBElod1DQH1X3XZeHl+/026tuq0AK9j595F9SaDTmJeUBiwy1Eillyux9f83eVcqqZj960uGmhcBL54Da42MzW+s+MPVW7HTubuMlmJRQkwddBc5xEk4rrNorV3GgFgYrk09pirKBEirHZbYGUo4OQSGCaAg5c92EAMvueWvMZlQy3mQUTSY+pORSNyRzkhG+GkwhS3KSDWpZNsPMRCTvaTSTmUyfhNnVtJ1o1zloip2K+rSLFoBASCaDnnm+Vr/Opaic18CASTWzUEaOoPNG1Xdw0vanxl9tNJGa21Ly/s4mb7GinZRM0RySiuZ1X3NSqunX0YUGtVyOaDKQcXYREAlyOcLWz+EUVp9RmJ50y1IMOJ9HmjUWFdnMSKuxMnEShf2/njTaLYEqOno2gWXFTO624qupuESeh5SsREIHKDx8USDm6CIkEkD7QjE4+d9h7lc9EVIuqyQmv0ktoEYk2iyiUCFBxDWAoCHU97EzWTYVF+2DEu8t/70ycRKi4robOASWofueHDw6kHF2ERAIodqcAyA++WPmMahI0K19VWThpndYWCJEIkpModg1jJLo1nhuqbsdOxEkYkRj51CHlv3cuxXWzOoldVNwUEAdlxDop9O4WSFk6CImEJpS6g2bFTUpOQsdPIphJV+wYaOr9IMNy5Bfvr/fcomqCHnVvXgF5pLcKueFKwLm2K67T80gkcrPEN99bM35Vq9EyxXWmvrgpSG4xnzo8sLLqISQSmlByEs2Km5SmtbZyFZtdUBO+MLBvcwUEuPAKg/WJhBGNU+zbXaOwhaErUcFhJNHm07VSfKrzbrPOdPk5Bv9wJgN/+ffmymkAreIkdIwmgqw7v+SIwMqqh5BIaKIV1k2esOeb8PDR6Fx9FX3Xf6SpqgqLmiMSgXISHiK/EgoDL4JIrO5zamViYzqPoJFP2TgJzVNm4rlVDPz5nU3pFKBJcVN+rpL7o9H324DY9sdg2uVPMJ8msJoHynKkWSuqcM2iloScxIJDNNteImEvN6pyMCuk6bvlfDrXXqMfFdIompMvX9nYiz1L67xjeIpugtRJFAYP0HhmP62yVCdeHZ2HsswAo2/auaZITpNIPHsLyQ23Erc2ktoFZ0h9fw+ST/216qfU9/dg+NJ9PInM4K9OIfX9PTw5sQ75O1Lf34POx36h1W47AosiW0ecuPjXr4FvOjnTSH6Ontu/wOAvX91QlZG0whoxM0nq+3swdKX6hO93r/CSFOSH2qe8DomEJtTOdM4P3/uPT5kLzC9c+SrsCvGowruyEXFT4tlbzMQ2q39vL8lTL9F/7Vne3EqAnERhcX1OIq8hkoKK3N1NLOxWRX4RnVNkDGsEsUT50q+82iufQMcTfwJg4LoP1fw9Usx7mmzGrVwfsYn1yme6Hl4BQOfqq+q2tar+Jg9WRqzTKsc1ZoZB6vt70HP7FzzqTtP90BVmjpgG4M7XUUJ0erP5v2c8tgb6XcjQd/3yqoRTpbmTedHr/JfpEyGR0ITScsb14bse+6X5/Iw/t/mIm0jk7JxEbeexRiZdObmQqz9eeon49sdIbrxd+bsXJ1HaoHVj7he763A16HMS8TEzGUzUnbch2vi0N2JJyKc9RQENQUHwI0btYHheRMKPV2/V4UdTjBSxOdT59db34iTKhyyPuW10mgcat2VhyRO/+6Er1HWriLGmkUNc0Ved+d3Ieu2+/xI61/6ZgT++zfe7QSEkEprwa90UoYZYwoe1jVPc1CQnYS0MI9ah3Fzq6SWimXG1PNeDSCS2PQRQzpRXF5H6+oKCht6ilRi+4kAWXf0m4lbfgoByA3GJt0rPBZWZzD2vdTcy+0bvN7eB17wtHbISz92tfKbYsUjxi8b6UugFnIYiapGiyuS9VR7XEctE3ivOW6sREglNqK2b9OSrnY/9gtQle9ZNdVhK6u4gErMKnYTmpCtxJUaiWym60rFwUulGmg3wZyR6fT1fWPSipurzDfdGbYW69kyx6hO6G0jXI6aYJ7nxtsDqdkBzPttFeH59hZoWNzVhsq3iJOx9aERnouVMN4+e5s0gJBKaUJm46U74vn98GoCOtdcqn4mNrWV4xTKGVhzlEjc1x0mUF0C8u2FOwmyHgkg0q7i2uIfMPrWDK5aQH9gHACPe1Vx9PtFMWG3tOjR1EmUCpUi81HQ7NM1b7T4pvjfVJhXXRtLfocIO1UYdyc2UPf0bsRzTsm6aT0/zJhASiSbh91RkJHqUv8W3rwEgOjfimFDqzdnfxmJyErWJRH6xqFuOKrBe0GE5VCj2NWAQEAACVVaroGndVEKg0URt36+hU7TPd5q3CGzchFnJSeRmMTr6AXVcK6+c9O32k2gnqjPsBAQhRBS4BDgCyAAfkFKutf3+LuA8oABcKaW81Lr/GeBfgSRwiZTyR61qYxDwTyQqljXFjgEPXYfdukkl5vFft0rcZHSact70fmco328ZJzGfKOQcVka10A4iMZ/BGpsRHZnvt5tINA6lDjE3i9GxCDITStGRl3ixnRES2o1WchJnAp1SyuOB84GLXL9/EzgFeBlwnhBiUAhxMnCCde8kYM8Wti8Y+JQzOohEjfhDJZTt8Q1DnYjHpyLMiHcSna2z4XlsmEorqzZxEq2AzqYYnVWbNQbXDtu31DVwCCgNqD1gXyPe1L51EkH5SQBaymo7VFZkuVmKFiehEjc1lb2RkEjUwsuB6wCklHcBR7t+fxgYADox+UcD+BfgEeBq4BpALcBfIGhG3GR0Lq5ffm5affrxay1RyFWZ2vqBikgs9PAXXtDZFCPt4CTs31JTlFSX4OvWbZOnNx1yQ6e+ADfLoBzUIvnZskJclRagmeyNZh07J5FombgJ6AfsJLkghIhLKUvHn9XAfcAM8Acp5bgQYhjYGzgD2Bf4sxDiQCml8rgghLgQuABg+fLlnHvuub4amUr1wTbTOaevtxMWmSf97u4k3UO19QepVF/5OmGYG2Q0EnHcH1pcUa4N9FcUrQNDi2HKfCcxsBRs+25/f2dVXcm0U4fgqJvKhp8artwfXFRpd29vpcy+uHOBDA6az3V3J+m22tvZkaDTVlYq1QdRUwbcmd3hvJ+MW+2onGj7+zvLiujuLidX0t1lJkuJ4ByT4SGz7o5k3Cw3EiEajzr6mkr1QSJW6WunWfbQUA9010/C0tfbUb62f5uh3gjY6nHUV3o3UtlE7WPb39dl62sSps32JeIxEt0dNfuaGnb11UIyYn7LaDRCqr9ydiuNDcCiAafCviQ770jGy+MRi0bNeWxh8WCFc03V6CfA4p7K8hrorFwvHrT3tRNiHbiRiDvPmY46ZnM178eL6Zr3VX3t6e4Aq56OZJwO2299scrGa29v1dwptdfIOu/HOyGfpieegz7z0JbIT5XnfFdngq7S+3PPOvo6tLj+/mC/7ojY12vt/aGn2znGpTUTjUTK/evuStJdWjMdceV3Vd33i1YSiUnA3spoiUAIIQ4HTsckBNPAVUKItwA7gMellFlACiHSQApQeqZJKS8ELgQYGZkyRkb0nLbAHMSRkSmSk2kGgKnpNPnxWQaB2dkscztmqCUQGhmZImVdFzIzxICiYbDDdn/H6HT53YnJOUpGe+OTGaLTafqBuWgf9mU/OWnet6M4tcXB7jnqTpt1A4xsr9wfG59h0Lqenk5Tmo5z2zc66hsbm6n01WpvOpNjylbWyMgUi4sGMSA/vrE8YUZGpujP5ukA8pnZ8v3JyTR9hmknMjuXw+7bPDuXpRuTZZy0jcn2HdMMA5lsnsmRKYYMg0K+yPiIsx0DuQJJq6996RydwI4dM3TNZqnnQz01nSlPRvu3Gds2Qp5K2GV7faXrue2byuNmH9vJqTl6DYOo1be41b5cvkB2NkNPjb6ObJ8mZetrqY5ceoYEUCwajG0ZKbevNDYA4xNz1PIQyGTzFCM5uoBCscjsdLrc19GxWUr8qr1Pdkxs21pu39ToDtu7M+V3J6fSROcyuO2Kcvki9qOAff1F0pW22+suZmbLc9p+X9XXmdkMSaueTDZPeqIynrOjW8vf3t7eEdfcqazX2cqaGZliKN5FNJ9mbnICIj3mGM6axDcGzKVzTFt9WrT1CUdfd4zW3x/s19m5aUrHmdI8AHN/6Mc8UMxY86aE0popGgbjVv9m57LMltZMxpxHbpT2Nl14EZRWipvuAE4DEEIchylGKmECmAPmpJQFTCIwCNwOvFYIERFC7A70YBKOeYWXyWUz8lXDrZOoIWbw9OQMMJSDDlS6kZ1aJ6EhXmmL4tqm22p3DuhI1m7O2npxU5CmoL4DFbrqLoVoieRny6LgmtZNhkG8Sb+YndVPopWcxNXAqUKIlZhE8mwhxDuBXinlD4QQlwO3CyGywFPAj6WUWSHEicDdmATsoxYRmVcUO/qJKfUCjX/4oksn4XcS+dVJNEsklLoRhU6i3RY7RjReNvfVhQ6R94rHExTsY9UO2XUx0VP2dYjk2q2TCI4I+vVhca+ZEmGI5GbA8uSuNZ+jM1uazx0T6iSckFIWgXNctx+3/X4ZcFmN9z7dqjY1CqNjESi9jRv/8G7PUb8L1DeR8BlPShsKTqKZxDaNwEj2KW3ZVQp7HSLRDsW13eomWOuf2jA6BqBEJBycROs3siAj6frlJNzr1UiYUoJ6Y+5l/moQIaJhZaVlJrsAjUBCZzoNlJxsmiih9t2oMyeCbyLhc0IFFe9Htx3tzjltJNXfSTW2euKmNpvAqjasADPt2Q8o7bZuChK+OQnXYaFkkl6fSKgtm7T3B5289QtQJBUSCQ00n95Tz+xUmf+gmSQ5tkXRKlm3ikg0k9imEXh9p6aIhGYE22ZgJxLKIHJB5hJ3EInm/CTmE80mXyqbpLuIhFtUWjJ/rTXHjKSeFZGO1KHd+igdhERCA7qTQAXdhac6zVQpuH3VPX+TTifoWZDwOtHZRSqO+/M4PnbYw2woN5MAZdolD3vAEfZ7Z5Obq0Jo6KKSm8JFJFxztyRuqhUIM1AisUDmox0hkdBBJNZUUDmvD1+0OdepiEmhXuY4r7rn8WTS7CnPL7wWq5JQL8CTmzoIXXBtfd5wEgEFl3T3261jiI0/TbFz0EFcSyh6iDkdZeoYVYREYudFMyInr43aziWoFmixe0njdQck42yk/+0XN7VGJ9FuqIhBkATf6FBwEr7HIzg9yXzCc50U88Qm1ytD1BsdwTitQShu2qnRVAx7j4VX7By0PVd7gtTNQe1Vt2vSGdHGDNqKnYMY0fqezY6622ypYSRbo5NoN5Q6iQBPmfb5HHVwEn6D9bXmG6tCY7QKXptzdHIDkWJemXOlWXG0ox2h4nrnRcuIhA4n0QyRcC36Ytew4sm6JTXVjnbAUyehJBIL7+Sm5CTaIm6qX4cRteXmrjGunY/8hOjUc1X3/SCSUYflbgW8REElJzolJxFNYkSC2UpDTmInhpcoox68Fp49yF8riIRbxtmM6KrY+4LG29EGNKKTWEJcUe0AACAASURBVJBEQsNxs1mLO4cJrE9xkz1IZVWe6e2P0Xfr5xj66Uubat/8oLborDQ+dq6/6s2AuIlG52P3vd8l+dRfA2mDGyGR0ETrxE2L6j7XlE4i7yYStaL36KHQvbA5CU8TWKV108ITNynzMNs2kGa+IzSnuHYQCdempso5sjNAx1S90FP7oOTlo1MP0enNFUV51BkUM7H9UfN2Hf1ez6qvM3Ddhxpug2f7WlLq8xBNKa69Fl4kVve55oiE097bvrlELe/k5LobPMuIzW4lPvHMwuck2iBuio095ev5RqDUSdjuNyQ2tHEihopIaOiRHERiPjyEW2SmqyPqKfbXTnHTDCfRd8tnytfuNMKJzXeXr0u+Gh1P/aXhuhpBSCQ04TgpFP2Fk9JVRqkV141vzlU6CRvBiW9fbf4/thYdFNvESURzM55hEFRoSNyUn6X7notJrrtRXW68EmO2/6b/LF83rt/xho5OohFOIjo3Wr52+En4TIVqJNUpeJuC5rrqXHtNS6rXOzBUtkz7AazYpIVT2WHWY68YuO7DAMQU2SVbhZBIaMKxqNKjHk/WgK4zneJUVuxufDOqJhKVsiI64SZsoSDayUlEGxADGVF1Vj1l6JDcDD13X8TAX96rfNc+ZrnhQ8pEo5nv4gW1TqJZIlGJQdUMZ+yVp935oL8YTVGPHNJ2xLfc66tcXfgVPcZHnyhfNyNumjvo7cwe8wmzDQvQmTEkEpqwO8z4jYHUtNw7VjE97Xx4hb+63SawNmsq+8lSCftpKUjrJp0NJKD0nF7QOT3aOYbJf7mM9EFvbWWTPDgJm7ioAS98eya7ZjY1LyJhd0KLTW3yVa7WoQV1yPqm4dP8NLbjsfJ1I/Hd0vv/GwBzRy0v3wutm3YqOC0dHHblvomE/w+v8mfou+3zTdVtLzeqwRFFbUH6mhF7uRGbWFe+Tq7/R+1nJp+teT9I6BDwRsRKpaCAOtFBq9qkOk3avqVh12Vlp0lsvqd+ufbv7ZHLvB68iIRdTBgb96e/0ZmPrQz14neDjltKZWhMJ1FrHL32iukT/K39oBASCRdi05sB6Hz0Ksd9o1NNJIx4ddpRO+wbUTM5pgGmj/9s7R8UEUK9FIs6Jzd7hNAgrZvs4aL7/7685jNtURJrcCvFbv+n9i7b/Ol6+Ep/bfLpcZ3ceDtxa0MuLBbKcoPKie3JSdhERn6/n0603cTWB3yVCdD/l7O1nqt1YPAS/8S3VzgJ3dAc9dvgEcJHJd5ssfFASCRciE6bLLLb5MxL3FQvrpNdvp7YtFKzIbU5CTtraocuq+6oQodI2E9urVJYlopff7Pj79jYky2tTxd+OYnElvscfyc33Orr/UY8rnNLjgAgu9dJymeCyrDnpbi26xXKQfH699IqV0f86VfJDtBRx3qvXHaN8Y3vkMrnYzOVBPW6nIQ9akGn/L154chKWJsoeemQ4jseV/4WBEIi4QH7hLTHuqkmEvUyLFeg64nqN6BgfLy+hZK7nTqKwkgbwmRPnGaetCOG07pF1+qq1WgmCi/A7BGm/Xqhr7b5pBvKAH+KDWT6+M8we/S5dcvVEefowJuTqERlLYmbdIlEJN2avB3b379ar36LSNjXSdymd/CCbvymUpIjqHD5sZnNVff8IL7tkfoPNYGQSFBhYTtcZpDxbQ+Vr+2KqajLBM3+4evBfvrwgh/CA3qsvdsiRmdCtoNI5FOH1rzvxUl0lLxLmxTf6aARnUTJpNEgQmb/1wMQm9qg9abKD8BNPEoHieis3uYaaYO4yR66uyQCc3PFqvworUruVCtyay2UOLhip3/LL11DgFJocl3kFaFA7IiPPOyrTL8IiQQQtawwoi5rjMSmO8vXJb2DEY1Xse1GrEO/Ll0i4YPwAMQ0Tt3FHv9Oee6EO7klRzQVNt0PvAhfaUG3Q7mtSyTsh4q5oz5Svvaz+UXyaaWy2y1qKRF996HF8Y6trKA2YV1OQpmuUzF/tKzt7O1oMFhlCRGXRKCkn3Jv+DrZ77TD9tTRX7qRe8Ex9YscCTmJeUPiubuq7uV2P84hbjI3TP3McdFpXU4ieHGTY7PTdFzy4iQ6nrleUZF/uXEtRDXSnzabdEYHuoprux7Ly2dDBSPW4ctOvpx6s6DOnWwnXIHpJDQ5CfX7tee2rl6tNLbNxkuKj9QWQxU7nJxHfMea+m3S1Un4PPyVvrEShWyok5hPxBVev/aTW92P6IKu4s23uGlURydhO8Vo5gZuxORQK7lKQFCd8vKDLw6sjiA9qyMF9fevZyWni+h0RcadXHdT5X5gnIR6buqculVzO6qpk8i94Cirsua2L7sJqx1unwfVc453dMVNAXPh8VHZtMVkPYREwi+KOYfCt2Wil0T9DcMu241N+3NcYkbvVNkOnUQzcCvf84P7m/8vPjCwOgyP6J9+4SUeC4pI2E+WyfUVPVtgcZZsPhpu6HB/qjWjK24qWN+4EdhFwypOwh3M027q6mhH/96A2WdtxbXV94iGM6mOoUNsaqNWvc0gJBI+4T6NNUIkvE6TvsptZlPRVGK6dRJl+Ixf1SpE2iBuUpkjN4JSkLbaPwbESdj0XjqbUbuhFDd5EDEHl2HjIPzqJeyEuBS7zA23uamqXfnhgwCI7VhD0SPhlaN+i0jpHL4Kg/WV1u1ASCR8wq+PRM0yNJTXOuKmpk6es3qntojiZOilLG0JFM6CUU2xWc0i6xgc5JYs82WUoANV4EKDCEbMX+Y/FXSNI1oN1djZ14xbeaxCzCZCc5TVoWe5VKnbRiQm1tU8BOmG2MgPH1Iuh6i5lWb3PNGj7i7KObUDTO3rN2OkX4REwgPlwbfJ2N1Odq0jEvXLbYpI6IqbMrVPPFHFog0Shd7dK+2wJcaxoxlOQjtQXYCIjSs4iXgnfgwgPOvwGdtIZZLaLJSZ3Gxz2683uht+AxW611Vse7VS2i5uKnapAynmh2qJNNVjaa87SIOL2u0IDiGR8EDuhccDapkkAArW2fAYWh1fCR0rCL821w5oiptUbHF0urn0lDqwy55V8bKa4iR8Gh0EAZW4KSh9RCMIMkezHflF+9X+wTbuXY/8pKk6dH0gynCNc2y2mqDaCU9++GB13YleX1U7OKgm5q0b+dRhgZVVC8EJW10QQkSBS4AjgAzwASnlWtvv7wLOAwrAlVLKS22/LQHuA06VUrbWvssDud2PI7nhFuJjTyifMeJdtc8OHhuQjhmslnWTbcIbsQ5/ikldByyFuElJ6IoabVCIjtzID76Y5IZbzHYoLHN2Pk5CIW5qEZHILxbER9WhJcAKs2F9Z9/zyAOFwdpEwnGi1lB0e8E3J6FxsDIcROKg8hxsFg5v6wDFTfklh4GeY3hDaCUncSbQKaU8HjgfuMj1+zeBU4CXAecJIQYBhBAJ4HJg3gOr55YeUfcZB/uquflpyYx9iptUrL36ZT2FpkpxrQov4nWyL8W/0jUDLtjMWNWchF4OglqYD05ClYayVVZy2X1eXfcZI9Fnuw5uTHTETcUmuRjfOgnbRm1vh12ub9dJ5IcOaqJ1rrrtxDFQTuLwwMqqhVYSiZcD1wFIKe8Cjnb9/jAwAJSEsaUd9pvAZUDr5Rn1oKFIdLKQehtWvXy1oClustUdpF+AHUqdxExtnYTXGLhTM9aDg0ioOImmxE0uTkKTyLcEAVk24bKc0/HYtQfs0+WudBT6BYW4yU6I0oe+R6s+FZrhJOwEwEk8Ku1z+j80OT/se0WgOgl15N8g0DJxE9AP2HfDghAiLqUsaYFXY4qUZoA/SCnHhRDvBUaklNcLIT6DBoQQFwIXACxfvpxzz60f6MyOVKoPOsxhiMei9PZUJv+igcpkSQ3bTzwVmtbVvwhGoxCJMByrbGTDw/Xllf19nTBb2zO3d7Bim59K9dW8TnZX6uh84cFgpXUcXFR7oXd2JOjsr018hhb3Vp6x9dV+6k+l+iBu9rUzXVs52oONqESiDo5lIFUJNd7TUyHApbrd/Vv04iPL132RSUiY9vn2bxExCnR3m2UNLuqGWNTqR9y05zeqTXVL9SV7nFYsqb5KW1OpPrO+SMTRpqHFPdBl1rd4sKdsrdLd5TxQ9HRbpo7AQI0x70jGHeXGu3qgBkEeHqo9j3p7OiAWK5fVYdXRW3QS6YHB2pY69roTPZXTeKyzl9InXDxYmUf9fZ1gEYaBgS4zIvCct1hqcD8zJlfS1dfu/srG3v3K/4T7L6l6t6+3sg5LY1lCV6e5ZqKRCN2LK3Nq8aBtvSrWTEePrd97Hglb7zfL6ugF6/A2aOv3wEDl2/UxAbZ5u8j2W2lOJpMxZd2J7t6yOXVPpBJxdqC/C2aqiW4yESdpzatoJEJ/X6W+4UWV51MvqDh7qupuBq3kJCYBeyujJQIhhDgcOB3YF9gHWCKEeAvwPuBUIcQ/gWXAT4UQnplupJQXSikjUsrIO9/5PkZGprT/AYyMTJHOmHQrXygyPVOZ+OMTlQ85sr2ygItdi8vXM7kYuXyRomEw8WxF9rt9e21rHMcATaWZnavtLTk5V9F0lNrqvs4UKzR+sqMSaXNsvHYinXQmx8RkbSnejtHp8jP2vtoxMjJFLl/EMAwK47WdeNJjFdNYd4z98YlK3TOTlTpKdZfqKF/PVhbC3PbnyObMDd/dvtkZs9yx8VnyhaLVjzx5RfTRcl8LcYdlz+iGdY525HIFDMNwtGnH6AxzcybhHB2boWhxH7NzzhP8zKw5jwyoOeaZbN5RbtZIkM9XiwC376g9j6ZnMuQLhXJZpToy253OevY5bBftOOYRldN1LlK5Hh2rzKPJqTTT02afJibmKGjozEbGzbmddfV1OldxxhtJ1y5narqyDktjWcJc2iy3aBhMFyrtHX+u0nfVmkkXKmtmqveA8nU+VtmAx8YrYzYxMVfm0jPrH2BmpvKd7fO5NCez2YLHek2QyZp7TXq8Ij6dmJxjZqaa4GZz+fK8KhoGk1OV+ka3VETW7vrse5ufvVCFVhKJO4DTAIQQxwH2KFQTmDqHOSllAdgGDEopT5RSniSlPBl4EDhLSrkwjL5tcERTtbGQQXo/6siGHazzotaIm2pXXCSqMLN0eKN7iAJiE+vr1xOpbODRObU9vSp0SH43t4Szunz7OHvV0WoEFpLDw/y1qAh5brdu0hY3NaH0d+tfvHJg1ENJ72FEYsTGahsFOOu2rRlb9GEvQ5HCwD5A8+vbXnc7Yo4FhVYSiauBtBBiJXAx8AkhxDuFEB+SUq7HVE7fLoS4HVgE/LiFbQkUdttpuyzTHgq6WesFv34SfuX9zSBSyDhyP9jl03Yi4SUv9vQ8rlWnhzVWMwvOvtlFZ+aPSDTlPW+Dl1GEKi9GMWHXSag3y1KwwEh6tDnLMMXcNhqIxVTKljh32HuJWUEuvfQlDj2eLYuf0gs8n/YMQ+IHDv1lgNZNrUbLdBJSyiJwjuv247bfL8NUUKveP7k1LWsedk7CYbEwWTlpePpW1IBBxBHWWY9I2J4J2DPYD4x4Z9lsMpLR5CQUpqAqROdGKNqc6+yoZzDgadZpX7ht4CSKXamaHEtQ1k1eREIVqNDJSaiJROdT1wJm/pWmOAlFHUbnUFPfoJSkytOIw06MVdc2RLJNbuZG7TUdpHVTqxE60zUAFZGws6O6Ga0qL7gmqY4povL0qedB22w8/jJs5oP2IG1e4Q2UMaFUz3tEMK234IykU/mbfMYMetf55J+cnERASXm8UOL4olmnrqEpx0gbvGI1qcVNlfFRbf7ucNTZPV7WQOusOtwE0Si1b3H1wz5Qyj8SV3m116q7hHztQ0SzJ37HASbRGuumViMkEg3AQSTKH94gOlWfkyj0LK15372xNxKWo9C3h9m+Hk9dfxnFzubSclbaUeFi7Cd2v+aJXohmJpSBEestOPcG3HfL+ZXfbOafQekkvHJJ5C0i4Q7P0Q6Paz0iUftw0vWIM3xGse+FDbfDLf8vZ4RrikgYZXGvKke4WXftcXb3u/QNdczVvWA/ODrDcqjLnU/v+1oIiUQDqMVJRHMzRHMVa5CYIga97gZuxOonrXFvfrndLJt4zVA8RpMnt0o7avuT+A1bUA8qr+u6rHvEK56OTXGtGWyuHgoeG2hhwOIk3ATJ7j3fIsc6lU7C/p1UnESQOULc8v/Sd23m0FLFBWZrW/ipxtZNuEpcsMpPSLtdNj2l7nf1m0um1QiJRAModtvSgCo+fHxiXe13NYmEzk7f7Imj2KkgEhqhzB1Q6UM8NudGoMqs5nVyrIXxf/1l+dph3RQQkTBU44rawMBoA5FQ6yTqcxJBwt2/kohSRcR0EHel701suUdRt2LNuDmJiCWKreFn4wexydqchBfmIxKAF0Ii0QCK3ZXFpvtBS5YbxV41kfAd8rfJzUQlftAxT7XrM4IKcV0PzbL+Jdh1JS3RSXgQxxInUdUmG1fYqk1CS9zkcYoNKiR1FZGwxIXNiJti48586MlN1amHAaUer1VjHlWIm7wQchLPA9g5Ce1TXykvr4dViN+JqstJKOP6KxZlTCdfdndFt+JX6VrQ5qZaDweRaCIHtCqUeVV9SpFP7bAQjUAV+ltJJDTETUa8W5l9zXd7FeamqvbpwG29Vis/PXiIm1pEJBy+FdqcRIuyXTaIkEg0AHs6S/eks2+AdjtsrXJ9mhVqEwnFAlAtyvjYUzXvmy+Zsulin80c1Scn4TsYYQvhiMzZRPTT2JRe+lhDZXPv4CSa5BAV3Goz4qb0AW9Q1meftzrchnKjDsiQAio+HdV1KziJFp3e7b5TeuH/O5rO3R00FlZrdhbY0ye6Jnyxv5KXtl4ykEg+7fhbl0hMvuYSJl91kT6RUCwAVR4BN+tuR0luX+jZzVa+Px8NVeC3+UBQ4cJjkxvqP+TVjnhnWf7drE5CpfdS5eou2g0MVD4MHnPNTliKGvkdVPOxWRNYOyLF2uFu1JxEa8LGR22HBy2LxQWmj4CQSDQN++mgmOwrxysqdi4uR5BUsf9uxyfdTT+z/7+SOeht+l66Sn+I2u2K2ZSAblFVzEpbWuzdzXbTH5EoKs2A24+gNofotB4noWxHvLN8aGh2o1AaRygs5hoJy+F43y4q0wndrWiHnUi4D1A60BJXKXUSrRHx2P2BdNb3QtNHQEgk9KEyAbSJCYp9e5RP2vaMVu6FVwolUOUd65PNbIkVjGE4vKGNZG2HODuR8K24DtjqqRkEdXJr1kTUJBKz1nWzRMInEbZbVjVCJOJ2TqI2t6IDuwlsYvPdvt+3c6iqg5muCWwr8LzmJKyYS18RQnQLIc5qdaN2Ktg2vELfnmVP6/zQwWCYG4d74ZVkxlW5iP0SiQa8dCOF2mx4CdG57Y7TT1GhrLTnn9bJLeCGl8NZLRSbPPErlfcL5eQW7ySSs0x5m9wofBsG2Oawb+MJIk7zXb/pRO2wiS2Tz97q+/W8LROePXifHao105YshTqcxM5IJIQQX8OM5vpGzFhPZwsh3FnmQmA6UZVOlPnUweVFX+XNWZqobn8E35xEA0QiPer5u1sfodJbNCNuAv/Ka0OhdC26xBslDq4wfIjzfZWsfR5SmNaCEe8qK86Vp13NcXaIfHxyeb6JRKIbu9jS/T0ahV/fF3ByErndj6v5jK7HdSvgOx7bAoHOrvQvwHuAtJRyEjgVeF1LW7WTwqm0thMJzfDLfqNNNkAkvGIgAcSmnRnn1OKm5jgJv0TC7pviuO86uU6d+j3G3nwts0d/3HFfadrp9W2KeZp1ptKFobBu6nz05+VrL09uB2ySFr+btm+i6fZUboaTaBL2TIZqItFeE1idutvdDr/QIRKliGGlcIYdtnshbCjFTgLTByFihenQXng+lWeNcBLlUN6aliQq23iHOWUDznR+LZyU5pvuTTASIb90WfVzSiKhHvPE9tWBhqPwgtPjurJR9N71tfK1I4+JGwprHv/taISTsP3dDCfRZEhuOxHN7f7S2g8pTWC7lHqMoKCXknjnJBK/AX4NLBZC/CdwK/CLlrZqJ4WdkwDKREJ38/fvJ+GfNS1HtezUC76nTFQfrSxoXRPYqMWlGJEYuSWHab1TboeKSGieXHU4CYeNf7E9HEQZDuVxZaModC8lu9ux1jPq7x1USBH7d3VAU0yjYwLrxvTxnyU/dGBVtF4d2MPr24mMytxX2T9oOoJBGbmKqMxxkLPNr6IqrtkC5CR0YkV/EzgFWA/sBVwgpby2pa2aTzThyGLnJAD/4ia/rL5P5a8DHv0sdg2XvY9V4iY73NZNqvwNPfd8CyOaILPf6cR3rNFq5vRx5xPNTCgtqHTFKTpEotidImaZsvrNd9EsDAWRGH/Tn+i551uweZWnKKKUka5Vp2FlOJEGOYmRj1Y8keeOWs7cUcsbapdXJj6/MBI9DelC3EhuWlm+LvTtUYkrZTcQ6FwENbz0dU1xlUSmBdDZEe+RUl4vpfyUlPKTz2sCAaQPehsAk6d8W/sdo3uIQu9uVQvELm5KH/hWALJ7v1pdjt9ThMuUtOOpv1r11s5jrQu7lYhKce2AWyehEBvEpjYyd8T7KfrIojf3ko8xc8LnmuckkjpEolJHfORhrXIjudrRRv1CFeCv6PDu9yASFlH3FEn5hJ07UWWNcxNf1Tj7RW7pUVrPqSIDN4Kg9AHJ9f8oXxfdB0cr34fqcKMrbjJ6ljBzzCcZe3Prt2MdTmKLEOIVwN1SysbjFuwkMDoGyqccVfwXNyZP+Q6RfKZq07ZbN02f+GWmT/of77DVTVralE7vdmVnIygs2g+eW2W2ySNxUAm6iuti1zCzR5/bUJuU1k2advnKRWl3hrRtsPGRR2o9XgVHGtdovHEdhk0U0YhyNT90kPm/wvSzESS23Ff3GSPeRcSWQCkoTia754kktt4fSFleyO12DInN91AY3C8ghzqD5Pqby39VSRessVJlbfRDqGZf+skG2ucfOkTiGOAWACHKsYgMKWUwiV+fBzC6huyS0TKqxE21CIR1z4hEyS/ev3y7dALJ7Hea7/Z0P/SDStuicVOMMrneTJ05W589t1uJKHUSNtiJRDHRq8yONnPcp7U4kx3vWVn1nMq6SVe8kU8dCpaEK2+Zx6YPfIvTGdJGiOLbNIiE4fzqxd4XEpusH0G3Juxzw75RxGzRdj1k5unD30d+yTLyS4+k87HmDgklxDfXDrdth5HodhCJoJBfemT5Ovn09YGXX8L4G68uXwdhDh0be6ossoQaRMLSCao44IWouK5LJKSUwfGvuxgiRdMPwmvyzR3+frofuIypU77tzLwWiTjkto3CiHczcfoKeu78KjPHfZq+m8+r+47Dc1VH3GRTXBudg0oRQPrAt5Wv3Wy4HcX+varvKTkJXUWpbROOxmuOrT26b3z76rol2gmCQYRC726NEwkbjEQ342/4HfEt9zlEee5TZnr/M+nd8TUyLzIPEvkX6IlodJHYcq9GWzU3Vk0OKzd8CIntj5KzWaglbKK/7F4nOV8IMEx9EBt0zBWexT3PSwmy1JztTkgkhBDdwAXAq63nbwY+L6UMRhi7C8Drwxd7dytvWB1PXtOCurswOgeZfuU3tN/xq5OwK5WLnYMOX4xC91Jis1tNbsNmWZJfcjiTp36P7J4nEinUj9PTLCehA3sdUQ1dQ2Lrg5V3e5YqN6zcC44GIPMiPfciI95FfskRVbb+7k1s7iUfY+6oj7YszIlOHnLdjTX5zN+1nht/W22uoZjsJ5qdrIoym9nvdIybPsnkay/TKt8LrdigC26LRyt3hqGwLvQSeZU4/HY73Okorr8H9ADvA/4dSALNf5FdCO0+HeR2q9iIuxdx+UTeu4RaMGIdjtOPX8W12/SwYBGc3JJq34XMAWdqp1A1OgYciY7K5bsWYTMwuvwxzXE7kfDgjIzORYx8dCOTr7tC6XfieF4VtbfWPJrnOFjuTa1ghcd35zfveuQnvssefev1FDsHGTnnaQo27nLusPcCMP2yCyCWYPtHnia772t8l+9GUOs0N3xoWVdWpbi2/Fm8FNczx5i6htmXOB1C80uOYOwtf2H7Bx4LpJ260NFJvERKeYTt748JIdrbynlCYJm43LGb+veEzascEx8g94KXAJDd/dim6ptd9mEGrABp7ok/c/z5RPJzdJ3xFVj506p3CwP7OO3NfSquGwnwphXHKRKl2DVUjnc1+vYb6Vh7DYXFB/iuTwUVt6KC3QKq0PfCiqOiF2IdbP/gGs/83+oQIu05bGT2O52Op/6i9WzV3O7b3exfsg/yppVdbPyZhsRwhdQh7Hh/tW6osPiAQESxbuhwRdm9TqJn1ddJH/BG9TN7v4quR3+GEU0qrc2UOolEN7m9Ti73L7nW+R3yS46o9VpLoUMkokKIRVLKcQAhxCKgPW6o84z80iMpdg4yfcLnfb9r3zjdm+DUK79Bbuky0ge/y3G/2Ld745PfdqK0b3bFgb2dz8W7mH7lN+jqdp5oS+1158BwK64NIk4HJgIgEt0p8gP7kD7UO3ZksWu4TCQKQwcyWydfhx8UE73OjIM1+umGIxBi3556RIL63JmSSCzAuD5GoruKuLr7F4Seph0o9O+JQcQRjdaN/JLDGVm+ASIREhtuq/lMdu9X0il/a3LtCtNhO6dlJHvJ7X4sudRhZaOKhQQdIvEt4B4hxJ+tv/8V+GrrmrSAEIlUTjKaWcsmT/0eHWuvUXt8AsSSpC2WOUgYkSgRo2hyAxbyg/urX7C/2zXExOuuID/s9IR2L/jtH62RXMcmi/fstwfG3n173WfyS44wN2ZV+AaFVVU9lAhzxJZDuzD4YuJjT1Y9ayckdhT69tD2ragHJTFoISex/f2PVIicNY5GvLviXBZ1bna53V5KZp9TyO15ohZxLOmmFjLmjlpO5sC31A+17iHiK3YMkF96JFOnfteTqBsdixg/8zd03/N/Zd3T+Fv/1lC7Ww0d66YVs99KUQAAIABJREFUQoh7gJMwdRhvkFLWNf0QQkSBS4AjgAzwASnlWtvv7wLOAwrAlVLKS4UQCeBKYB/MGFFfllL+2V124LA2Oc+8CNZpec5moVMLmQPOJHPAmYCpsExsuddhztc6RNj+kfVEctOuJDL6G0u2pFi1m3bqhDdukpPQxfTJX4diVrlIdRStXrB7l+dThzmIxMjyDUSyU+ByFsvt9lISm+8mP3wQHU//tan6y1CFNW8hkTA6BylY367kbZ7b7Rgy+51G3z//PzL7nUHXwz8mOreDQv9eFPteyOTpPwYgu9fJ9Nx9UdlhtBbSh7yTnnsubln7A0Ek2nhCrGiC3G7HkNvtWIjGHUYHE6/7EVErWVcJxc5BCsMHM/HCE7zLLe9N/oNoBgUd66bDgM9JKd8uhDgIuFwI8UEppazz6plAp5TyeCHEccBFwL/Zfv8mcAgwDTwmhPiV9c4OKeV7hBBDwANAy4nEzPHn0/nEH5g65f88n7OLgkospxfG3/RHc8NtoXLRLZ6qEmW0YXLZYzcF6fFbhUjEsz8ly5GmyreQX3I4PPEHx2+19DPTr/giRjROIUDRl2q+NGKiWTIcYM/aUVFrIT5qLu3cbkeTPuRdpA8xxaLjZ/6Wzid+T/rgdzqezy890nM9GJEY6YNbTyRKHHQtIwm/mFv2IXpXfomMeCNdD15R/4VI1OFzYUf2Rf9SdU/lTOfG7Es+RveDlzP5uh/WfbZVhERH3HQFcCGAlHKNEOJLwI+Al9d57+XAddZ7dwkhjnb9/jAwgKnfiAAG8Fvgd7Zn2qL7KPY2oAvQ3fjnPQtb6+rP7PsvdDxzPcX+it7D7ogHFaLRUuJhQVcn4IViopdobpp8qn4AQiPebXo6ewWNCxANcRLxTkY+upFUqg9G/HFaJdPdMhJdpA95NwBrtk5x1lUPcPnbDueoPRbVnufRJIX+vcnucQLF3t2YXfZhZx6SoBFLBqbQnjvyw8wd+WEAPSLhE9q5QToHtfrUCkV+CTpEokdKeV3pDynlDUIIHaP7fmDC9ndBCBGXUpY2/tXAfcAM8IeSYhxACNGHSSz+W6OeXQIjH3oyUMehIDB52o/K10a8i0h+DohgxDuI5GcxYh1Mn/x1jGQfMye0/lP6zsdRq4xkL0Zuhtxw/fAWuSWHt4VA5JYcQeK5VY5sgI1i6hVf0rYI8xK9/GClqYz+v38+zU/frXDii8YYfXdFuTvzMv8GICHmHzpEYpsQ4hzgKuvvdwA6GqhJwC77iJYIhBDicOB0YF9McdNVQoi3SCl/K4TYE7gauERKWTckuRDiQkxnP5YvX8655/qLDZRKafgBLAg01s7e3k56FX3s7a3oGxzjUCzWvO85VolOyM+xeHEPvPt3sOI0ek79L3o6B+At36dRuxzDMDAMiEY9OKKz/gy/eBt9J32YPnvY8g7TqiwWjdL3oiPNIPd9u3n3Y/HeMLQvqRdWUoBWPd9lEuvkvsc6f0uYBCMRj5HoNtsRqfW+He+5GrqHvMf5VR+DV36UVCRiRv4t5ugdSim/K0MVrq2q3Ff/R+W5C8xzWUrB7S7ec1/orl1HMmluHfF4rD1rKG4qzjuScd/1qcZWuxxVXycrs9pvm4aHeqG3teMW1HfRIRJnYyqg/xfIYi61D2i8dwfweuA3lk7CbvA8AcwBc1LKghBiGzAohFgK/B34mJTyJp0OSCkvxBKHjYxMGSM+WOpUqg8/z+9M6Dr+c/Te+RW27/5ajBp9TKX6mJ5OU7LWd4yDUSRlu5+q9YwLi6NJYsDo6AyFIQHnPAVTwFRz4/uZa9Ywncnz3Td7iH/6joIPPwljWcwpaiIuzmLwoV8weexnyPQcTvytf2PwwGO9v/nrrgIM8Oh371yWLmCi72Cytt8GcgWSQC5fIDuboccsie1e9fUfY1WiN86xt/yFvn+ez/heb1KLj5aeyqLUYUyf+GXy1jO+5vqHniQ2uZ7CTAJmar+TzVppevOFtqyhRfkiCSCTzTOpWV/voWfRte/RVXM48v7VRNOjFDTL6c8V6QByRRi3vZOYmKPk7aA7BqV2bN8xjTHnP2mYLvzubV4ERce66VngDAAhxACwh5RSRwB2NXCqEGIl5oHqbCHEO4FeKeUPhBCXA7cLIbLAU8CPMQnRIPB5IUSJN32dlLK52Ne7IOaO+ghzR32kfRW2SGm2evMk2UJjpq351KEOWW0+dVh98ZBGJNDcC15CYuNKcvUsU3wiu8criM5s9nymMHww42+uY8sRiTZnTpnoClYRP0+YPul/6KqhizE6F1HwkRxp5thP0/H035h+xZcc90sRd/NDBzff2AUMHeumD2AqoT+FaW00JYT4mZTyf7zek1IWgXNctx+3/X4Z1eE9zrX+hWgDMvucQu/tFzB36L839P7YbJatUxkOXNrXMsuKmWyBRKy1yv/r12xj78VdHLjUeZoafcfNNZPaZA54A5kD3hB4Oyb+7Zd1nxmfzfGn1Vs465g9iMy7UcSugcLi/WsqhnWVyjs7dMRNH8HkJN4B/AlzE78L8CQSIRY+igN7a03yHWfdXTNu0sX/fJobnxjh5o+e4O1j0iAMw2A2m2egq4kMfBr477+aZ5d7zjvRcb+w+ABfYT+ye7yc5IZbye7xctPk895vM33y1+q/6ANv+fG9jM/lWNrXwWsPqu3YF6IaMy89ryq0ewg96BAJpJSbhRCnAd+RUuaFEAsvPkCIlqHYV9uqZnQ2S65gkCsYLRE3ZfJFCjvRup47ajnpQ88q52puxSlzfM4MELdlsn7k3BAVzB7zifluAmBa4EWMgnZGxYUAnSiwjwohrgVeBNwohPg1cHdrmxViZ0A6V9EV+OUkCkWDYy66lWsf3aJ8ZiZbUP62UHDfhnGOuehWxmat3CHJ9uUe3pVQip6rFZV4AWP78vWm42E0zp8f2cIxF91KcYFzODpE4n3AN4DjpJRZTFPYD7a0VSF2CmTyNoVy3B8nccczowB84bonlM/M7gRE4vxrzHR3P73n+S+bnk9MnfpdckuPZPoVX5jvpjQPS5f0pb+bc3/V+uadQFsJHeumPKbZa+nv4DPjhFhg0FOIpvP2/M7+iES+WP/0NJNd+MGG85ZPiU5/QjSOYs8LGH/z83PryS9wmaoOJxFiV0MkwsjyDXVl6nZOwvDJSehgZxA32XH9mm0cc9Gt/PkRtQgtROuQLxqc/YsH+M0Dm+o/HEIbIZEIURsa5pV2nUQrQobsDOImO66x9Ct/DInEvGAqnWP15qmyKDNEMPAkEkKIV1mRX0t/f1wIcUrrmxViZ4CDk2iBddPOxknsSijpWhvVuU6l84zOZus/2ADG5xa+mHJngpJICCHeBlwO2ENPbsMMFf6mVjcsxMKGYRgOnUR2zxPJLT0y0JzTszuBTmJXxV3rzNP649umG3r/U39+lA/88sH6DzaACctMOEQw8OIkPgWcLKW8r3RDSvlr4NXAZ1rdsBALG/migV1Xm33xGaZiMcDEOCEnsXDRrK51fC7Hpol0S8w/x0MiESi8iERUSlmlAZJSrgPaE0A/xIKFQx/RIuxqROLSO9bxPzeoTYKfbygaMJMJ/hvPZAvkGoz3FaIaXkQiIoSo8gyycj0srMQGIdqOTL71G/jOprhuFlfe9SxXP7xrKb0n0q059U+kQ1FlUPAiEj8Dfi2E2Ld0QwixB/AL4DetbliIhY10vh2cRLjQn+9o1WYeipyCg5JISCm/BTwErBZCjAkhtgCPYeaF+GKb2tcWbJ5M85HfPMTa7TPz3ZSdBu0gErsaJzGf+LcrVnHMRabP7CeuXs0xF91KoQ0Ogq1SMjdS7ndvfdoRYiWECU8TWCnlZ4ElwCnA64AlUsrPSikXtougTzy6eYp7N0xw/4aJ+g+HACCTa/0GPh0SibbhuclM+fr2p03LpfVjsy2vd7JFnEQjRKIUWuWWtTuCbs5ODWVYDiHEWa5bRWB3IcSdUspdxltlZDrDUE+SqMu5bCqdJx6L0JVoTIe/bSrDkr7W5GDw245Ub1I7N0EmX2QuVwg5iTaiaBjsmMmS6g12vkxn8kQi0JNUR+cxDIPtLai7hMkW6SSeT+KmdK5Ap7XPjExnGO7RX69BwIuTeKXr3ynAR4HHhBCvakPb5h0j0xlOu3wVx37rtqrfXvX9lZz4nTsaKvd7tz3D6T9YxWV3rGuyhc3hoU0TnP6DVbz9J/fVf9jCy799O6decmdIJNqIY791G6ddvirw8OCv/N5KTv7uSs9nXn/F3Zx2+Spkg/4Q9TDRIse354tD3bods5z4nTu4UY6wdmSG0y5fxWmXr2prG5RHCCnl2bXuCyEE8BPguFY1aqFgw3hrsqY+vMkUaz20aX7FW2u2mgv/6R3+xQqZUHHddrRqPnph65Qphnpi2zRiSfBh0Ftn3fT84CQ2TaYxgHs3jJfT+G6faa/OxHfsJimlBMKkQzZct2YbK1Y927b6/vjwZn51f/BBzL7y9ye0CVe6DToJXU5i3egs51/z2IKPy1/Cxf98quyx3A587cYnuXuBxjNq1rrJMAw+d+2aKqOT55O4CWDDWPsPCCX4JhJCiBihM50Dn//r41xy+7q21feVG57kon88FWiZ63bM8sdHtvCBXz2k9XyrOQnDMLSd6d7xk/u46YntXPtodT7qhYbpTJ5f3LeJj/9+dVvq2zQxx+8f2sxbL78zsDKNAIlxszqJW58a5e9yhHe4RKbPNyKxcR64yBK8FNcn1ri9CPh34LqWtSjEvGAq4+9E12qdRLZgaOdoKD23M8TsybbZE3i6BR7NQR4QmrVuUhGZVuk65gtbpjLz5kXulXTInQKqCIwCN2BLQhRi10SrTWAXcnC/1ZsnufbRrXzqVS9uuqyXf/t2Dkj1cOU7jwygZe3BtM8DRQknfecO9hnq5ifvqvS1VYTdi5M49w+PsPKZMe45r9Y5eGGiaMCmifnJa+6luH6l/W8hRAJ4I3AO8E0gTOa7C6PVnMRCjtv083s3cuMT23nzst2bLiuTL/LI5qkAWtU+NMqdzOYKPLbF2deW+Ul4iLFWPrOw04WqMF96ibo6CSHEvkKIrwIbgJ8C/wD2aXG7npdQebD6vd+qdqhQNIwqOXQtkUNQ7S0U1fqIdngBl2AYhkMZXqq7bA7a4qa0s69+UMvqrNG2TqbzLenndKZAvkXimfkykNgwPj+chFc+iTcIIa4D7gaGgPcAm6WUX5RSbm9XA58vuOBvj3Pcxbdxtyvp+Z3rRjnu4tv44nWS6Uye0y+/iz88vJlMvshxF99WDpUQFP62ZivHXXwbX/vb49rvHPut23ipy1fEbd307p/dz3EX38amieZPO8ddfBvv/tn9VfePuehWjrv4trYt0q/c8CTv+4WZ8+ClVt07ZrJtWaylvi5EQuHmJB7aNMFxF9/GZ655zHdZBk7x1W8e2MSZP7ybuQDEmeMt4FK2TKY55ft3ct2abYGXXQ8LkZP4PTAOHC+l/JCU8gZMvUSIBnCjHAFg1fpxx/1V68y/b3xihK1TGbZNZ7n5iRF2tMgW+g4r5ML1jzYXbdTNSZRO149taY3TlR25NiWOf/i5ybJpZanGdqfGbIc/il9MuziJu58tzeHGzo52kdPKZ8bYNJEOZP63Qt+xeTLDVCbPg/Pg4zTbBrPzWvAiEocDm4DbhRB3CSHOxVvR7YAQIiqEuEwIcacQ4p9CiBe7fn+XEOJ+IcQ9QoiP6Lyzq0Bum8FwyTJuemKEh5+brHp2x0yWdaOmM9zG8Tl+88BzbWljIzqJomFw5V3Pts0KqVA0uG/DOFmrrT+9ewMjUxnPd+54ZpRVFrdXS14ut7aeCDaCfNHgh3euL5/Kf3bPhpY5XflRXF+zegtrR7wDZ9r1B88FqJxtpRnsyHT12F776BaeHKmeH5sn0zzTgMPqQoFXFNjVUsrzgD2Ar2GG5lgqhPiLEOI0jbLPBDqllMcD5wMXuX7/Jmaoj5cB5wkhBjXe2SUwPpdjZMo5Cc+/Zg3vr5Hu8Rs3reXsXzxAvlDkDT+6h/+9eW1bbKobOeH+7sHNXHrHOs7+xQMtaFE17t84zjm/eZi/PraVW9bu4Lu3PcPLvn6z5zv/+YfVfOx3j2AYBlM1lJ+tCk/RLFbc9SyXr1zPf/z+Ee54ZpTv3PoMp19+V0vq0jUqSOcKfPH6J3jHT73DvpQc6gzD4LkAQ4+08jAyMu08bGTyRb5w3RO886fVYtIL/vo4H/3dwy1rS6tRV3EtpcxLKf8opTwTk2DcDHxVo+yXY/lTSCnvAo52/f4wMAB0AhFMjr7eO7sMdDejiXSO6UyBEdupcWy29Sf1RjyuSyEettY5zQeFUtazbdMZtlmLOqtJ3DL5ItkaYq2FSiTsY1u6bpU6Q5eT0D1IlDbzHbO5QMVrrdBJlLDNxUl4+TBMZwuMTGd32mx52uIjACnlCObpXueE3w/YBXcFIURcSln6cquB+4AZ4A9SynEhRL13qiCEuBC4AGD58uWce+652v0BSKX66N9sLvy+vg5Sqb7ybwO207z9vvt9+/W3b3ySFSuf4cH/9xrngxGTDnZ3J0kkzGFPJOJ0dyesnyMsXtxTfny9LXSzuw77dcKKDpmOVpzgFw12c9/WGc656j7WfPG1dCUrv3V0JMrXXd1Jxzt++lq0RaEcGq5YQ/f3dzqeW7ttilO+dSs3fOJEukv1RSIM9Hc6yl32xb9z9gn7cu4p+1fVG41GHWUOD/eWo2La0dvr/H7920wxRz4apaenEsVUp3+Jns6a9+1itsHF3eVonF1dCZLJ0nd1trdQNNjvs3/lG286nFcdtKRmuarrYdvYLhqofKMeV187O83vGo1G6euttH3Q53e1Xy8erMzHvr5O0rEYP71zHVN5J/Xp6a49tonZ2usnleojHqt8v2I8RirVx4a5ilHH4sU9pIYq9avQ11fR8Q0NmWPVk4wxky3w1RueZONUli+deWjNd1Xj0dfXyfqZHG++7E5WffbVLLXN1UXTJkEbnc2yaHEP+3/ub3zlDYfyr0dUzKHd5cbjZl9j3R2kbGWVMDDQxaOjc7x3xT08dMFrGOiqrNGBUVMqsKg7wbjt8Oeu42d3rmP3RV28+qCldfvnF76IhE9MAvZWRkubvRDicOB0YF9gGrhKCPEWr3dUkFJeCFwIMDIyZYyM6Nucp1J9jIxMMTlpfoipqQz29++UFQsGVbn2+yMjU1x8o5mjWK7fwWLbJoxlkTM7myWXM7uUy+WZtT68YRiMjlZktw89W1kw7jrs1znrRC83VJ4fH5vlHEs09eNb1jrs+TOZykS77f9v77zD5Krqxv+Z2dmZ7btJdtN7Owmk0QIphARBOkhTDKig9BKIURFFBYVXwR+gEAEDBFTKi5QAgoKvSEkhGIohEHKANEoKm92Ubdn++2Pmzpy5O/fOuVN2N9nzeZ59np07987cM/fe8z3fvu7LuGO8jLVGUeWrdsRW13v27I3b7/qn1gBw3VPvcUC/yKVtb2e3Ylb4cFMVu+qbueNfHzF3cv8O39vW1hb3mTt21BIKdFSCa2vjr9+eSKTVl7saqFZs3Trj27hlV8LtKjur66OhwQ0NzTQ1Wdc1/nzfiNRp+tFT7/HSZbG6mG7XVR2rxa7dsWtUZxvr3ohprK2tjZra2Fh3eryu6v/VO2P3Y03NXv6ybAP3rdhMeWF89+K6+saEx6vmHvt3tLTGNNEtO2qprKxhrVLPqrq6joK25CvvmprYWKuqwr9VaV4gahL7y8rNzJsxLOGxTr9HTc1eroz0Gv/tC2u5Vlm47NoV/j3b2+Gx5RsA+OmS9zmsf0yY2z+3JdLq95PPduJPUCRx9+4Gro1Eht39f5ILpw2Lew9gcGlenJCwf8dNL3zI2IoiJpWHFwXW3KaLm0DxXLvJA8uBEwGEEEcQ7mhnsRtoABqklK3Al0CvJMd0OulUaU03dPGjJM4+O05lpN1KW6STwanb47ol8qC3uqjabr+VPwNl82v2ttDqMWzWraZQIgHlRksnRWNlG8sO77WESzKsEhqZ8keoK/FUsZ4bt+dn625vZtOdLj4S6x5x+r4hZe41Vdvb2zsEu2SKbGoSS4BjhRArCPscLhBCzAWKpJSLhBB/JBw51QSsBx4CWuzHZPH8ErKxqp6vP/QWp0/qHxdN1N7eztTbl5IX8LP06pmdfVpJScXOn04kyd5m50n/D0s38tB/PuOBb05x3KdB0/lZEEy/lmRNYwslebFbvbm1jdwc94m+xsWePbaisFtnSavVc3XqX+lW27UiejIdlmtFN2Wq7EQmhIQOX9Z6e+Z21Tdz3D1vUF3fzMr5R+p9R+S5zlZIvA5ZExJSyjbCJTxU1inv3wvcm+BQ+zGdyrIN4daFS96LzyOwnJid0WzHjk6zmVSERDpx124TxePvhsuY/9slbl73fAtyc7QL/Tlhd7RuqKpP2hvBrVzEyPLuLSQ+VUxMqsnn050NDO3VcUV6yeN6lX+/zHDAQSjgp7GlLRrdtDVDQqIgN4fcHF/W82l2JAiDdWNnQzPVEZORbr+LtyI5KOu6MGAim+Ymg4J1v77z+S7e/SKsoTjdwr1sK6FE+RF2tnVSxJDFXk1zUzr4fSR0ULvR2tbOsg1VcWVE7BO+ToSSk5DoWxQklEQLsfhsZ0PaAi6T/O7VxOXldSegRLkB6RAK+Anm+KK/tZu5qbWtnb+++4VWmXKfD8psz9Cm6vpoPpHK3ubWlIWfV03CzdzkhPqblObFr+n/+/nuTsk5MkKik7Ds7u8rK9C3P0vs87Cvcld/kVxIZHqVl4xMmhycQgMLgjl4dUlc9/yHzF/yAYtWbI5us2sSH+kICQe7+1jN7mwfV9ZyxuJVXPnkexm34afK0g3VCZsdXT5zeNJjm9vaHSe5VP1vPqAkL5c9e5tpbWtn2x7ne/iYu1fw23+v5zuP6OXYlObFC4mzH3yLsx98q8N+C5du5MzFq7Rbw6rNjZyEplOY9a4UQtNVk7CqBe5uaOaix1dzzN2Z6xPihBES3ZBBZfFhcqsVTcIpP8Frh690O8tlcoGs9lhQhU+BRy0Cws2TgLhVY11Ta9xEpqNJOPkkdFt4Ws7Ytz/bzS/+IbWOyQbqZfIBt7+6oYN2c8HhQ5N+TpWLFmFNsH2Lgo77OFGaH2B3QwuVtY2uWpdVL+pDzYz3snw9S3pVXTN7W9p4Ya1ew6r3t8aeRaeMdnvZEotUNIlqRbAMVpzXndlUyQiJLPL5rgaeWr0lTkUe3y/5JOMD+hXHYs/VVP8Ptnm3hW+qrufEP66MW8lkyv6bad79PBZ6WhjKnMtM1SY++rKOw257nfsUbcOOU3ST1z7P6nXsClRzxGkT+7Oxqp6nV4dLt/QrDjGwtGPcfiLcTCsbI7H8X9Y2ccer65mzcHnUt5eMkrxcahpbOGPxKq39dfHqvP7b+85CYkddEyfcu5LPdzVoldeobWzlppc+4p7lm+K276pPz1w3JIE/qTMwQiJLtLa188Nn1/Kbf32SUtSGOhmpCywd/4Sdnzz/IZW1Tdy9fKPnY3VId1Wjjk+t9V+YgcgmC1VIWA77RW/ECwnVTODkk3ATEvkRzadKmQxOm9CfgweXej/hDKGacC6LmJVeTqEQn5s/Qu0R8ejbX1Db2Mr8JR9ohR1bdnbLyZyBiGego08iEeqz5PaM/u7V9eyoa+IX/5Bs0BASdU0tPPv+NhavjO97r2oSbtGBFvZS5zoLzGxghIQL6awCn1mzLWq/TCXKQvRNnG2q45+wY5VdbtC4MVMh3TIbdcoErlZZzaSQcPIxqLyr5MXY/QgzR/ZOek4HRYSBPbR41qg+2ueZadRr07sgiN+XWj8Ee60iJ+bNGhH9/+nVW5Pub/cd9M2Q5qWjSdz2ynqt3AJrQm9obtXyxTmVLVHL5ejUV7MHo1jz0ZmTByQ9NpMYIeHC5IElKR/7gG0V4RWnFWsqmkS2qaxLT0io/pRPlZr5BUHv5qaNEV+EfeXrlvdgsUIRUHZNIqCR1aceoyOUvKL6VWo0u8O5CfDtNY3auTK6kU2jymOLmz+6mPMsSmwROzrmLx0hZ48ESsTabTVRrcptfyvC6GPNBFenzn3qff6ZhpBwujY692ImMULChYlpCAmAPI+ZuSr9i2MPyzDFFtldImVU7BVrveJk/08nkW61TZg6/W7qhGMJib5FwZTaalox7UCH5lKZQJ3w3/psl8ueMbbVJJ5ovIZOpuJ01cG+4u+voUnoCCwdc5OaOX/CAf0c99MVDhaJOvfZ+VSjgVAmy6angxESNtTVmn2V44VQwM8xoiL1E1EWC5MHpSesUqG6Xr9qpdd4cTtWJJCdIhch0dbe3iHsd5fD54CzqU01AWyKOGBzc/wJy4QnQ/XNrMhQcyKna7AhYspMZupzev/NLAgxOzqrefUZKwyGk+BUEp2/TtCFjpD49mGDo/+fdEBflz29odMDPFmXudrGloyWTU8HIyRs6DimdJg3a6TnGj9OTB6Y3PGp80C6oa4U29vbOe6elUz/3TKtY91Wdm9FckHedfGlqJrEyD6xqqVumsThty/lpEVvxgmKl9bptZRUtfVEtvb6plZSSdZVhcRWh5h/dRGiY+d3Ctfd29LGb/71MScvepO/rPrM8XinCaszOuxdM3tk0n1UTWKArULqdx99l5MXvRktkGihM3nq+CS+fdiQ6P9FSiSd6sTWbdy0StEi3UqpByNC0M3ctHxjNXMWruCFD/TCcrONERI21N66fQoSx33rJMVMHVbm+J5Xk+IkRZModggL7Z+gBLEX1JWv1/lR16nphGqrnTGid/R/nTyJz5We2roOdNXfY+8LAKn7E3RyVdSckPOVxDCnhDS72SzuvYjg9eqnamuHNzZmX5OYOrRX0n3UxY3dH2GVPllrC/vWiRbUyZNQs/nV5141Tb6xSe93+ukLsZ7xbprzOXctAAAgAElEQVSEVd7HrU/6+oh5K9G92RUYIeFCbiA2m6u5Duk2nhnXz1udd9UnMXFg4mPTjce3el+ngl2TsKJBdKtSquam6YqQyGSehIoqJHYoAm5knwL6FgXTruCrizoJbHVYHb/nooGlep7rttdkzcfgFTW6STdnY1NVPQue+cA1S1o1N9lDSb2g+xurZjIdn4Tb53aXa2NhhIQmauZjukIipNxQDQ6Zz2p9IJ/S4MfJ9KQ6/DbaatRYN2SOiwaTTu0nuyZh3f5rtugl/qnmJtX/kskQWJWpw2IrXHWiVgVUOgzvrZf0dPNJ46L/O5k53bSEVG3WiTruZQMdjVn1SQwo0Vvo/FNW8vr6Kq56ak2cA1gdlaqFdkZds8VKxWMdn4TKf20LASMk9lHUTMtMtrBU0/xVgg7+DCcntqpJ2LUCy1Zut9NmKpTOac5JVFAtEaqZRi3hnS0hMUhZsaoCbkaGhESx0v3PrdrnV8eFnaV9CoOOmbxulXrTqZ/ltmBwwmuZlGTl2CFeSAzS1CQsdje0xFVlfu2TWJa3urDqjCghtWSGU1kOJ557P77idCo1nrKJERKabKiKhcFlUkh4bS50YP/E5qb+yirMHlljRfbYIz501ftUsYed/tnBweo1BPbSv8ZKW6sh8xMHeG/XqGoSmYwiOzzik0pm0y4K5dC7IJeNVd7ug1SxrByTByUPhrj08ffiXlekUJspGapfIN378fevbUi4PRUh4eSw1jHr1nnUJHY1NMdpQaloEk2t7XHFQzOJERKaqCs9nRhnr+hmQ6sPlRpFod68Tj0veuXnxp37wDSd3YnYqVGfxp4o6JST4JRMpz4MqrN6vEdfD8T3BNBZ+TphD1U9ICLMk4U6WmQqqk4XJ9PaHUo5cftkVZGG30unxLe9RL4O6u9b59A8yatZ7h9rnaPkRijRd04h8ok0CS85U2qNJ52oxXR8LjoYIaGJ+hB3lw4Baqy7zgqnOC8QV0WzVLNSpoVbaJ+Fk5Z10KASFp41EYAZI+MnKKeH28nc9NpVMxJu769p01axzE0HpVlfyS7o/D59e057O47mppwsZdc6mdYeffuL6P+Pn39I3HtOVV51NACtBlcefjOLRKG89i5uXjUJt3NVQ7SdNKu6BM9JIMfnGJloRxXOOr9tVZbNU0ZIaKJT/bGzUc1K5UXpqcE6JUh0SgnILzNnNtHxSaRQhigOt7BV9ftfjdi7ncpZp1PkcFvNXkftb4KDedFOc5u31eSo8oKE2w8d4iwsKxzusaFJ+i9DfNKiIw4Xc5lD5J29PMcl04cBHR3BWzz2onZDNdk6/R5Oi55eBUokl8OCprGlLc6qoAoJKwrw8Xe30Kzch9lubWqEhCbptPrMFss37ow+VzqOSLcsW8uJ6oaO6SST/hqdshzVaZZfdiPRys+pH7RbmQv1QU9kqnSLhtH1kyxdH55IdcOrfQ6r9jvPnOh4TInDSlinhLWOEA0FYtdbnficbO12AT9nTHnC/byamy6bMTz6/zFjnasmVBQ6aBIO94gqYNTfbILiS7Ob+NRcLbWywYJnPoj+r5vwlypGSKRApjKpy203mVM4rBNVdU1xnbIs1Jht1VauquZ2K8Y4jTLE2fDFuJGvEU2TTVt+ccQerNOgyW0SVKPIfvdqYueqE5McQp6DcXH5rdEggXTNZm5+GSdrkI4vodqjScTei0EHp/PzutLuXZAbfTaLQs73oK6P5suaRmobW+N+pyGK9qVWCbZHNjmNSTWhGU2iG6JWukyHYbZ4+lSiMBI5fY9WVlRqPSN1hTOgND+uFIGTIqIm/eiYmzKJjl0/W2bAvICfYGTCXJ9AENvR7QyoI9RUR+SB/RML7zEVse1qSQj7L5aKM9gJx/wvDS1WJ6xTXdyorX3PnjIw+RdkEXs9KQvdkF3r3igrSKxJBBXBrFsvTa01ZTSJbohTrwev+Ii/AZ3q/Tjh5NM8TjEd7WpIfAMFA36G905sl1bj79WSCLqROio6VT3TQUdIpKL5qZErOia0dHt+q9rGU0ofBjXCSzVLqVE2bqTizHfCq6ar4hbWef85k5k3awR5uTkJ3RJ9CnOj/Z2PGJ681IcbOsEXdo6IJF/a/TheqxyoAnuozUT3nalDOnymm0/umtmjgHDYt9EkuiFeW1i6oTqMvWoSEwYktlertk+7w1AnWEYNC1Wd46q5SV1cuYXpqfZ4q/ObW90ar9h9RWq2s+XYLEqhvEexMqZ1GfSzzB4d34DImhTVScPeMc9C1eQGl+mtYjPVPrU0LxCdYN1MME64Oa4nDyrlW5Fie//+ONzfwT4hW6bHXNsNbJ9sk6Gb4KliabReItYsVKGkOq6H2Jz9VoUFr5FN5UUhIyS6C2r4XyaFxGjFbOBVSLhlCFtO1zybXf/wYclXYqqDTPVjqCYV1cno1k1Mfabf+TxsQnjhg+3adZ0s7lkWa73qJuh8iu3D6gdebJvUdMwEqpM2kxFblypOUYiZANXQSqe8kc/ihHT8j3CiQ6nrTAmJvsUhaiPnWhgMdPCnJcNJo7Vj/R7fO2KY1v5eS6l0dj6KmsejLt7KHcJnVd9W7wI9U6EREt2EkX1iJqbRGfJJQPzD7jUKw01InD6pP9BxQtXpkaEW7FPzKlTycmO3jm4mrroSe2ldpdYxza1tVNc3s/jNWLa2U2SOHUvAqWUyIFye5LWrZvDG/CMdBU5JpPBcS1u7lk9Cl14OD75qPnJaHbv5hI4cmbhFaqaEREVRMBr/XxTKiQY66PZd9ppFbBfsToyp8PYsdnYouyok1GvvpJWoGpfufW58Et2E4cpDbF+dZwqnSqBOjO1bSJ8kKzp1ZaL7QOuU/lbt/Gq8uFrfx63xi+7DuiXip5noYFpzY/2O8HcU53W8XgXBHAJ+n+PvZ5mbWtva0/Y36DBCWYTMd+jD4OazsrS/v777Rdz2dEvIW1QUxjSJohTaymrlSWii2up1plH1Xu1SIaERROD1d2pvb993NQkhhF8Ica8Q4g0hxKtCiNHKe/0j26y/XUKIS4UQuUKIR4UQK4QQS4UQ49y+ozMZ5tH2mQrNmtU5e+Xn4veFVxpXzBzOtw4d7LjqUG86XfutTnvIPMXcpMaLq079WaPiNR1Lu9FVo1XuPts5fl/l8pnDgfiJxC3T1clskk5XwlRQNQndQoNWqYfx/YrYEBGIW/Y0suS9WMG4IZq+i2SM61ekaBLOv80hQ8I1q+YeMihuezrJhnZmjvRmYoqrkJxmjayTIm1OfzBnVNx2p99km4O5SUX1L3nNx6ptbMl6Vd9sahJfA/KklNOAHwO3WW9IKbdJKWdLKWcD1wHvAPcBJwIBKeV04JfAzVk8P0/oRpN0Bv+8fBpvfn8WAKdM6M+8o5w7gKkPp676qmoSTtFJcZqEss/YvkXkR0xRdjPP4LJ8Vi2YxZLvTdU6DxVd7W32mHJWLZjFhdNiNm03IdHXIWu2s4WEmrWse52syqNuJiXdz0pGKOCPOmHdom5CAT+rFsxi/uz4STSTmoQ1IeuG96oO4C0eIwjt9C0OsWrBLL5x8KAO761aMIv7z5kcty3e3JR4QZKKQ9wi26YmyK6QmAm8CCClXAkcat9BCOED7gIuk1K2Ah8BASGEHygBOrVmbnt7e7T8gh0nIeEU1+x04dO5IVIhlYqSamVUtc6SGqap+iRUp/4BGmUkCoI5HDUqsQ09U0wfEXPQF7tM+E7+FFXAOV2x2sYWz70DnHAqDa9LYcSGn41KrRaWU7kwhegmp7IjnYHX6rKpRMNZ2BtlbVdMyKGA33O5devzioI5Cc+rqi77U2Q2l0slwG7ldasQIiClVEM3TgE+kFLKyOtaYDiwDigHTk72JUKIG4BfAFx++eVcffXVnk6yoqKYkq1h5+xDtlLWZaUxwTBsYFncMRYvfFSVcPvg/iXkRZyfvZV8hMnDevFJxBmcmxugIIHpJT8/SO/eMbON+rnq/06U9SogP7JqUU1YeaFAXEJUkbKKLusVO0e1YNjxkwdF4/aH9ilgQ6S0ebGyKho7JDYhTxvXD9/LnwBQUJBLbm4gOlb13L9xxDBeW99RIDuNVf2/vDyxb6WoKBTdT92nvCxewKufNaJ/CazeSm5uDhUVxfj94cl6cN8iAh+FnesjygvZEHFe9+5dSH5kBXvR46tJRG6un0Lr9/FBaWlMS+jTJ3Ze6Yy1sChEICJYQqEAPzl1Amu/rOPOcw7iFfklv3guXLahl3JdKyqKOffwoUwZUqb13b17xe7B4uK8qFZSWBBkd1N40g8EcigsCCU8PtehZEpFRTGBnBx8fp/j/VyqPHuFBfFjtX5/v99HcXFMAKjnq37u2IGloOSeJNqnV69Cnr1iBjf//UPOmTGCOyJlx/Pzc6mLPEOBQE7cMWW7Gjt8VlNu/JS6I/IsXTxrJBUVxfQpDlJf3RB3XUtL8ylIYOINBgP88MQDeHfLHm7/+hSG9yngyfe28aPjBeURH5a9HL/XuUKHbAqJPYB6ln6bgAA4D/i98no+8JKU8johxBDg30KIiVJKR4+ulPIG4AaAysqa9spK/ZrqFRXFVFbWsGdPOGpkh+1C7dodc3Lt2BGL8lG/43f/+ij6/8ebY+GiVVW17I30SdjwRUxWBtraaW4O/wzNzS3UJ1DDGxqaqK6O2U7V79MZ366d9TQkeED3NrbElWyuVcxKu3bGxqqufsaWxiaAgcWhqJDwK9Kmub6RUMBPY0sb1dV10e+or2+OG6t67jU1iS+p01jV/9VrkZ/rjxZEq61tjNuvvDDIjrom3t4QL4zUffIjobjNza1UVtbQZhXKa2rh04iTc1SfgqiQqK6uY92W3bjR3NxGnfX7t8NupQ93VVXi+0hnrCp1tY20RFbnjY0tNOxp4L6vT4K2VvYq4aY7letaWVnDNRGfjc53V++M3YM1NXtpi1zzhoZmmiLlsFtaWqmrb0x4vFM9q+1f7qGltZX2tnbH+3m38uzV1dvGGvnctrb2uPtIPV/1c0scMqbVfXburGNMRRF/OGMCO3bUxo+1MTZW9Rh1frC2N9km7S27Ghjfr4iLDhtMZWUNJRGnv3pdd+9uoD6B2aipqYXa3fUsOnsStLexY0ctd55+YPj4qsT3hXUe1tymi5tAyaa5aTlhHwNCiCOANQn2OQRYobzeSUz7qAZygeyEEilYES7HjauIK+6lQ1QFD+Y4ZuaudCms54TXMt466OZ3qJVO1SJ7quNb9Un48DmWkdZBt4RyItSoIDtWHSO3ZDg3n4RlqrP/bm9uDpfBOG5cBVdEJt1sEUylhVw3Z0+D96zndEi3mZGIRAWO03h+7EUpm1rb43xGw/sUUJIX6JAU2J3JpiaxBDhWCLGCsFn3AiHEXKBISrlICFEB1EgpVdf8HcBiIcRSIAj8REqZ9ZZdBw0u5R+XHE6fwiAPv/W5p2NH9C7A74dtexodhcQbCWreJ6MwGOCJCw7NSFmLOWPK+d4RQxF9i7j+hQ+1j5tg6/SmtmhUfRIQDoNNNZO6ND8QpzY/d9FU7dalI/oUxJUOUbHs/G4NbyqKnXwSsUfDqQzLzJF92JZin+lkvHbVDHbUNRFIoxFSd6Uqi5V7E6EKibL8XM+RVhdNG8asUX20Fll+n4/CYE5cnTQ1DPmnx46h7qiR+9R1zZqQkFK2AZfaNq9T3q8EptiOqQW+nq1zckOnH0Mi5s8ZyR+WbgKca/w4JaSp5Of6Cfj9cZOlU20lr/TKz00pS9xe80iNwFEzriE9h2k4cS022Q7wENs/Ks2oM53opkxm2OtSEMxhaDD7YdddQSYjnXRQy8aM6FPAu5+7mwsT4eUesAsJVZPIzfFTlr/vCAgwyXRpM214LPon3Ro/2eo5XZZCXkIi1MqV9naMc8aUM2lgiedm9qDXotGJsRXOD+8pB4Zj2q26QIkoDOYwZ0w5M225CcV5sd/MKXRxf0LV3CZF6okNLcvntInh3JZpHstfgHNUmVsPEKu21URbE6xvRkJOz5g8IJqrcP7hztdVRQ0DHqm5qPh2pODeyZF7yAv2KKRsF7lM5/nRoXODwfdj6ptaqW9Kr5T2gJJQRpv2WGSqXLS6IrJrGV8ZW8FXXBq0uJFOTkJuwNm2e8iQMlYtCOeT3BqJuLLj8/m49dQD4rYVRrKxvVKSF3Csu5QJrOCAbPCq0hL2gW/GFPzrvzqW6786NqXP9Pt8rFowi093NnDm4lXR7W7tNn972oEJt590YD9OUiZs67r+7f1tCfd3QldIfPPgQVHB5BW7kMhUaRQnehcGtUvVp4LRJGyok19IsRsmmzPace59neOPlZ0LuUxq2dIkMiUkcuLyJPR8BpZfwJ4HoP62buU7EuF0LdxKguv6f/0+X8orM8vkZr/G6tjthfm8oAYO6OZVBLuB7dt+/+3MoE/Caxn4zkiKtfvTvJZrtyoShDSvnddii17p+juom3HaxAEEc3z84ayJHNC/mPLCIDeeIMjN8XNA/+KUolmmDCrhvnMmE/D7mHvIYMf9UjHV6JApc5OK7sP5w6NHk+ODHx8zJm77tBG9KMkLcMsp47U1ictnDueA/sUduqf94ayJBHN8nDZxgOOxuglS18weyffnjE6+YwJ+deI4/D647tj4lffUoWWU5gX49cnjKc3PZXR5YcL6TGdNHtChlLjKkLJ87jl7EsEcH2dMch6ryog+BQwsCXGLS1vSbFMUitfMqjOYAHb02AoKcnO44/TEGogdt2i4TKHeazl+H709mitvPfUAcnyw4OhRyXeGpPXb0sWYm2yEAn6WX3Nk9PU/Lj0i+v+fzj0opc+cMaI3kweV8sb8I1338+Kw9YJXTSIv4HfMkB3fr4gPt9d28Ek4Mbgsn5WREiIqfp+Pl6+YDsRneLtxweFDueDwoR22Tx3WK+6aJaI4L6ClkqdqMgv4fYwuL4yWS1Hx+Xz8KzJWgMe+c0jCz7jWJkjtDOmVz6FDy5KO1c6zFx3uOW4+k/h8Psryc6MlJDLZlzzg9/HavLCpbINGXaY+WVgw2VH7bfQrCsZp4Mnw+8L+mETPjBNGk9iHsK+uy/JzyfH7OpRxHhiJEhpkazwyqrwwvPLI8EVXSxRb3znUFjmlrkbc+kNYhHIzd+ukkxNSUainyqeTi6FDWX5uxuokOWFvxNPZDIoUohtsu2+tAnUDXcwq6j3otd91Jsn2NYL4Krle/RGlebmeS/cYTaKbcv85k6OVUK36TaPKC+Ni9s+fOoQTxvdlmG1Cvv7YMRw0qISTDuzPwtdjzXQGlubx1/MPTSsxLRGqzf+S6cMYVJLHebNGsujlj+O++zenjGfyoFKuf+HDuC50icgLZC7HsSTPeXX38HkHs7elY32kFy89gv9+sTsu4sqNbAuJbCQ/2sn2ijEZ95w9iX/KSk45sB9vfxbrq338uL60trVz9BhnLUy9B6vrmzoknWWDhWdNZPyw3jh7C7ODWtuqn0frQCqm4fLCIH84a2Jcyf5MYoREikweVBr936pRL/rGC4m83JwOAgIgkON3tJ97bceYjKJQTpwN3+/zcerE/h3yHCBmatG52VLpG+1EmYtPQjj0wOhTGPRkGnIr8pcJvDrf90XycnM4dUL/Dtt9Ph8nH9hxu0qv/HhNIj9LPVlUDh/Wi4qKok43s6WjSaQSZFJeGOTQoWXJd0wRY27KANY6JZWkqyMjPRcSPXyZIJWbrkJjxWrPuE6HzshDGJVlh2VXCAmrZekxKfpROhPV3NTY0kaDx74J3RHreR9r646nOq7dhIRV0UCtnOzUudANY27ah0hFSKix/NmgLN/7DVSh45PIoLnJi2MvVVJR471cl1IXk1m2+NZhQ1wTBbsTdiFaXd+sHfzQXSnLz014j6ghsG6JdA/O7RgIk8piwziu9yHU3ted3TfCiVRWJjo+kX39Ac8Eqmwr6wSfhC6dURPQur9173P7fdgZLWG7Cl1NIhFeNf9gji8umiobmCc9g+Tl5kTbNmZbBdQlJXNTJ/sk9jWuOzYcqjp7dHl0W2mK5qaBJSGO9NiOMxnHjQuboX5+XGqZ0jpcFskX+qFmLH+mEjq7irvuuoMrr7yYuXPP5IwzTuLKKy/m+uuvTbivOmmriXRbt25h+fKlCY+p27WD4LM/ovrjt6LbVq16k1/+8mdx+y1c+Dteeunv4Rf1Owmt+hNXXXUJF130be6441ZaWjKfed19lj/7CfNnj+rQurErScXMolOsTzfjen/kjEkDOiSzpeqTePaiwzNxSnHk5eZk1YQJYa3Zy3dkI6GzM7nqqvkA/P3vf2Pz5k1cdtlVjvuqmoQaVffWW/9h69YtzJjRMc/l41X/pnX0LNYufxHOPyvp+bS2tpC7cjH9j/kOC+efAcDtt9/C4sWLuPjiy7XHpYMREvsh6qo21WiJUMDvGoHSU81N9tad1m9kryZraZLdRaPsanql4Btz4/evbeDlSOdAixalC+Op970JgN/vjzWSUrbb/5/31PscP74vV7v0i3fi7rt/z5o179HW1sbcud9i0mEzyVn/Ov7P3uHSjx5kypSDufDCS3n00T/T1NTEhAmTmD59ZvT4trY2Nv13Ka1TL6Nt3cNs2rSR4cNHuH7nuvdXQ2FvRo4dH912xRVX41IVP2WMkMgAvztjAl/WpNdgPRM89u1D+PfHlXFhtKn4JHJz/Nxz9iTXCa6nCYk/n3cQKzft7FBi4dSJ/RlUlsfBQ0rjtp8xaQCbq+s5f+q+4VjONvu6ucmJZctep7KyknvueYDGxr1cfPH53HnwYfg3r8J/6De494ZvsmTJk/j9fubO/TZbt26JExAA//nPSg6acCDHf2MqlavrWLLkCebP/5Hjd/p8Pmp2VXHMweO4ShFqoVB2KjYYIZEBZqRQRjkbjK4oZLQtHC9VM4i9VLNKjo99qmlKJhjfr5jx/Tq2eMzPzWHmyI71lnL8Pn5wdGo1oPZHSvID+H3h+zETGddXHzWyw6p/Q1Ud33jobQCei5jxrHIkh932enR7ov/vPHMCY1zKzjuxYcMnfPjhWq688mIAWltbqarcjv/wc8nf+DpXXfUqEydOdm189be/LWH79u08vfAGWlqaWb/+Yy655ApCoRBNTfElTBoa6gmFQpSWluFrWBbnP9y5cyfr1n3AtGkz7V+RFj3rSe9BDO2VTyjg1y6N7OVzR5ZnNuegJySipYPlCB2RoSZUXYHf52Ncv2ImDSxJWlHZ4ojhvQCYMqg0yZ5hnBpIZZNhw4Zz6KFTWbhwEb///T3MmXMMAwYMomzrW0w6+UIWLlzE2rXvs3bt+/h8vg7CYufOaj76SLJo0UPcfvtd3HnnvcyYMYsXX/w7w4ePRMoPqa4O92hvbNzL6tX/ZezYcUyYMIlPP93MunXhTpNtbW088MAfee+91Rkfo9Ek9lNOmziAEw/o16Fiarr88sRx0SbxmSIU8PP6vBk9zoSlyytXzqChubVTspSzyQPfnILfByfcu1JLm7jrzImexl0UCnT6fTRr1hzeffcdLr/8Qhoa6pk9+yvk5+dzwVen8szTv2bea73o27cf48YdQDAY5JFH/sSYMYKjjz4GCDvC58z5Cn5/7JxPOeVr3HLLzZxxxtlcdtk8fvCDeYRCebS0NHPOOecyYMBAAH71q1u4447f0ti4l4aGBiZOnMT3vndJxsdohMR+TKYFBIRXhP4sBOLv6xNgttkffh+rXHivAn2Tk9dxZ/t3OvHEU+Je+3w+rrnmBx32O/P0szjz9PgopXHjDuCxx56O23buud/pcOzEiZN5+OG/AnD00cdEBYqdwYOHcNttd3o6/1QwS7d9iDEV2a+FnwksW/yZk/V6Hhh6Fl77K2QTq9vdqAybUPcnjCaxj5DtuPdMcuqE/lmrRWXY9+ndjXImbjhecMPxoqtPo1tjNIkeTLbT+Q2GRHQnTcKQHKNJ9GBOGN+P59ZsY0GSUM2Lpg3dL2zihu5Bd9IkDMkxQqIHk+P3seicKUn3u3j68OyfjKHHkOnOi4bsYsxNhrTpjK5s6WJV6B1Qmp2sVIM+RpPYt8ja0y2E8AN3A5OBRuBCKeUnkff6A/+r7D4F+LGU8l4hxHXAqUAQuFtK+UC2ztGQHkvnzWB7TSOFwe4vJA4aXMpj3zmEw0U/qqpqu/p0ejRd7ZN45crp7N7rLev7rrvuiCa27d27l4EDB1FW1oubbrol6bEffyxZtux1LrjgIu3vu/ba+bS3w6233hHddtZZp/DII08SCoWTBjdv3sRvf/s/LFy4iLa2Nh5++CFWrlyB3+8nGAxwxRXfZ9So9LP+s/l0fw3Ik1JOE0IcAdwGnAYgpdwGzAYQQkwDbgbuE0LMBqYDM4ACoGMAsqHb4NSetbsyurwQfyc0ODK409WaRFEoEFepVQcvVWDtjBkjGDNGP4Jq+/ZtNDQ00NzczBdffM6gQYOTHvPII39m9+5dLFy4CL/fz9atG5k37xoee+wpAoH0pvlsComZwIsAUsqVQohD7TsIIXzAXcC5UspWIcRxwBpgCVAC/DCL52cwGLqATLSrLVz+K0LrX0i+o99P77Y2loX2AtD7z87mxsZRJ1E342eO7yfinXfe4p577iI3N5dTTz2dUCjE008/ES2/cdNNt7Jhwyc8++xT3HjjrznnnNOZOHEyn366md69e3PTTbeSkxMfFPL8888yc+ZRhEIhlix5kiuvvCbpeTz33BIeeOAv0cztSZMmcf/9f0pbQEB2hUQJsFt53SqECEgp1a4YpwAfSCll5HU5MAw4GRgBPCeEGCeldKwDIYS4AfgFwOWXX87VV1/t6SQrKjoWbesp9NSxm3Hrbc8mxXkBfH5f6t9dEAS/nks1x+9ncFlyjbegIEhBkvMpLs6joCAYPe+ysgLa2lp44olwJvW9997Lgw8+QH5+Pj//+c/58MN36devH6FQLhUVxWzZ8gUPP/wXBkNVedcAAAnaSURBVAwYwDnnnMO2bZuYMiUWPNLW1sYrr/wfjz/+OIFAgJNOOonrrvsheXl55OT4qagojpqb9uwpIBgMUFFRTHNzI6NGDYo719GjM1OBOJtCYg+g/uJ+m4AAOA/4vfK6ClgnpWwCpBBiL1ABfOn0JVLKG4AbACora9orK2u0T9CqENkT6aljN+PuSFf8Hn0KctmztyX17z7o2vBfEjxf7yT71tTspb6+KfqZu3bVM3DgkOjrYLCQa65ZQEFBAZs3b2LUqHGEQsU0NjZTWVlDaWkZgUARlZU19O5dzvbtO+PO7403llNTU8NVV4W1h5aWVh577AlOPvlrBAK5bNlSTVFRuFrtli078PsDVFbWUFhYxKZNWyksLIqO+8knn+PQQw+Lbkv2OzmRzeim5cCJABGfxJoE+xwCrFBeLwOOF0L4hBADgULCgsNgMOxHzJ89igVzuk8Hx3Sw/Fy1tbU88MAfufHG/+Haa68nFAp1qPrqS9IT/Pnnn+Haa3/G7bffxe2338Uvf/lrnn76CQDGjh3Hq6++HN135coVjB9/IADHH38yixffF/2+d955h4UL7yAYTL8ybjY1iSXAsUKIFYAPuEAIMRcoklIuEkJUADWqKUlK+bwQYhbwH8IC7AopZWsWz9FgMHQB07tJD5ZMUlhYyMSJk/nud88jPz+f4uJiduyojFZtTcbOndWsXfsBN9746+i2SZOm0NTUxJo1q7n88nnceuvNPPPMU+Tk5DBo0GB+8IPrAJg791vcf/+9XHLJBQQCAfLzQ/zmN7eTm5t+kIDPrRnGvkZlZY2nwfRU0wP03LGbccewGu7sS3XBvGKut/b+jipO9w9wNxgMWeGhuVOSmj8MBiMkDIYeyoEDnFvUGgwWpiyHwWAwGBwxQsJgMBgMjhghYTAYDAZHjJAwGAwGgyNGSBgMBoPBESMkDAaDweCIERIGg8FgcKa9vb3H/o0dO/aGrj4HM3YzbjNuM+7uPO6erkn8oqtPoAvpqWM34+5ZmHGnSU8XEgaDwWBwwQgJg8FgMDjS04XEjV19Al1ITx27GXfPwow7TfarUuEGg8FgyCw9XZMwGAwGgwtGSBgMBoPBESMkDAaDweCIERIGg8FgcMQICYPBYDA40iPblwoh/MDdwGSgEbhQSvlJ155V5hBC5AKLgeFACLgJWAs8BLQD7wNXSCnbhBAXAZcALcBNUsrnu+KcM4kQoi/wNnAs4XE9RM8Y93XAqUCQ8P39Gvv52CP3+p8I3+utwEXs59dcCHE4cIuUcrYQYjSaYxVC5AMPA32BGuA7UsrKZN/XUzWJrwF5UsppwI+B27r4fDLNeUCVlPJI4ARgIXA7cH1kmw84TQjRH5gHzACOA34thAh10TlnhMik8UegIbKpp4x7NjCd8JiOAobQM8Z+IhCQUk4HfgnczH48biHEj4D7gbzIJi9jvQxYE9n3z8D1Ot/ZU4XETOBFACnlSuDQrj2djPME8DPldQtwCOGVJcA/gGOAqcByKWWjlHI38AkwqTNPNAv8P+BeYEvkdU8Z93HAGmAJ8DfgeXrG2D8CAhHrQAnQzP497vXAGcprL2ONznvKvknpqUKiBNitvG4VQuw3pjcpZa2UskYIUQw8SXjF4JNSWpmTNUApHX8Ha/s+iRDifKBSSvmSsnm/H3eEcsKLnbOBS4FHAH8PGHstYVPTOuA+4E7242supXyKsCC08DJWdbv2+HuqkNgDFCuv/VLKlq46mWwghBgCvAL8RUr5KNCmvF0M7KLj72Bt31f5LnCsEOJVYAphlbqv8v7+Om6AKuAlKWWTlFICe4mfBPbXsc8nPO6xhH2MfyLsk7HYX8dt4eW5Vrdrj7+nConlhG2ZCCGOIKym7zcIIfoB/wSulVIujmx+N2K3hrCfYinwH+BIIUSeEKIUGE/Y+bVPIqWcJaU8Sko5G/gv8G3gH/v7uCMsA44XQviEEAOBQuDlHjD2ncRWx9VALj3gXlfwMtbovKfsm5T9xsTikSWEV5wrCDt7Luji88k0PwF6AT8TQli+iauBO4UQQeBD4EkpZasQ4k7CN4sf+KmUcm+XnHH2WADct7+POxK9MovwBOEHrgA2sv+P/Q5gsRBiKWEN4ifAW+z/47bQvr+FEPcAfxJCLAOagLk6X2AK/BkMBoPBkZ5qbjIYDAaDBkZIGAwGg8ERIyQMBoPB4IgREgaDwWBwxAgJg8FgMDhihIShxyGEmCCEaBdCnKls2ySEGO5yzOxIkl4q33eTEOLeBNtfEUKc6nLcDUKIG1L5ToMhUxghYeiJfJdwfatLOun7HgTOihQfBEAIMRQYC/y9k87BYEiJnppMZ+ihRCbqc4EjgRVCiFFSyvXK++cDJxMu5zGAcLG8BZG3K4QQfwdGARI4W0rZKIS4GfgK0JtwYcFvSCm3W58ppVwvhPgAOD7yeRCu1PuwlLJFCDEBuAsoinzvr6WUcZqHEKJdSulTznG2lPJ8IcRhhBPKCoAdwCVSyo1CiO8D3yFctuE/UsrOEoiG/QyjSRh6GicBm6WUHwHPABcn2Gcm4UJ5BwJHAKdHtg8lnMk8HugPHBOp5z8OmB6pH/QpYQFgZzHxGa7nRbYBXEi45v9hwBzgtzoDiWTZ3g/MlVIeTLjk/X1CiBzgOsIF/w4BgkKIQTqfaTDYMULC0NO4AHgs8v/jwAWRyVblWSnldillE/C/wNGR7aullBullG2ESyCUR5pVLQAuFELcBkwjrBHYeRKYLYQojKz+qyOF+IgcnxdpGnSTw/GJGEtYq3lOCPFf4BZgpJSyFVgBrAJ+AdwmpfxC8zMNhjiMkDD0GCId604AFgghNhFehfcivj4/hPtvWPiV1+r2dsAnhDiEcDFFP2FBsIRwPbA4pJR1wAuEG159C3hAefuvhLWVtcBPXc7f+lzLt5EDbJBSTpFSTiGsNcyMvPc1wk1mfMCLQoijnD7XYHDDCAlDT+JbwMtSysFSyuFSymGEO5ldatvveCFEqRAiD/gm4QYtThwFvBrxIXxE2J+R47Dvg8DXCfsmnlC2Hwv8XEr5LGEhRsRkpLIDODAiKKyIqHVAbyHEkZHX3wUeFUJUEBY4a6SUPycsxPbFBjuGboAREoaexPmEez+r/IFwJ688ZVsl4aij1cDztiZGdh4HJgsh1gCvEq5AOiLRjlLK5cAYYKmUslZ56wZgmRBiLWGH+qYEn/Fjwt3m3iDsNEdK2UjYd3KbEOI9wo7q70X6Fi8CVgkh3o6MbTEGQwqYKrAGg4IaOdTFp2IwdAuMJmEwGAwGR4wmYTAYDAZHjCZhMBgMBkeMkDAYDAaDI0ZIGAwGg8ERIyQMBoPB4IgREgaDwWBwxAgJg8FgMDjy/wGkRPCkkXhatgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Train Accuracy vs Test Accuracy')\n",
    "plt.xlabel('Alpha Values')\n",
    "plt.ylabel('AUC score')\n",
    "plt.plot(param,test_score)\n",
    "plt.plot(param, train_score)\n",
    "plt.legend(['Test AUC', 'Train AUC'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----------+----------+\n",
      "| Accuracy | Precision | f1 score |\n",
      "+----------+-----------+----------+\n",
      "|  78.731  |   71.304  |  74.208  |\n",
      "+----------+-----------+----------+\n"
     ]
    }
   ],
   "source": [
    "table_b = PrettyTable()\n",
    "#table_b.add_column('Optimal Alpha' , [round(opt_C_1,5)])\n",
    "table_b.add_column('Accuracy', [accMulti_bow])\n",
    "table_b.add_column('Precision', [precMulti_bow])\n",
    "table_b.add_column('f1 score', [f1Multi_bow])\n",
    "print(table_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt =DecisionTreeClassifier(class_weight='balanced', criterion='gini', max_depth=670,\n",
    "                       max_features='auto', max_leaf_nodes=None,\n",
    "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                       min_samples_leaf=4, min_samples_split=2,\n",
    "                       min_weight_fraction_leaf=0.0, presort=False,\n",
    "                       random_state=None, splitter='best')\n",
    "dt.fit(xTrain, yTrain)\n",
    "pred= dt.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "accMulti_bow= round(accuracy_score(yTest, pred) * 100, 3)\n",
    "precMulti_bow = round(precision_score(yTest, pred)  *100 ,3)\n",
    "f1Multi_bow = round(f1_score(yTest, pred) *100,3)\n",
    "confMulti_bow = confusion_matrix(yTest,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----------+----------+\n",
      "| Accuracy | Precision | f1 score |\n",
      "+----------+-----------+----------+\n",
      "|  78.358  |   72.222  |  72.897  |\n",
      "+----------+-----------+----------+\n"
     ]
    }
   ],
   "source": [
    "table_b = PrettyTable()\n",
    "table_b.add_column('Accuracy', [accMulti_bow])\n",
    "table_b.add_column('Precision', [precMulti_bow])\n",
    "table_b.add_column('f1 score', [f1Multi_bow])\n",
    "print(table_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Bag of Words with L1 regularizer\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix for Bag of Words with L1 regularizer')\n",
    "Labels = ['Negative' , 'Positive'] \n",
    "plt.figure(figsize=(5,4))\n",
    "sns.heatmap(confMulti_bow, annot=True, cmap='RdBu', fmt=\".3f\", xticklabels=Labels, yticklabels=Labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(class_weight='balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'n_estimators':[x for x in range(10,1000)],'criterion':['gini', 'entropy'],'min_samples_split':[2,5,10],'min_samples_leaf':[2,5,10] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
      "                       criterion='entropy', max_depth=None, max_features='auto',\n",
      "                       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "                       min_impurity_split=None, min_samples_leaf=2,\n",
      "                       min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
      "                       n_estimators=595, n_jobs=None, oob_score=False,\n",
      "                       random_state=None, verbose=0, warm_start=False)\n",
      "0.8459069020866774\n"
     ]
    }
   ],
   "source": [
    "clf = RandomizedSearchCV(rf, param_grid, n_iter=100, scoring= 'accuracy', cv =k_fold, return_train_score = True)\n",
    "clf.fit(xTrain,yTrain)\n",
    "print(clf.best_estimator_)\n",
    "print(clf.score(xTrain,yTrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
    "                       criterion='entropy', max_depth=None, max_features='auto',\n",
    "                       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "                       min_impurity_split=None, min_samples_leaf=2,\n",
    "                       min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
    "                       n_estimators=595, n_jobs=None, oob_score=False,\n",
    "                       random_state=None, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.fit(xTrain,yTrain)\n",
    "pred = rf.predict(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = round(accuracy_score(yTest,pred)*100,3)\n",
    "precision = round(precision_score(yTest,pred)*100,3)\n",
    "F1_score = round(f1_score(yTest,pred)*100,3)\n",
    "roc_auc = round(roc_auc_score(yTest,pred)*100,3)\n",
    "conf = confusion_matrix(yTest,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+---------------+----------+----------+----------+\n",
      "| Number of trees | Roc Auc score | Accuracy | Precison | F1 Score |\n",
      "+-----------------+---------------+----------+----------+----------+\n",
      "|       595       |     82.411    |  84.328  |  85.714  |  78.788  |\n",
      "+-----------------+---------------+----------+----------+----------+\n"
     ]
    }
   ],
   "source": [
    "table = PrettyTable()\n",
    "table.add_column('Number of trees',['595'])\n",
    "table.add_column('Roc Auc score',[roc_auc])\n",
    "table.add_column('Accuracy',[accuracy])\n",
    "table.add_column('Precison',[precision])\n",
    "table.add_column('F1 Score',[F1_score])\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Labels = ['Dead' , 'Survived']\n",
    "plt.title('Confusion matrix of the classifier')\n",
    "sns.heatmap(conf, annot=True, cmap='RdBu', fmt=\".3f\", xticklabels=Labels, yticklabels=Labels)\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('Original Class')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}