Applied Machine Learning Ensemble Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6b07183",
   "metadata": {},
   "source": [
    "### LinkedIn Learning\n",
    "### Applied Machine Learning - Ensemble Learning\n",
    "\n",
    "## Dataset Used : Kaggle's Titanic Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08c744f",
   "metadata": {},
   "source": [
    "## 1. Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee56e845",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40a14e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9cd8cac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": [
    "tit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a87c77",
   "metadata": {},
   "source": [
    "## 2. Clean Continuous Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6f2860",
   "metadata": {},
   "source": [
    "### A. Fill in missing Values for Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1680df78",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9252de67",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data['Age'].fillna(tit_data['Age'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64c4b5a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>22.000000</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>38.000000</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>26.000000</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>35.000000</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>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex        Age  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.000000   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                             Heikkinen, Miss. Laina  female  26.000000   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                           Allen, Mr. William Henry    male  35.000000   \n",
       "5                                   Moran, Mr. James    male  29.699118   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.000000   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.000000   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.000000   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.000000   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  \n",
       "5      0      0            330877   8.4583   NaN        Q  \n",
       "6      0      0             17463  51.8625   E46        S  \n",
       "7      3      1            349909  21.0750   NaN        S  \n",
       "8      0      2            347742  11.1333   NaN        S  \n",
       "9      1      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03183816",
   "metadata": {},
   "source": [
    "### B. Combine Sibsp and Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6792162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAHpCAYAAACful8UAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqP0lEQVR4nO3dd1jVdf/H8dfhMGWqyFDAvcEFDhwNK0ut1CxNS8tRmVqZdXdrO++69e7u107TzMzK0oYttSIt9wJFUdwLRIagDFHW4fz+8A47ooXK4ct4Pq6L65L3+XwPL66oePkdH5PVarUKAAAAAACUOwejAwAAAAAAUF1RugEAAAAAsBNKNwAAAAAAdkLpBgAAAADATijdAAAAAADYCaUbAAAAAAA7oXQDAAAAAGAnNa50W61WZWdni+3JAQAAAAD2VuNKd05Ojry9vZWTk2N0FAAAAABANVfjSjcAAAAAABWF0g0AAAAAgJ1QugEAAAAAsBNKNwAAAAAAdkLpBgAAAADATijdAAAAAADYCaUbAAAAAAA7oXQDAAAAAGAnlG4AAAAAAOyE0g0AAAAAgJ1QugEAAAAAsBNKNwAAAAAAdkLpBgAAAADATijdAAAAAADYCaUbAAAAAAA7oXQDAAAAAGAnjkYHAAAY785Z65WclSdJCvR21VcPdzc4EQAAQPVA6QYAKDkrT0mZZ42OAQAAUO1weTkAAAAAAHZC6QYAAAAAwE4o3QAAAAAA2AmlGwAAAAAAO6F0AwAAAABgJ5RuAAAAAADshNINAAAAAICdULoBAAAAALATSjcAAAAAAHZC6QYAAAAAwE4o3QAAAAAA2AmlGwAAAAAAO6F0AwAAAABgJ5RuAAAAAADshNINAAAAAICdULoBAAAAALATSjcAAAAAAHZC6QYAAAAAwE4o3QAAAAAA2AmlGwAAAAAAO3E0OgAA49w5a72Ss/IkSYHervrq4e4GJwIAAACqF0o3UIMlZ+UpKfOs0TEAAACAaovLywEAAAAAsBPDS/fMmTPVuHFjubq6Kjw8XGvWrPnL9fn5+XrmmWfUsGFDubi4qGnTppo3b14FpQUAAAAAoOwMvbx80aJFmjRpkmbOnKkePXpo9uzZ6tu3r+Lj4xUSEnLRY4YMGaLU1FR9+OGHatasmdLS0lRUVFTByQEAAAAA+HuGlu7XX39dY8aM0dixYyVJb775pn7++WfNmjVL06dPL7X+p59+0qpVq3To0CHVqVNHktSoUaO//Br5+fnKz88v+Tw7O7v8vgEAAAAAAP6CYZeXFxQUKCYmRn369LGZ9+nTR+vXr7/oMd9//70iIiL06quvqkGDBmrRooWefPJJnT176QdBTZ8+Xd7e3iUfwcHB5fp9AAAAAABwKYad6U5PT5fFYpG/v7/N3N/fXykpKRc95tChQ1q7dq1cXV21ZMkSpaena/z48Tp58uQl7+ueOnWqJk+eXPJ5dnY2xRsAAAAAUCEM3zLMZDLZfG61WkvN/lBcXCyTyaTPPvtM3t7eks5don7nnXfqvffek5ubW6ljXFxc5OLiUv7BAQAAAAD4G4ZdXu7r6yuz2VzqrHZaWlqps99/CAwMVIMGDUoKtyS1bt1aVqtVx44ds2teAAAAAAAul2Gl29nZWeHh4YqKirKZR0VFqXv37hc9pkePHjp+/LhOnz5dMtu3b58cHBwUFBRk17wAAAAAAFwuQ/fpnjx5subOnat58+Zp9+7devzxx5WQkKBx48ZJOnc/9siRI0vWDx8+XHXr1tWoUaMUHx+v1atX6x//+IdGjx590UvLAQAAAAAwkqH3dA8dOlQZGRmaNm2akpOTFRoaqmXLlqlhw4aSpOTkZCUkJJSs9/DwUFRUlB555BFFRESobt26GjJkiF5++WWjvgUAAAAAAC7J8AepjR8/XuPHj7/oa/Pnzy81a9WqValL0gEAAAAAqIwMvbwcAAAAAIDqjNINAAAAAICdULoBAAAAALATSjcAAAAAAHZC6QYAAAAAwE4o3QAAAAAA2AmlGwAAAAAAO6F0AwAAAABgJ5RuAAAAAADshNINAAAAAICdULoBAAAAALATSjcAAAAAAHZC6QYAAAAAwE4o3QAAAAAA2AmlGwAAAAAAO6F0AwAAAABgJ5RuAAAAAADshNINAAAAAICdULoBAAAAALATSjcAAAAAAHZC6QYAAAAAwE4o3QAAAAAA2AmlGwAAAAAAO6F0AwAAAABgJ5RuAJIkq6xGRwAAAACqHUo3UAPl5hfp/VUHlZKdVzI7npmnCZ9t1daEUwYmAwAAAKoXR6MDAKhYaTl5GvnhZu1JySn12tK4ZC3bmaxpA0I1oltDA9IBAAAA1QtnuoEaxFJs1YMLYi5auP9gtUrPfbtTv+1Nq8BkAAAAQPVE6QZqkNX7Tig2MbNMa99Zsd++YQAAAIAagNIN1CBfbEko89qtCZnal3rpM+IAAAAA/h6lG6hBDqSdvqz1e5Kz7ZQEAAAAqBko3UAN4mAyXdb6p77eoccXxeqXXSnKK7TYKRUAAABQffH0cqAGaVPfS/sv42x3XmGxlmxL0pJtSXJ3NuuG1v7qFxaga1v4yc3ZbMekAAAAQPVA6QZqCKvVetlnuv8st8Ci77cf1/fbj8vNyazerfzULyxQ17eqp1rO/KcEAAAAuBh+UwZqgIKiYv3z6x1asi2pTOvNDlJkk7ralpCp3ILSl5WfLbRoaVyylsYly9XJQde18FO/doHq3cpPHi78ZwUAAAD4A78dA9Vc1tlCjfskRhsOZZRpvbOjg94a2kF9wwKVV2jR6n0ntHxnin6NT1VOflGp9XmFxfppV4p+2pUiZ0cHXduinvqFBeiG1v7ycnUq728HAAAAqFIo3UA1lpR5VqM+2qx9qbb3cV/Xop76hgboue92qcBSLElyMEn3d2+s+7o3VMO67pIkVyez+rQNUJ+2Acovsmjt/nQti0tRVHyKsvNKF/CComJFxacqKj5VzmYH9Wruq75hgbqptb+8a1HAAQAAUPNQuoFqatfxLI36aIvScvJt5sO6hOhfA9rK0eygt1ceUFLmWUlSoLebnr+tzSXfz8Xx3IPUbmjtr4KiMK07mK7lccn6JT5VmWcKS60vsBRrxZ40rdiTJiezST2a+apfaKBuauOv2u7O5fvNAgAAAJUUpRuohlbtO6Hxn8aUuh/7qVta6uFrm8p0FQ9Uk85dgn59Sz9d39JPr1iKteFghpbvTNbPu1J1Mreg1PpCi1W/7z2h3/eekHmJSd2b1lW/sED1aeOvuh4uV5UFAAAAqMwo3UA1s2hLgp5eslOWYmvJzMls0mt3tdeADg3K/es5mR10TYt6uqZFPf1rQLE2Hz6ppXHJ+nlXitJPly7glmKr1uxP15r96XpmSZy6NTlXwG9uG6B6nhRwAAAAVC+UbqCasFqteiNqn95eecBm7unqqDkjIhTZtK7dMziaHdS9ma+6N/PVtAGh2nLkpJbFJWv5zhSduOAyd0kqtkrrD2Zo/cEMPffdTnVpVEf92wXqlrYB8vNytXteAAAAwN4o3UA1UFBUrClf79A3F2wJ1sDHTR+N6qwW/p4VnsnsYFK3JnXVrUldvXhbW8UknDpXwONSlJKdV2q91SptOnxSmw6f1Avf71JEw9rqGxqovmEBCvR2q/D8AAAAQHmgdANVXHZeoR7+NEbrDthuCda2vpc+ur9zpThj7OBgUudGddS5UR0917+NtiVmavn/zoD/8SC3P7NapS1HTmnLkVOa9mO8OoX4qF9YoPqGBaqBDwUcAAAAVQelG6jCjmee1aiPtmhvao7N/LqW9fTe8E5yd6l8/4o7OJgU3rC2whvW1jP9W2v7sSwtj0vW0rhkHTtVuoBL0taETG1NyNTLS3erfbCP+oUGqF9YoILr1Krg9AAAAMDlqXy/kQMok/jj2Ro1f7NSs23vlb67c7BeHhgqR7ODQcnKzmQyqUOwjzoE+2hK31bamZStZTuTtSwuWUczzlz0mO2JmdqemKnpy/corIG3+oYFqF9ooBr5uldwegAAAODvUbqBKmj1vhMa/9lWnc4vspn/4+aWGn/d1W8JZgSTyaSwIG+FBXnrqZtbKj45W8vjUrQsLlmH0nMvekxcUpbikrL06k971SbQS/3CAtQ3LFBN63lUcHoAAADg4ijdQBWzODpRT38Tp6ILtgR79c52GtQxyMBk5cdkMqltfW+1re+tJ/q00L7U01oal6zlccnan3b6osfEJ2crPjlbr/2yTy39PdUvLFD9wgLU3ICHyAEAAAB/oHQDVYTVatUbv+7X2yv228w9XR01e0S4ujf1NSiZfZlMJrUM8FTLAE9NvqmF9qfmaFlcipbvTNaelJyLHrM3NUd7U3P0xq/71MzPo6SAt/T3rJJXAQAAAKDqonQDVUBBUbGmfhOnr7ces5nX93bVR6O6qGVAzTmb29zfU4/5e+qxG5vr4InTWh6XrGVxKYpPzr7o+gNpp/X2inN/WdHE1/1/T0EPUJtALwo4AAAA7M7wJy3NnDlTjRs3lqurq8LDw7VmzZpLrv39999lMplKfezZs6cCEwMVKyevUKPnbylVuNsEemnJhB41qnBfqGk9D03s3VzLHuul35+8Tv+8pZXCGnhfcv2h9Fy9+9sB9X97ra577XfNWL5HcceyZLVaL3kMAAAAcDUMPdO9aNEiTZo0STNnzlSPHj00e/Zs9e3bV/Hx8QoJCbnkcXv37pWXl1fJ5/Xq1auIuECFS846tyXYhZdRX9Oinmbe00kelXBLMKM08nXXw9c11cPXNVXiyTNavjNZS+NStD0x86Lrj2ac0furDur9VQcVVNvtf5egB6p9kDdnwAEAAFBuDP2N/fXXX9eYMWM0duxYSdKbb76pn3/+WbNmzdL06dMveZyfn598fHwqKCVgjN3J2Rr10RalZOfZzIdGBOvlQaFyqgJbghkluE4tPXhNUz14TVMdO3VGP+1M0fKdKYo5euqi64+dOqs5qw9pzupDauDjpltCA9QvLEAdg2vLwYECjprnzlnrlZx17r89gd6u+urh7gYnAgCg6jKsdBcUFCgmJkZTpkyxmffp00fr16//y2M7duyovLw8tWnTRs8++6yuv/76S67Nz89Xfv75fYyzsy9+3ydQmazZf0IPf1p6S7Anbmqhib2bcSb2MgTVrqWxvZpobK8mSs46e66Ax6Voy9GTuthV5UmZZ/Xh2sP6cO1hBXi5/q+AByqiIQUcNUdyVp6SMs8aHQMAgGrBsNKdnp4ui8Uif39/m7m/v79SUlIuekxgYKDmzJmj8PBw5efn65NPPtENN9yg33//Xddcc81Fj5k+fbpeeumlcs8P2MuX0YmaesGWYI4OJv1ncDsNDq8eW4IZJdDbTaN6NNaoHo2Vmp2nn3ed2wd88+GTKr5IAU/JztP89Uc0f/0R+Xm66JbQAPUNDVSXxnVkpoADAACgDAy/IfTCM3ZWq/WSZ/Fatmypli1blnweGRmpxMREvfbaa5cs3VOnTtXkyZNLPs/OzlZwcHA5JAfKl9Vq1dsrDuiNX/fZzD1dHPX+iHD1aFY9twQzir+Xq0ZGNtLIyEY6kZOvn3ed24Zsw8GMixbwtJx8LdhwVAs2HJWvh7NubnvuDHjXxnXkyKX+AAAAuATDSrevr6/MZnOps9ppaWmlzn7/lW7duunTTz+95OsuLi5ycXG54pxARSi0FOvpb+L0ZYztE8oDvV310ajOahXgdYkjUR7qebro3m4NdW+3hso4na9f4lO1LC5Z6w9myHKRBp5+ukCfbUrQZ5sSVMfdWTe39Vff0EBFNq3LvfYAAACwYVjpdnZ2Vnh4uKKiojRo0KCSeVRUlAYMGFDm99m2bZsCAwPtERGoEDl5hRr/2Vat2Z9uM28V4Kn5o7oowNvVoGQ1U10PFw3rEqJhXUJ0KrdAUfGpWrYzWesOpKvQUrqAn8wt0OebE/X55kR5uzmpTxt/9WsXqB5NfeXsSAEHAACo6Qy9vHzy5MkaMWKEIiIiFBkZqTlz5ighIUHjxo2TdO7S8KSkJC1YsEDSuaebN2rUSG3btlVBQYE+/fRTff311/r666+N/DaAK5aSladR87dod7LtA/56NffVzHs6ydPVyaBkkKTa7s4a0jlYQzoHK+tMoX7dfe4M+Jr96SqwFJdan3W2UF/GHNOXMcfk6eqom9r4q39YoHo295WLo9mA7wAAAABGM7R0Dx06VBkZGZo2bZqSk5MVGhqqZcuWqWHDhpKk5ORkJSQklKwvKCjQk08+qaSkJLm5ualt27ZaunSp+vXrZ9S3AFyxPSnntgT7Y1ueP9wVHqR/3xHGZcqVjHctJw0OD9Lg8CBl5xVq5e40LYtL1u/7TqigqHQBz8kr0jdbk/TN1iR5ujjqhtZ+6hcWqGta1JOrEwUcAACgpjBZrRfbNKf6ys7Olre3t7KysuTlxX2yMMa6A+ka90mMci7YEuzxG1vo0RsqbkuwHjNWlmwL1MDHTeum9K6Qr1udnM4v0so9aVoel6zf9qYpr7B0Af8zd2ezerf2V7/QAF3X0k9uzpWjgPOzgD/j5wEAgPJj+NPLgZrm65hj+ufXO0ptCTZjcDvdyZZgVY6Hi6Nub19ft7evr9z8Iv2+94SW7UzWyt1pOltoKbU+t8CiH7Yf1w/bj8vNyazerfzUNyxAvVv5qZYz/0kGAACobvgND6ggVqtV7648oP+Lst0SzMPFUbPu7aRezesZlAzlxd3FUf3bBap/u0CdLbBo1b40LYtL0YrdqcotKF3AzxZatDQuWUvjkuXq5KDrWpwr4De09peHC/95BgAAqA74rQ6oAIWWYj27ZKcWRSfazAO8zm0J1jqQWx2qGzdns24JDdQtoYHKK7Ro9b4TWr4zRb/Gp5a6rUCS8gqL9dOuFP20K0XOjg66tkU99ftfAffigXoAAABVFqUbsLPT+UUa/9lWrd53wmbeKsBTH43qrEBvN4OSoaK4OpnVp22A+rQNUH6RResOpGvpjhRFxacoO690AS8oKlZUfKqi4lPlbHZQr+a+6hsWqJta+8u7FgUcAACgKqF0A3aUmp2nUR9tUfwFW4L1bOarmfd24gxmDeTiaFbvVv7q3cpfBUVhWn8wXcvikvVLfKoyzxSWWl9gKdaKPWlasSdNjg4m9Wjmq/5hgbqpjb9quzsb8B0AAADgclC6ATvZl5qj++dt1vELtgQb3ClI0+8Ik7MjW4LVdM6ODrqupZ+ua+mnVyzF2ngoQ8vikvXzrlSdzC0otb6o2KpV+05o1b4TMi8xqXvTuuoXFqg+bfxV18PFgO8AAAAAf4fSDdjB+gPpeujTGOVccOnwYzc016Qbm1fYlmCoOpzMDurVvJ56Na+nfw0o1ubDJ7VsZ7J+2pmq9NP5pdZbiq1asz9da/an65klcerW5FwBv7ltgOp5UsABAAAqC0o3UM6WbDump77aoUKL7ZZg/74jTEMigg1MhqrC0eyg7s181b2Zr166PVRbjpzU8rhkLd+ZorSc0gW82CqtP5ih9Qcz9Nx3O9WlUR31CwvULaEB8vdyNeA7AAAAwB8o3UA5sVqteu+3A3rtF9stwdydzZp1b7iuacGWYLh8ZgeTujWpq25N6uqF29oqJuGUlsUla3lcilKy80qtt1qlTYdPatPhk3rxh12KaFhbfUMD1Tcs4JIP7TuQlqPMs+cvZ0/JztPLP8br3m4N1cjX3W7fGwAAQE1A6QbKQZGlWM99t1Ofb7bdEszfy0Xz7u+stvW9DUqG6sTBwaTOjeqoc6M6eq5/G21LzCw5A56UebbUeqtV2nLklLYcOaVpP8arU4hPyRnwoNq1JEmzfj+oV3/eI+v5CzNkKbZq7trD+mj9Eb1wWxuNjGxUQd8hAABA9UPpBq7S6fwiTVy4Vb/vtd0SrKX/uS3B6vuwJRjKn4ODSeENayu8YW0907+1th/L0vK4ZC3bmazEk6ULuCRtTcjU1oRMvbx0t9oH+yjAy0U/70q95NewFFv1/He75OnqqEEdg+z1rQAAAFRrlG7gKqRl52nU/C3addx2S7Aezepq1r3hbAmGCmEymdQh2Ecdgn00pW8r7TqeraVxyVoWl6yjGWcuesz2xExtL+P7T1+2R7e2qy8nM0/cBwAAuFyUbuAK7U/N0f0fbSl1We8dnRpoxh3tqsSWYIHerhf9M6ouk8mk0AbeCm3gradubqndyTla9r8Cfig994reMy0nX7/Gp6pvWGA5pwUAAKj+KN3AFdhwMEMPfhJdakuwR3s30+M3tagyW4J99XB3oyPAjkwmk9rU91Kb+l56ok8L7Us9XVLA96edvqz3ik3MpHQDAABcgcp/Kg6oZL6LTdLIeZtsCrfZwaT/DA7T5D4tq0zhRs1iMpnUMsBTj9/UQlGTr9XNbf0v6/gCS7GdkgEAAFRvlG6gjP7YEuyxL2Jt9uB2dzZr3v2dNbRziIHpgMsT1uDynqjfsE4tOyUBAACo3ijdQBkUWYr1zLc79d+f99rM/TxdtOihSF3LHtyoYgZ1CpJDGS/KcDBJt7Wvb99AAAAA1RSlG/gbuflFemBBtBZuSrCZt/D30JIJPRR6mWcMgcqggY+bBnZsUKa1xVZpzppDsv55M28AAACUCQ9SA/5CWk6eRs/fop1JtluCRTapq/dHhMvbjS3BUHW9MjBMSafOatPhk3+7dvaqQ7JYrHqmf2ueWwAAAHAZONMNXMKBtBwNem99qcI9qGMDfTy6C4UbVZ6bs1kLxnTRlL6tZL6gSIfUcVOjurb3cc9de1gv/RDPGW8AAIDLQOkGLmLToQzdMXN9qT24J17fTK8PaV8l9uAGysLF0axx1zZVwJ/2affzdNHvT16v5Y9do17NfW3Wz19/RM9/t0vFxRRvAACAsqA5ABf4fvtxjfhws7Iv2BLs34PC9OTNbAmG6s/J7CAHB5PcnM36YGREqQcFfrLxqJ79bifFGwAAoAwo3cD/WK1Wzfr9oB79fJvNnsS1nM2ae1+EhndlSzDUPK5OZs0eEa7erfxs5gs3JejpJXEUbwAAgL9B6QZ0bkuw577bqf/8tMdmXs/TRYsfitT1Lf0ucSRQ/bk6mTXr3k66sbW/zfyLLYl66usdslC8AQAALonSjRrvTEGRHvokRp9utN0SrJmfh5aM786WYIDO3fs9855OurmtbfH+KuaY/vHldoo3AADAJVC6UaOdyMnX3XM2asWeNJt518Z19PW47gqqXesSRwI1j7Ojg94d3kl9QwNs5t9sS9LkxbEq+tNtGQAAADiH0o0a60DaaQ2auU47jmXZzAd0qK8FY7rIuxZbggEXcjI76O1hHXVru0Cb+XexxzVpEcUbAADgQo5GBwCMsPnwST2wIFpZZwtt5uOva6on+7SUgwNPKAcuxcnsoDeHdpDZwaTvYo+XzH/ckaxiq1Vv3d1RTmb+ThcAAECidKMG+mH7cT2xeLvNE8odTNK/Bobqnq4NDUwGVB2OZge9PqSDzCaTvtmWVDJfFpciS/FWvTOsE/vZAwAAiMvLUYNYrVbNWX1Qj1ywJZib07ktwSjcwOUxO5j037va687wIJv5z7tSNf6zGOUXWQxKBgAAUHlQulEjWIqteuH7Xfr3MtstwXw9XLTooW7q3cr/EkcC+CtmB5NeHdxOd3cOtpn/ujtN4z6JUV4hxRsAANRslG5Ue+e2BIvWgg1HbeZN67lryfjuahfkY0wwoJpwcDDp34PCNLxriM38t70n9BDFGwAA1HCUblRrJ3LyNWzORv2623ZLsC6N6+ibh3souA5bggHlwcHBpFcGhmpkpO1tGqv2ndADC6Ip3gAAoMaidKPaOnjitO6YtU7bL9gS7Lb29fUJW4IB5c5kMuml29tqVI9GNvM1+9M1ev4WnS2geAMAgJqH0o1qacuRkxo8a70ST561mY+7tqneGtpBLo5mg5IB1ZvJZNLzt7bR2J6NbebrD2Zo1PzNys0vMigZAACAMSjdqHaW7kjWPXM3KfPM+T24/9gSbErfVuzBDdiZyWTSM/1b66FrmtjMNx46qVEfbdFpijcAAKhBKN2oNqxWqz5YfUgTFm5VQZHtlmAfjIzQiG5sCQZUFJPJpCl9W2n8dU1t5puPnNR98zYrJ6/wEkcCAABUL5RuVAuWYqte+iFeryzbbTP39XDWFw920w2t2RIMqGgmk0n/uLmlHu3dzGYec/SURs7brGyKNwAAqAEo3ajyzhZYNO7TGM1ff8Rm3qSeu5aM76H2wT6G5AJwrnhP7tNSj9/Ywma+LSFTI+ZuUtZZijcAAKjeKN2o0tJP5+vuDzYqKj7VZt65UW1983B3tgQDKonHbmyuJ/vYFu/tx7J079xNyjxTYFAqAAAA+6N0o8o6dOK07pi5XtsTM23m/dsF6pMxXeVTy9mYYAAuamLv5nrqlpY2s7ikLA3/YJNO5VK8AQBA9UTpRpUUc/TclmAJJ8/YzB+6poneubujXJ3YEgyojMZf10xP92tlM4tPztawDzYq43S+QakAAADsh9KNKmd5XLKGfbBJpy7YEmzagLaa2q81W4IBldyD1zTVc7e2sZntScnR8A82KZ3iDQAAqhlKN6qUuWsOafwFW4K5Ojlo9ogIjYxsZFwwAJdlTM/Geun2tjazvak5GjZno9Jy8gxKBQAAUP4o3agSzm0JtksvL90tq/X8vK67s754MFI3tWFLMKCqua97I/1rYKjNbH/aad09Z6NSsyneAACgeqB0o9LLK7Ro/Gcx+mjdEZt5E99zW4J1YEswoMoa0a2h/j0ozGZ26ESu7p6zUSlZFG8AAFD1UbpRqWWcztewDzbq5122W4JFNKytrx/urpC6bAkGVHXDu4bo1cHtZPrT4xgOp+dq6JwNOp551rhgAAAA5YDSjUrrcHqu7pi1XtsSMm3m/cMC9enYrqrtzpZgQHUxpHOw/ntne5vifTTjjIbO2aBjp85c+kAAAIBKjtKNSinm6CkNnrVeRzNsf9l+oFdjvTOMLcGA6ujO8CC9PqS9/rwBQeLJsxo6e6MST1K8AQBA1WR46Z45c6YaN24sV1dXhYeHa82aNWU6bt26dXJ0dFSHDh3sGxAV7qedKRr+wUadzC0omZlM0ou3tdEz/duwJRhQjQ3qGKQ3hnawKd5JmWd195yNOpqRa1wwAACAK2Ro6V60aJEmTZqkZ555Rtu2bVOvXr3Ut29fJSQk/OVxWVlZGjlypG644YYKSoqKMm/tYT38WYzyL9gS7P17w3V/j8YGJgNQUQZ0aKC3h3WU+U/N+4/ifSSd4g0AAKoWQ0v366+/rjFjxmjs2LFq3bq13nzzTQUHB2vWrFl/edxDDz2k4cOHKzIysoKSwt6Ki62a9kO8pv0YX2pLsM8f6Kab2wYYFw5Ahbu1XX29O6yjHP9UvJOz8jR0zgYdOnHawGQAAACXx7DSXVBQoJiYGPXp08dm3qdPH61fv/6Sx3300Uc6ePCgXnjhhTJ9nfz8fGVnZ9t8oHLJK7RowsKtmrfusM28sa+7vhnfXR1DahuUDICR+oYFauY9neRkPl+8U7PzNXTORh1Io3gDAICqwbDSnZ6eLovFIn9/f5u5v7+/UlJSLnrM/v37NWXKFH322WdydHQs09eZPn26vL29Sz6Cg4OvOjvKz8ncAt0zd5OW77T9Z94pxEdfP9xdDeu6G5QMQGXQp22A3r83XM7m8/+7OpGTr7vnbNS+1BwDkwEAAJSN4Q9SM5lsH4pltVpLzSTJYrFo+PDheumll9SiRYsyv//UqVOVlZVV8pGYmHjVmVE+jmbkavCs9Yo5espm3jc0QAsf6KY6bAkGQNINrf01e0S4nB3P/y8r/XS+hs3ZqD0pXL0EAAAqN8NKt6+vr8xmc6mz2mlpaaXOfktSTk6OoqOjNXHiRDk6OsrR0VHTpk3T9u3b5ejoqJUrV17067i4uMjLy8vmA8bblnBKg2au1+ELHoo0pmdjvTe8E1uCAbBxfSs/fTAywqZ4Z+QWaNicjYo/TvEGAACVl2Gl29nZWeHh4YqKirKZR0VFqXv37qXWe3l5KS4uTrGxsSUf48aNU8uWLRUbG6uuXbtWVHRcpZ93pWjYRbYEe/7WNnruVrYEA3Bx17aop3n3dZbLn4r3qTOFGj53o3YmZRmYDAAA4NLKdmO0nUyePFkjRoxQRESEIiMjNWfOHCUkJGjcuHGSzl0anpSUpAULFsjBwUGhoaE2x/v5+cnV1bXUHJXX/HWH9dIFTyh3cXTQW3d30C2hgcYFA1Al9Gzuq4/u76wxH0frbKFFkpR5plDDP9ioT8d2VbsgH2MDAgAAXMDQ0j106FBlZGRo2rRpSk5OVmhoqJYtW6aGDRtKkpKTk/92z25UDcXFVk1fvlsfrLF9Qnkdd2d9MDJC4Q15QjmAsunezFcfjeqs0fO36EzBueKdnVeke+Zu0idjuqpDsI+xAQEAAP7EZLX++Zxj9ZednS1vb29lZWVxf3cFySu0aPLiWC2Ls71/v1HdWpo/qosa+fKEcsBoPWasVFLmWUlSAx83rZvS2+BEf2/LkZO6f95m5f6veEuSp4uj5o/uwl/kXaWq+PMAAEBlZfjTy1G9ncot0L1zN5Uq3B3/tyUYhRvAlercqI4WjOkiD5fzF23l5Bdp5IebFH3kpIHJAAAAzqN0w24SMs5o8Kz1ir5gS7Cb2/pr4dhuquvhYlAyANVFeMM6+mRMF3n+qXjnFlg0ct5mbTqUYWAyAACAcyjdNdCds9arx4yV6jFjpe6ctd4uXyM2MVODZq7ToQu2BBvVo5Fm3hMuN2e2BANQPjqG1NanY7vKy/V88T5TYNH9H23RhoMUbwAAYCxKdw2UnJWnpMyzSso8q+SsvHJ//6j4VN09Z4MyLtgS7Llb2+iF29rKzJZgAMpZ+2AfLXygm7zdnEpmZwstGjV/s9YdSDcwGQAAqOko3ShXCzYc0UOfRCuvsLhk5uLooJnDO2lMz8YGJgNQ3YU28NbCB7qqdq3zxTuvsFij52/R6n0nDEwGAABqMko3ykVxsVXTl+3W89/tUvGfnodfu5aTFj7QVX3D2IMbgP21re+thQ90Ux1355JZflGxxi6I1u970wxMBgAAaipKN65aXqFFj3yxTbNXH7KZh9Sppa8f7q7whnUMSgagJmod6KXPH+gmX4/zxbugqFgPLojRyj2pBiYDAAA1EaUbVyXzTIFGfLhJS3ck28w7BPvom/Hd1aSeh0HJANRkLQM89cWD3VTP8/wuCQWWYj30SYyi4ineAACg4lC6ccUST57RHbPWa8sR2y3Bbmrj/7+zTGwJBsA4zfzOFW+/PxXvQotVD38ao592phiYDAAA1CSUblyR7X9sCXbCdkuw+yIb6v172RIMQOXQtJ6HFj0UqQAv15JZUbFVExZuLXWFDgAAgD1QunHZfo1P1d1zNir9dIHN/Nn+rfXi7WwJBqByaezrrkUPdVN97/PF21Js1aNfbNMP248bmAwAANQElG5clk82HtWDn0TrbKGlZObs6KD3hnfS2F5NZDJRuAFUPg3rumvRQ5Fq4ONWMrMUW/XYF9v0XWySgckAAEB1R+lGmRQXWzV9+W499+1Omy3BfGo56bOxXdW/HVuCAajcguvU0hcPdlNwnfPFu9gqPb4oVl/HHDMwGQAAqM4o3fhb+UUWPbYoVrNX2W4JFlzHTV8/3F2dG7ElGICq4VzxjlTDurVKZsVW6cmvtmtxdKKByQAAQHXlWNaFd9xxR5nf9JtvvrmiMKh8Ms8U6MFPYrT58Embefsgb829r7PNdjwAUBU08HHTFw920/APNulw+rmHQVqt0lNf7ZCl2KphXUIMTggAAKqTMp/p9vb2Lvnw8vLSihUrFB0dXfJ6TEyMVqxYIW9vb7sERcVLPHlGg2etL1W4b2ztr88v2P8WAKqSQO9zxbuJr7vNfOo3cfp041GDUgEAgOqozGe6P/roo5I///Of/9SQIUP0/vvvy2w+tzWUxWLR+PHj5eXlVf4pUeHijmVp1PwtSj+dbzMfGdlQL9zGE8oBVH3+Xq7nznjP3aQDaadL5s9+u1PFVqtGRjYyLhwAAKg2ruie7nnz5unJJ58sKdySZDabNXnyZM2bN6/cwsEYK/ekasjsDaUK99P9WukltgQDUI34ebnq8we6qYW/h838+e92ad7awwalAgAA1ckVle6ioiLt3r271Hz37t0qLi6+6lAwzmebjmrsxxdsCWZ20DvDOurBa5qyJRiAaqeep4s+f6CbWgV42syn/RivuWsOXeIoAACAsinz5eV/NmrUKI0ePVoHDhxQt27dJEkbN27UjBkzNGrUqHINiPJnlbXUn4uLrfrvL3s16/eDNmu93Zz0wcgIdWnME8oBVF91PVy08IFuunfuJsUnZ5fMX166W0XFVo27tqmB6QAAQFV2RaX7tddeU0BAgN544w0lJydLkgIDA/XUU0/piSeeKNeAKD+H03M1e9VBHc/MK5kdz8zT44tilXW2UCv3pNmsD6rtpvmjuqiZn8eFbwUA1U4dd2ctfKCr7v1wk3YmnS/eM5bvkaXYqgnXNzMwHQAAqKquqHQ7ODjoqaee0lNPPaXs7HO/mPAAtcpt46EMjf04Wqfzi0q9tmRbUqlZuyBvfciWYABqGJ9azvpsTDeNmLdJO45llcz/+/NeWYqtevSG5gamAwAAVdEV3dMtnbuv+9dff9Xnn39ecp/v8ePHdfr06b85EhXteOZZPXCJwn0xN7Ty0xdsCQaghvKu5aRPxnRVh2Afm/nrUfv0etQ+Wa3Wix8IAABwEVdUuo8ePaqwsDANGDBAEyZM0IkTJyRJr776qp588slyDYir9/GGI8opY+Fu6e+p2SPCVcv5ii6CAIBqwdvNSZ+M6aLwhrVt5m+v2K//+4XiDQAAyu6KSvdjjz2miIgInTp1Sm5ubiXzQYMGacWKFeUWDlevuNiqr6KPlXl9UuYZO6YBgKrD09VJH4/uos6NbIv3u78d0H9+2kvxBgAAZXJFpXvt2rV69tln5ezsbDNv2LChkpJK3x8M4+TkFSkjt6DM60/nWy5rPQBUZx4ujpo/qkupHRzeX3VQ/162m+INAAD+1hWV7uLiYlksllLzY8eOydPT8yJHwCgOV/BP2OzAXtwA8Ad3F0fNH9VZkU3q2sw/WHNY//qR4g0AAP7aFZXum266SW+++WbJ5yaTSadPn9YLL7ygfv36lVc2lAMPF0c1qlurzOvre7uqrrvz3y8EgBqklrOj5t3fWT2b+drM5607rBe/30XxBgAAl3RFpfuNN97QqlWr1KZNG+Xl5Wn48OFq1KiRkpKS9J///Ke8M+IqmEwmDe8aUub1w7qElDyNHgBwnpuzWXPvi9A1LerZzD/ecFTPfbdTxcUUbwAAUNoVle769esrNjZWTz75pB566CF17NhRM2bM0LZt2+Tn51feGXGVhnUJKdPZ7gY+bhoR2bACEgFA1eTqZNacEeG6vqVt8f50Y4Ke+TaO4g0AAEq5on2hzpw5o1q1amn06NEaPXp0eWdCOfN0Pbfn7H3zNutQeu5F14TUqaWPR3eRTy0uLQeAv+LqZNb7I8I1/tOtWrEnrWT++eZEWYqtmnFHOznwbAwAAPA/V3Sm28/PT/fee69+/vlnFRcXl3cm2EFwnVpa+mgv/XtQmJzM538ZdDKb9K8BbbX8sV5q7OtuYEIAqDpcHM2adW+4bmrjbzNfHH1M//hqhyyc8UY1cees9eoxY6V6zFipO2etNzoOAFRJV1S6FyxYoPz8fA0aNEj169fXY489pi1btpR3NpQzN2ezhncNkZ+na8nMz9NVIyIbyd3lii56AIAay9nRQTPv6aS+oQE286+3HtMTi2NVZOEvpVH1JWflKSnzrJIyzyo5K8/oOABQJV1R6b7jjjv05ZdfKjU1VdOnT9fu3bvVvXt3tWjRQtOmTSvvjAAAVEpOZge9Payj+rcLtJl/G3tcjy/eTvEGAABXVrr/4OnpqVGjRumXX37R9u3b5e7urpdeeqm8sgEAUOk5mR301tAOur19fZv5D9uP67EvYlVI8QYAoEa7qtKdl5enxYsXa+DAgerUqZMyMjL05JNPllc2AACqBEezg14f0l6DOjawmS+NS9YjC7epoIjiDQBATXVFpfuXX37RfffdJ39/f40bN05+fn76+eeflZCQwD7dAIAaydHsoNfuaq/BnYJs5j/tStGEhVsp3gAA1FBXVLoHDhyoM2fO6OOPP1ZqaqrmzJmja6+9tryzAQBQpZgdTPrvne00NCLYZh4Vn6qHP41RfpHFoGQAAMAoV/TI6pSUFHl5eZV3FgAAqjwHB5Om3xEmBweTPt+cUDJfsSdND30So/fvDZerk9nAhAAAoCKV+Ux3dnZ2qc8v9QEAQE3m4GDSKwNDdW+3EJv573tP6IEF0cor5Iw3AAA1RZnPdNeuXVvJycny8/OTj4+PTCZTqTVWq1Umk0kWC79MAABqNgcHk/41IFSODg6av/5IyXzN/nSN+XiL5o7sLDdnzngDAFDdlbl0r1y5UnXq1Cn588VKNwAAOM9kMumF29rIwWTSvHWHS+brDmRo9Pwt+vD+CNVyvqI7vQAAQBVR5v/T//lBadddd509sgAAUO2YTCY9d2trOZpNmrP6UMl8w6EM3f/RFn10f2e5u1C8AQCorq7o6eVNmjTRc889p71795Z3HgAAqh2TyaSpfVvp4eua2sw3Hz6p++Zt1un8IoOSAQAAe7ui0j1x4kT99NNPat26tcLDw/Xmm28qOTm5vLMBAFBtmEwmPXVzSz3Su5nNPProKY38cJOy8woNSgYAAOzpikr35MmTtWXLFu3Zs0e33nqrZs2apZCQEPXp00cLFiwo74wAAFQLJpNJk29qocduaG4z35qQqREfblbWWYo3AADVzRWV7j+0aNFCL730kvbu3as1a9boxIkTGjVqVHllAwCg2jGZTHr8phaafFMLm/n2xEyN+HCTss5QvAEAqE6uqnRL0ubNmzVp0iQNGjRIe/fu1Z133lkeuQAAqNYevaG5/nFzS5vZjmNZGj53o07lFhiUCgAAlLcrKt379u3TCy+8oObNm6tHjx6Kj4/XjBkzlJqaqkWLFpV3RgAAqqUJ1zfT1L6tbGa7jmdr+NxNOknxBgCgWriiPUpatWqliIgITZgwQXfffbcCAgLKOxfsKNDb9aJ/BgBUvIeubSqzg0kvL91dMtudnK3hH2zUp2O7ytfDxcB0AADgal32mW6LxaL3339fP/30kyZNmnTVhXvmzJlq3LixXF1dFR4erjVr1lxy7dq1a9WjRw/VrVtXbm5uatWqld54442r+vo10VcPd9e6Kb21bkpvffVwd6PjAECNN7ZXE71wWxub2Z6UHA2bs1EncvINSgUAAMrDZZdus9msRx99VFlZWVf9xRctWqRJkybpmWee0bZt29SrVy/17dtXCQkJF13v7u6uiRMnavXq1dq9e7eeffZZPfvss5ozZ85VZwEAwEijejTWtAFtbWb7007r7jkblJadZ1AqAABwta7onu6wsDAdOnToqr/466+/rjFjxmjs2LFq3bq13nzzTQUHB2vWrFkXXd+xY0cNGzZMbdu2VaNGjXTvvffq5ptv/suz4/n5+crOzrb5AACgMhoZ2UivDAq1mR08kau752xUShbFGwCAquiKSvcrr7yiJ598Uj/++KOSk5OvqNQWFBQoJiZGffr0sZn36dNH69evL9N7bNu2TevXr9e11157yTXTp0+Xt7d3yUdwcHCZ3hsAACPc07Wh/jM4TCbT+dmh9FzdPWeDkrPOGhcMAABckSsq3bfccou2b9+u22+/XUFBQapdu7Zq164tHx8f1a5du0zvkZ6eLovFIn9/f5u5v7+/UlJS/vLYoKAgubi4lDzMbezYsZdcO3XqVGVlZZV8JCYmlikfAABGGdo5RK8ObmdTvI9knNHQ2RuVlEnxBgCgKrmip5f/9ttv5RbA9OffKCRZrdZSswutWbNGp0+f1saNGzVlyhQ1a9ZMw4YNu+haFxcXubjw5FcAQNVyV0SwzA4mPfnldhVbz80STp7R0Nkb9PkD3RRcp5axAQEAQJlcUen+q8u5y8rX11dms7nUWe20tLRSZ78v1LhxY0nn7i1PTU3Viy++eMnSDQBAVXVHpyCZHUx6fFFsSfE+duqs7p6zUZ8/0E0hdSneAABUdldUulevXv2Xr19zzTV/+x7Ozs4KDw9XVFSUBg0aVDKPiorSgAEDypzFarUqP5/tVAAA1dOADg3kYDJp0qJYWf7XvJMyz+ruORu08IFuauTrbnBCAADwV66odF933XWlZn++JNxisZTpfSZPnqwRI0YoIiJCkZGRmjNnjhISEjRu3DhJ5+7HTkpK0oIFCyRJ7733nkJCQtSqVStJ5/btfu211/TII49cybcBAECVcFv7+nJ0MOmRz7ep6H/F+3hWnu6es1ELH+iqJvU8DE4IAAAu5YpK96lTp2w+Lyws1LZt2/Tcc8/plVdeKfP7DB06VBkZGZo2bZqSk5MVGhqqZcuWqWHDhpKk5ORkmz27i4uLNXXqVB0+fFiOjo5q2rSpZsyYoYceeuhKvg0AAKqMvmGBetdk0sSFW0uKd0r2H8W7m5r5UbwBAKiMTFar1Vpeb7Z69Wo9/vjjiomJKa+3LHfZ2dny9vZWVlaWvLy8jI4DAJVCjxkrS56K3cDHTeum9DY4ES4lKj5V4z+LUaHl/P++fT1c9PkDXdXc37NcvgY/D/gDPwsAcPWuaMuwS6lXr5727t1bnm8JAAD+5KY2/po9IlzO5vP/C08/na9hH2zU3pQcA5MBAICLuaLLy3fs2GHzudVqVXJysmbMmKH27duXSzAAAHBxvVv5a87IcD34SYwKioolSemnCzTsg436bGxXtQ7kSi4AACqLKyrdHTp0kMlk0oVXpnfr1k3z5s0rl2AAgIoT6O160T+j8rqupZ8+vC9CYz+OVv7/ivfJ3HPF+9MxXRXawNvghAAAQLrC0n348GGbzx0cHFSvXj25uvKLGgBURV893N3oCLgCvZrX07z7O2vMx1uUV3iueGeeKdQ9czfp0zFdFRZE8QYAwGiXdU/3pk2btHz5cjVs2LDkY9WqVbrmmmsUEhKiBx98kD2zAQCoQD2a+eqj+7vIzclcMss6W6h75m7U9sRM44IBAABJl1m6X3zxRZv7uePi4jRmzBjdeOONmjJlin744QdNnz693EMCAIBLi2xaVx+P7qJazueLd3Zeke6du0lbE079xZEAAMDeLqt0x8bG6oYbbij5/IsvvlDXrl31wQcfaPLkyXr77be1ePHicg8JAAD+WpfGdbRgdBd5uJy/cywnv0gjP9ysmKMnDUwGAEDNdlml+9SpU/L39y/5fNWqVbrllltKPu/cubMSExPLLx0AACiziEZ1tGBMF3n+qXif/l/x3nyY4g0AgBEuq3T7+/uXPEStoKBAW7duVWRkZMnrOTk5cnJyKt+EAACgzDqF1NYnY7vK0/V88c4tsOj+jzZr46EMA5MBAFAzXVbpvuWWWzRlyhStWbNGU6dOVa1atdSrV6+S13fs2KGmTZuWe0gAAFB2HYJ9tHBsN3m7nf+L8DP/K97rD6QbmAwAgJrnskr3yy+/LLPZrGuvvVYffPCBPvjgAzk7O5e8Pm/ePPXp06fcQwIAgMsTFuStz8Z2lU+t88U7r7BYo+Zv0Zr9JwxMBgBAzXJZ+3TXq1dPa9asUVZWljw8PGQ2m21e//LLL+Xh4VGuAQEAwJUJbeCthWO76d4PN+lkboEkKb+oWGM+jtacEeG6rqWfwQkBAKj+LutM9x+8vb1LFW5JqlOnjs2ZbwAAYKw29b30+QPdVNf9/P+fC4qK9eCCGP22J83AZAAA1AxXVLoBAEDV0TLAU1882E2+Hi4lswJLsR78JFq/xqcamAwAgOqP0g0AQA3Q3P9c8fbzPF+8Cy1WPfxZjH7elWJgMgAAqjdKNwAANUQzPw998WA3+XvZFu8Jn23V8rhkA5MBAFB9UboBAKhBmtTz0KIHIxXo7VoyKyq2auLn2/TjjuPam5Kj7LOFJa+dLbSooKjYiKgAAFQLlG4AAGqYRr7uWvRgpBr4uJXMLMVWTVy4TTe/uVo5+UUl85O5Berxn5X6aSeXoAMAcCUo3QAA1EAhdWvpiwe7Kai229+uPZGTr4c/i9EP249XQDIAAKoXSjcAADVUcJ1zxdvdpfQ2oBeyWqUpX+9Q1p8uPQcAAH+P0g0AQA3m4eKowjLes51bYNHXMcfsnAgAgOqF0g0AQA227kCGCizWMq9fuSfNjmkAAKh+KN0AANRg2XmXd7l44qkzPM0cAIDLQOkGAKAGq13L6bLWH804o27TV+hfP8Zrb0qOnVIBAFB9OBodAAAAGKdHM1+5O5uVW2Ap8zEncwv04drD+nDtYbUP8tZdEcG6rX19ebtdXoEHAKAm4Ew3AAA1mKerk+4MD7ri47cfy9Kz3+5Ul1d+1aQvtmn9gXQVF5f9HnEAAKo7SjcAADXckze3VJtAr79cYzZJj/Ruphtb+8vsYCr1en5Rsb6NPa7hczfp2td+09sr9isp86y9IgMAUGVweTkAADWcp6uTPn+wm579dqeW7jiuC09UN6nnrpcHhqp7U19JUlpOnr7ZmqTF0Yk6dCK31Pslnjyr16P26Y1f96lnM18NiQjWTW385er09/uBAwBQ3ZisVmuNugYsOztb3t7eysrKkpfXX/+tPgAANc3xzLPq+9YaZZ0991RzXw9nbXnmRplMpc9uW61WbU04pcVbjunHHcf/8r5wbzcnDexQX3dFBCu0gbfd8qN89ZixsuSKhQY+blo3pbfBiQCg6uFMNwAAKFHfx00eLo4lpdvF0XzRwi1JJpNJ4Q3rKLxhHT1/Wxsti0vWl9HHtPnIyVJrs84W6uMNR/XxhqNqE+ilIRFBGtChgWq7O9v1+wEAwGiUbgAAcNXcXRx1V0Sw7ooI1qETp/VVzDF9FXNMaTn5pdbGJ2frxR/i9e9le9Snrb+GRASrRzPfi94rDgBAVUfpBgAA5apJPQ89dUsrTb6phVbvP6HFW47p192pKrrgZvECS7F+3JGsH3ckq763q+4MD9Kd4cEKqVvLoOQAAJQ/SjcAALALR7ODerfyV+9W/so4na8l2849fG1f6ulSa49n5entlQf09soDimxSV0M6B+mWtoFyc+bhawCAqo3SDQAA7K6uh4vG9mqiMT0ba8exLC2OTtT3sceVk19Uau2GQxnacChDz7vs0m0d6mtoRLDaBXlf8t5yAAAqM0o3AACoMCaTSe2DfdQ+2EfP9m+jn3Yla/GWY9pwKKPU2pz8Ii3clKCFmxLU0t9Td0UEaVDHBqrr4WJAcgAArgylGwAAGMLN2axBHYM0qGOQEjLO6KuYRH0Vc0zHs/JKrd2bmqOXl+7WjOV7dGNrfw3pHKRrmteTo9nBgOQAAJQdpRsAABgupG4tTe7TUo/d2ELrDqRrcXSiftmVqgJLsc26omKrftqVop92pcjP00WDw4N0V3iQmtTzMCg5AAB/jdINAAAqDbODSde0qKdrWtRT5pkCfRd7XIu2JCo+ObvU2rScfM36/aBm/X5QnRvV1l0RweofFih3F369AQBUHvxfCQAAVEo+tZx1X/dGuq97I+1MytKX0Yn6Nva4ss4Wllq75cgpbTlySi9+v0u3tgvU0M7B6hRSm4evAQAMR+kGAACVXmgDb4U28NbUfq0VFZ+qxdGJWnsgXVbbrb91psCixdHHtDj6mJrUc9eQiGDd0bGB/LxcjQkOAKjxKN0AAKDKcHUy67b29XVb+/pKyjyrr2OOaXF0oo6dOltq7aETuZqxfI/++/NeXd+ynu6KCFbvVn5y4uFrAIAKROkGAABVUgMfNz16Q3NNvL6ZNh7O0OItiVq+M0X5RbYPX7MUW/Xr7jT9ujtNvh7OGtSxgYZEBKu5v6dByQEANQmlGwAAVGkODiZ1b+qr7k199dLZQv2w/bi+jE7U9mNZpdamny7QB2sO64M1h9UxxEdDIoJ1a7tAebo6GZAcAFATULoBAEC14e3mpHu7NdS93RpqT0q2vow+piXbknQyt6DU2m0JmdqWkKmXftilfmGBGhIRrK6N6/DwNQBAuaJ0AwCAaqlVgJeeu7WN/nlLK63ck6rF0cf0+940FV/w8LW8wmJ9szVJ32xNUsO6tXRXeJAGhwcp0NvNmOAAgGqF0g0AAKo1Z0cH3RIaqFtCA5WSlaevtx7Tl9GJOpJxptTaoxln9Nov+/R61D71al5PQzsH64bWfnJxNBuQHABQHVC6AQBAjRHg7aoJ1zfT+OuaasuRU1ocnailO5J1ttBis67YKq3ad0Kr9p1Q7VpOGvi/h6+1DvQyKDkAoKqidAMAgBrHZDKpS+M66tK4jl68va2W7jiuxdHHFHP0VKm1p84U6qN1R/TRuiMKa+CtIRFBur19A3nX4uFrAIC/R+kGAAA1moeLo4Z2DtHQziE6kHZaX8Yk6uuYJKWfzi+1Ni4pS3FJWfrX0t26pW2AhkQEq3vTunJw4OFrAICLczA6wMyZM9W4cWO5uroqPDxca9asueTab775RjfddJPq1asnLy8vRUZG6ueff67AtAAAoDpr5uehqX1ba8PU3po7MkJ92vjL8SKFuqCoWN9vP657P9ykXq/+pjei9inxZOl7xAEAMLR0L1q0SJMmTdIzzzyjbdu2qVevXurbt68SEhIuun716tW66aabtGzZMsXExOj666/Xbbfdpm3btlVwcgAAUJ05mR10Yxt/zRkZoQ1Tb9DT/VqpmZ/HRdcmZZ7VWyv2q9erv+meuRv1XWyS8i64RxwAUHOZrFar9e+X2UfXrl3VqVMnzZo1q2TWunVrDRw4UNOnTy/Te7Rt21ZDhw7V888/X6b12dnZ8vb2VlZWlry8eBgKAAAX6jFjpZIyz0qSGvi4ad2U3gYnqhysVqu2JWbqy+hE/bA9Wafziy651svVUQM6nHv4WmgDryq79zc/CwBw9Qy7p7ugoEAxMTGaMmWKzbxPnz5av359md6juLhYOTk5qlOnziXX5OfnKz///D1Z2dnZVxYYAADUaCaTSZ1CaqtTSG09d2sbLY9L0eLoRG06fLLU2uy8In2y8ag+2XhUrQI8NSQiWAM7NlAdd2cDkgMAjGTY5eXp6emyWCzy9/e3mfv7+yslJaVM7/F///d/ys3N1ZAhQy65Zvr06fL29i75CA4OvqrcAAAAtZwdNTg8SIseitTvT16nCdc3lb+Xy0XX7knJ0bQf49X1379q/Gcx+m1vmizFhl1oCACoYIY/SO3Cy62sVmuZLsH6/PPP9eKLL2rRokXy8/O75LqpU6cqKyur5CMxMfGqMwMAAPyhka+7/nFzK62fcoM+GtVZ/cIC5GQu/btMocWqZXEpGvXRFvWYsVKv/bxXR9JzDUgMAKhIhl1e7uvrK7PZXOqsdlpaWqmz3xdatGiRxowZoy+//FI33njjX651cXGRi8vF/+YZAACgvJgdTLq+pZ+ub+mnk7kF+nZbkhZHJ2pPSk6ptSnZeXr3twN697cD6tq4joZEBKtvWIBqObObKwBUN4ad6XZ2dlZ4eLiioqJs5lFRUerevfslj/v88891//33a+HCherfv7+9YwIAAFy2Ou7OGt2zsZY/1ks/TOypEd0aytP14oV60+GTeuLL7eryygpN/WaHtiackoHPuQUAlDND/zp18uTJGjFihCIiIhQZGak5c+YoISFB48aNk3Tu0vCkpCQtWLBA0rnCPXLkSL311lvq1q1byVlyNzc3eXt7G/Z9AAAAXIzJZFJYkLfCgrz1TP/W+nnXuYevrTuQUWrt6fwifb45UZ9vTlQzPw8NiQjSoI5BqufJFXsAUJUZWrqHDh2qjIwMTZs2TcnJyQoNDdWyZcvUsGFDSVJycrLNnt2zZ89WUVGRJkyYoAkTJpTM77vvPs2fP7+i4wMAAJSZq5NZAzo00IAODZR48oy+ijmmr2KOlWzJ9WcH0k7r38v26NWf9qp3Kz8NiQjWdS3rydFs+ON4AACXydB9uo3APt0AAPw19mauOMXFVq0/mKHF0Yn6aVeKCoqKL7m2nqeL7ujUQHeFB6uZn0eF5ONnAQCuHk/rAAAAMIiDg0k9m/uqZ3NfZZ0p1Pfbk7QoOlE7k7JLrT2Rk6/Zqw5p9qpDCm9YW0MigtS/XX15uPDrHABUZvxXGgAAoBLwruWkEZGNNCKykXYdz9KX0cf0bWySMs8Ullobc/SUYo6e0ovfx6t/u0ANiQhW50a1y7TtKgCgYlG6AQAAKpm29b3V9nZvTe3XSr/Gp2lxdKJW7z+hC28KPFtoKbk3vLGvu+4MD9Kd4UHy93I1JjgAoBRKNwAAQCXl4mhW/3aB6t8uUMczz+qbrce0OPqYEk6eKbX2cHqu/vvzXv3fL3t1XUs/DYkIUu9W/nJ25OFrAGAkSjcAAEAVUN/HTRN7N9f465pp0+GT+jI6Uct2Jiuv0Pbha8VWaeWeNK3ck6Y67s4a1LGBhkQEq2WAp0HJAaBmo3QDAABUIQ4OJkU2ravIpnX14oC2+nF7shZHJyo2MbPU2pO5Bfpw7WF9uPaw2gd5666IYN3Wvr683ZwqPjgA1FCUbgAAgCrKy9VJw7uGaHjXEO1LzdGX0Yn6ZmuSMnILSq3dfixL249l6V8/xqtfWKDuighSt8Z15eBw6YevFVrOn0W3FF96OzMAwKWxTzcAALDB3sxVW0FRsX7bm6bFWxL12940Ff/Fb3rBddx0V3iwBocHqYGPW8n81/hUvbNyv7Yfy7JZ37OZrx6/qYXCG9a2V3wAqHYo3QAAwAalu/pIzc7TN1uT9GV0og6l515yncl0rlAPiQhWcuZZ/Xv5nkuudTKb9O7wTrq5bYA9IgNAtUPpBgAANijd1Y/ValXM0VNaHJ2oH3ck60yB5arez8XRQb9OvlbBdWqVU0IAqL7YQwIAAKCaM5lMimhUR6/e2V5bnrlRr97ZTp0bXfkl4vlFxfp049FyTAgA1RelGwAAoAZxd3HUkIhgfTmuu1Y8ca3GXdtU9TxdLvt9vt6aZId0AFD9ULoBAABqqKb1PDSlbyttmNJbLw9se1nHpp/Ot3m6OQDg4ijdAAAANZyj2UHXtvC7rGPMDiY5/sV2YwCAcyjdAAAAUAMfN9X3di3z+sa+tWQyUboB4O9QugEAACAHB5OGdw0p8/oDabl6/rudyi+6uiehA0B1R+kGAACAJOn+Ho3V3M+jzOsXbDiqIbM36tipM3ZMBQBVG6UbAAAAkiQPF0d9Nrar2gd5/+WaP9uemKlb31mr3/am2TseAFRJlG4AAACU8PNy1Tfje+ij+zvL1en8r4q1nM36alykNj99g+4MD7I5JvNMoUZ9tEX/98teWYqtFR0ZACo1SjcAAABsmB1Mur6Vn+q6n9+/u3YtZ0U0qqNaLo567a72enVwO7k42v4q+c7KAxo5b5PST+dXdGQAqLQo3QAAALhsQzoH65vx3dWwbi2b+boDGer/9hpFHzlpUDIAqFwo3QAAALgibet76/uJPdWnjb/NPDU7X0PnbNTcNYdktXK5OYCajdINAACAK+bt5qTZI8L1TL/WMjuc37fbUmzVy0t3a9ynMcrOKzQwIQAYi9INAACAq2IymfTANU30+QPd5OfpYvPaz7tSdfs7axV/PNugdABgLEo3AAAAykWXxnW09NFeimxS12Z+JOOMBs1cp8XRiQYlAwDjULoBAABQbup5uujTsV018fpmNvP8omI99dUOPfXVduUVWgxKBwAVj9INAACAcmV2MOnJm1tq3v0R8nZzsnltcfQxDZq5XkfScw1KBwAVi9INAAAAu+jdyl8/PtJT7YK8bea7k7N12ztr9dPOFIOSAUDFoXQDAADAboLr1NKX4yI1oltDm3lOfpHGfRqjl3+MV6Gl2KB0AGB/lG4AAADYlYujWf8aGKq37u4gNyezzWtz1x7WsDkblZKVZ1A6ALAvSjcAAAAqxIAODfT9xB5qWs/dZh599JT6v71G6w6kG5QMAOyH0g0AAIAK09zfU99P7Knb2te3mWfkFujeDzfpnRX7VVxsNSgdAJQ/SjcAAAAqlLuLo96+u4P+NaCtnMymkrnVKv1f1D6N/niLTuUWGJgQAMoPpRsAAAAVzmQyaURkI305rrsa+LjZvPb73hO69Z21ik3MNCYcAJQjSjcAAAAM0yHYRz8+0lPXtaxnM0/KPKu73l+vBRuOyGrlcnMAVRelGwAAAIaq7e6sefd11pN9Wsjh/NXmKrRY9fx3u/ToF7HKzS8yLiAAXAVKNwAAAAzn4GDSxN7N9emYrvL1cLZ57Yftx3X7u2u1PzXHoHQAcOUo3QAAAKg0ujfz1Y+P9FLnRrVt5gdP5Or2d9fpu9gkg5IBwJWhdAMAAKBSCfB21cIHuunBa5rYzM8WWvTYF7F69ts45RdZDEoHAJeH0g0AAIBKx8nsoKf7tdb794bL08XR5rVPNyborvc3KPHkGYPSAUDZUboBAABQad0SGqAfHump1oFeNvMdx7J06ztrtXJPqkHJAKBsKN0AAACo1Br5umvJ+O4aGhFsM886W6jR86P135/3qMhSbFA6APhrlG4AAABUeq5OZv3nznZ69c52cnG0/RX2vd8OasSHm3UiJ9+gdABwaZRuAAAAVBlDIoK1ZHwPNapby2a+4VCG+r+9RpsPnzQoGQBcHKUbAAAAVUqb+l76/pGeuqVtgM08LSdfwz7YqDmrD8pqtRqUDgBsUboBAABQ5Xi5OmnWvZ30bP/WcnQwlcwtxVb9e9kePfRJjLLOFhqYEADOoXQDAACgSjKZTBrbq4m+eLCb/L1cbF77JT5Vt72zVjuTsgxKBwDnULoBAABQpUU0qqOlj/ZSj2Z1beYJJ8/ojlnrtWhLApebAzAMpRsAAABVnq+HixaM7qpHezezmRcUFeufX8fpH1/t0NkCi0HpANRkhpfumTNnqnHjxnJ1dVV4eLjWrFlzybXJyckaPny4WrZsKQcHB02aNKniggIAAKBSMzuYNLlPS300qrN8ajnZvPZVzDENmrlOh06cNigdgJrK0NK9aNEiTZo0Sc8884y2bdumXr16qW/fvkpISLjo+vz8fNWrV0/PPPOM2rdvX8FpAQAAUBVc39JPSx/tpfbBPjbzPSk5uv3ddVoWl2xMMAA1kqGl+/XXX9eYMWM0duxYtW7dWm+++aaCg4M1a9asi65v1KiR3nrrLY0cOVLe3t4VnBYAAABVRQMfNy1+qJvui2xoMz+dX6Txn23Vv36MV6Gl2KB0AGoSw0p3QUGBYmJi1KdPH5t5nz59tH79+nL7Ovn5+crOzrb5AAAAQPXn4mjWSwNC9fawjqrlbLZ57cO1h3X3nI1KzjprUDoANYVhpTs9PV0Wi0X+/v42c39/f6WkpJTb15k+fbq8vb1LPoKDg8vtvQEAAFD53d6+vr6f2EPN/Txs5jFHT6n/22u1Zv8Jg5IBqAkMf5CayWSy+dxqtZaaXY2pU6cqKyur5CMxMbHc3hsAAABVQzM/T303sYcGdqhvMz+ZW6CR8zbrrV/3q7iYbcUAlD/DSrevr6/MZnOps9ppaWmlzn5fDRcXF3l5edl8AAAAoOap5eyoN4Z20MsDQ+VsPv9rsNUqvfHrPt0/f4tO5hYYmBBAdWRY6XZ2dlZ4eLiioqJs5lFRUerevbtBqQAAAFCdmUwm3dutob56OFINfNxsXlu974RufXuNtiWcMigdgOrI0MvLJ0+erLlz52revHnavXu3Hn/8cSUkJGjcuHGSzl0aPnLkSJtjYmNjFRsbq9OnT+vEiROKjY1VfHy8EfEBAABQRbUL8tHSR3uqdys/m/nxrDwNmb1B89cdltXK5eYArp6jkV986NChysjI0LRp05ScnKzQ0FAtW7ZMDRue29ohOTm51J7dHTt2LPlzTEyMFi5cqIYNG+rIkSMVGR0AAABVnE8tZ80dGaFZqw7q/37Zqz9u6S60WPXiD/GKPnpKMwa3k4eLob8yA6jiDP8vyPjx4zV+/PiLvjZ//vxSM/7GEQAAAOXFwcGkCdc3U8cQHz36+Talnz5/T/ePO5IVn5yt9+8NVwt/TwNTAqjKDH96OQAAAGC07k19tfTRXurSqI7N/NCJXA14d52WbDtmUDIAVR2lGwAAAJDk7+WqhQ901UPXNLGZny206PFF2/X0kjjlFVoMSgegqqJ0AwAAAP/jaHbQ1H6tNXtEuDxdbe/EXLgpQXe9v0GJJ88YlA5AVUTpBgAAAC5wc9sA/fhIT7UJ9LKZxyVlqf/ba/RrfKpByQBUNZRuAAAA4CIa1nXXN+O7a1iXYJt5dl6Rxi6I1n9+2qMiS7FB6QBUFZRuAAAA4BJcncyafkc7vXZXe7k62f7qPOv3g7r3w01Ky8kzKB2AqoDSDQAAAPyNO8OD9O2EHmrs624z33jopPq/vVabDmUYlAxAZUfpBgAAAMqgVYCXvp/YQ/3CAmzmJ3LyNXzuJr2/6qCsVqtB6QBUVpRuAAAAoIw8XZ303vBOev7WNnJ0MJXMLcVWzVi+Rw8siFHW2UIDEwKobCjdAAAAwGUwmUwa3bOxFj0UqUBvV5vXft2dqlvfWaOdSVkGpQNQ2VC6AQAAgCsQ3rC2fnykp3o197WZJ548qztmrdfCTQlcbg6A0g0AAABcqboeLpo/qoseu6G5TOevNldBUbGeXhKnJxZv15mCIuMCAjAcpRsAAAC4CmYHkx6/qYXmj+qi2rWcbF77ZluSBr23XgdPnDYoHQCjUboBAACAcnBti3pa+mgvdQzxsZnvTc3R7e+s1Y87jhsTDIChKN0AAABAOanv46ZFD0ZqVI9GNvPcAosmLtymF7/fpYKiYmPCATAEpRsAAAAoR86ODnrhtrZ6d3hHuTubbV6bv/6Ihs7ZoOOZZw1KB6CiUboBAAAAO7i1XX19/0hPtfD3sJlvS8hU/7fXaNW+EwYlA1CRKN0AAMBGoLerGvi4qYGPW6k9iAFcnqb1PPTthB66o2MDm/mpM4W6/6PNeiNqnyzFbCsGVGeORgcAAACVy1cPdzc6AlCt1HJ21P8Naa+IRnXO3dNtOXdPt9UqvbViv7YmnNKbQzuoroeLwUkB2ANnugEAAAA7M5lMGt41RF8/3F3BddxsXluzP123vrNWMUdPGZQOgD1RugEAAIAKEhbkrR8n9tKNrf1s5slZeRo6e4PmrT0sq5XLzYHqhNINAAAAVCDvWk6aMyJCU/q2koPp/Lyo2KppP8ZrwsKtyskrNC4ggHJF6QYAAAAqmIODSeOubaqFD3ST7wX3ci+LS9GAd9dpT0q2QekAlCdKNwAAAGCQbk3qatmjPdW1cR2b+aH0XA18b52+jjlmUDIA5YXSDQAAABjIz8tVn43tqoeva2ozzyss1hNfbtfUb3Yor9BiUDoAV4vSDQAAABjM0eygf97SSnNHRsjL1XZX3883J2rwrPVKyDhjUDoAV4PSDQAAAFQSN7bx14+P9FJoAy+b+a7j2er/zhpFxacalAzAlaJ0AwAAAJVISN1a+mpcdw3vGmIzz8kr0gMLojV9+W4VWYoNSgfgclG6AQAAgErG1cmsfw8K0+tD2svVyfZX9tmrDmn43E1Ky84zKB2Ay0HpBgAAACqpOzoF6bsJPdXE191mvvnwSfV7e602HMwwKBmAsqJ0AwAAAJVYywBPff9IT/VvF2gzTz+dr3vmbtTM3w+ouNhqUDoAf4fSDQAAAFRyHi6OendYR714Wxs5mU0l82Kr9OpPe/XAgmhlnSk0MCGAS6F0AwAAAFWAyWTS/T0aa9FDkarv7Wrz2oo9aer/zhrtOJZpTDgAl0TpBgAAAKqQTiG19eOjvdSrua/N/Nips7pz1gZ9tumorFYuNwcqC0o3AAAAUMXUcXfW/FFd9PiNLWQ6f7W5CizFembJTk1evF1nCoqMCwigBKUbAAAAqILMDiY9dmNzLRjdRXXcnW1eW7ItSQPfW6cDaacNSgfgD5RuAAAAoArr1byelj7aU51CfGzm+1JPa8C7a/XD9uPGBAMgidINAAAAVHmB3m5a9FCkxvRsbDPPLbDokc+36cXvd6mgqNigdEDNRukGAAAAqgEns4Oeu7WNZt3TSR4ujjavzV9/RENmb1BS5lmD0gE1F6UbAAAAqEb6hgXq+4k91CrA02Yem5ip/m+v0e970wxKBtRMlG4AAACgmmlSz0NLxvfQ4E5BNvPMM4UaNX+LXo/aJ0sx24oBFYHSDQAAAFRDbs5mvXZXO/1ncJicHc//2m+1Sm+v2K/75m1Wxul8AxMCNQOlGwAAAKimTCaThnYO0TcPd1dInVo2r609kK7+b69VzNGTBqUDagZKNwAAAFDNhTbw1g+P9NRNbfxt5inZeRo6e6Pmrjkkq5XLzQF7oHQDAAAANYC3m5PmjAjX0/1ayexgKpkXFVv18tLdGv/ZVuXkFRqYEKieKN0AAABADWEymfTgNU31+QPd5OfpYvPa8p0puv3dddqdnG1QOqB6onQDAAAANUyXxnX046M9Fdmkrs38cHquBr63Tl9GJxqUDKh+HI0OAAAAAKDi+Xm66pMxXfTGr/v03m8HS+b5RcX6x1c7FHP0lF68va3SsvP1+ZYEbU/MVKGlWMF1aunO8CBFNqkrk8n0F18B1U3WmUJ9GZOoNfvTdTq/SL4ezuoXFqi+oYE2T8iHLZO1hj0xITs7W97e3srKypKXl5fRcQAAACqtHjNWKinzrCSpgY+b1k3pbXAi2MvKPal6fNF2ZZ21vae7jruzTuYWXPSYjiE+mn1vuPy8XCsiIgz2ZXSinv9ul84WWkq9Fujtqpn3dFLHkNoGJKv8+OsIAAAAoIbr3cpfPz7SU+2CvG3mlyrckrQtIVN3f7BRWWd4+Fp1983WY/rHVzsuWrglKTkrT/fO3aT44zwP4GIML90zZ85U48aN5erqqvDwcK1Zs+Yv169atUrh4eFydXVVkyZN9P7771dQUgAAAKD6Cq5TS1+Oi9S93ULKfMyhE7materg3y9ElZWbX6QXvtv19+sKLHrx+79fVxMZek/3okWLNGnSJM2cOVM9evTQ7Nmz1bdvX8XHxyskpPS/7IcPH1a/fv30wAMP6NNPP9W6des0fvx41atXT4MHDzbgOwAAAACqDxdHs14eGKY9yTmKPnqqTMfMX3dY+UUWmbm/u1ranZKtnPyiMq3dfOSk9qRkq1UAt/H+maH3dHft2lWdOnXSrFmzSmatW7fWwIEDNX369FLr//nPf+r777/X7t27S2bjxo3T9u3btWHDhot+jfz8fOXn55d8np2dreDgYO7pBgAA+Bvc011zdfv3CqVk5xkdA1XQ87e20eiejY2OUakYdnl5QUGBYmJi1KdPH5t5nz59tH79+oses2HDhlLrb775ZkVHR6uw8OL3kkyfPl3e3t4lH8HBweXzDQAAAADV1JmCsp3ZBC50qfu+azLDSnd6erosFov8/f1t5v7+/kpJSbnoMSkpKRddX1RUpPT09IseM3XqVGVlZZV8JCay5yAAAEBZBHq7qoGPmxr4uCnQmydU1yQ8kRxXqp6ni9ERKh3D9+m+cG8/q9X6l/v9XWz9xeZ/cHFxkYsL/+ABAAAu11cPdzc6AgxyW7v6euPXfWVa27Seu94Y2kEmcU93dbT9WKae/XZnmda6ODqoTxv/v19YwxhWun19fWU2m0ud1U5LSyt1NvsPAQEBF13v6OiounXr2i0rAAAAUJMM6xqs2asP6kzB318qPLF3M7UL8rF/KBgitIGXPtuUoN3Jf78d2F0RQfKp5VwBqaoWwy4vd3Z2Vnh4uKKiomzmUVFR6t794n+rGhkZWWr9L7/8ooiICDk5OdktKwAAAFCT+Hm66p1hHeVk/uuz1/d2C9HADg0qKBWMYDKZNPOeTn972XjHEB893a91BaWqWgzdp3vy5MmaO3eu5s2bp927d+vxxx9XQkKCxo0bJ+nc/dgjR44sWT9u3DgdPXpUkydP1u7duzVv3jx9+OGHevLJJ436FgAAAIBq6YbW/lr4QDeFN6xd6rVAb1e9cFsb/WtA6F/eGorqobGvu5aM766+oQEyO9j+8/ZwcdToHo312diuquVs+N3LlZKhW4ZJ0syZM/Xqq68qOTlZoaGheuONN3TNNddIku6//34dOXJEv//+e8n6VatW6fHHH9euXbtUv359/fOf/ywp6WWRnZ0tb29vtgwDAAAAymhPSrZiEzJVaClWSF139WhaV45mQ8/fwSApWXladyBduQVFquvuomtb1pOHC2X7rxheuisapRsAAAAAUFH46ykAAAAAAOyE0g0AAAAAgJ1QugEAAAAAsBNKNwAAAAAAdkLpBgAAAADATijdAAAAAADYCaUbAAAAAAA7oXQDAAAAAGAnlG4AAAAAAOyE0g0AAAAAgJ1QugEAAAAAsBNHowNUNKvVKknKzs42OAkAAAAAoKrz9PSUyWS65Os1rnTn5ORIkoKDgw1OAgAAAACo6rKysuTl5XXJ103WP0791hDFxcU6fvz43/5tRHWXnZ2t4OBgJSYm/uUPCKo/fhbwB34W8Gf8POAP/CzgD/ws4A/8LNjiTPcFHBwcFBQUZHSMSsPLy4t/USCJnwWcx88C/oyfB/yBnwX8gZ8F/IGfhbLhQWoAAAAAANgJpRsAAAAAADuhdNdQLi4ueuGFF+Ti4mJ0FBiMnwX8gZ8F/Bk/D/gDPwv4Az8L+AM/C5enxj1IDQAAAACAisKZbgAAAAAA7ITSDQAAAACAnVC6AQAAAACwE0o3AAAAAAB2QumugWbOnKnGjRvL1dVV4eHhWrNmjdGRYIDVq1frtttuU/369WUymfTtt98aHQkGmT59ujp37ixPT0/5+flp4MCB2rt3r9GxYIBZs2apXbt28vLykpeXlyIjI7V8+XKjY6ESmD59ukwmkyZNmmR0FBjgxRdflMlksvkICAgwOhYMUFRUpGeffVaNGzeWm5ubmjRpomnTpqm4uNjoaJUapbuGWbRokSZNmqRnnnlG27ZtU69evdS3b18lJCQYHQ0VLDc3V+3bt9e7775rdBQYbNWqVZowYYI2btyoqKgoFRUVqU+fPsrNzTU6GipYUFCQZsyYoejoaEVHR6t3794aMGCAdu3aZXQ0GGjLli2aM2eO2rVrZ3QUGKht27ZKTk4u+YiLizM6Egzwn//8R++//77effdd7d69W6+++qr++9//6p133jE6WqXGlmE1TNeuXdWpUyfNmjWrZNa6dWsNHDhQ06dPNzAZjGQymbRkyRINHDjQ6CioBE6cOCE/Pz+tWrVK11xzjdFxYLA6derov//9r8aMGWN0FBjg9OnT6tSpk2bOnKmXX35ZHTp00Jtvvml0LFSwF198Ud9++61iY2ONjgKD3XrrrfL399eHH35YMhs8eLBq1aqlTz75xMBklRtnumuQgoICxcTEqE+fPjbzPn36aP369QalAlDZZGVlSTpXtlBzWSwWffHFF8rNzVVkZKTRcWCQCRMmqH///rrxxhuNjgKD7d+/X/Xr11fjxo11991369ChQ0ZHggF69uypFStWaN++fZKk7du3a+3aterXr5/BySo3R6MDoOKkp6fLYrHI39/fZu7v76+UlBSDUgGoTKxWqyZPnqyePXsqNDTU6DgwQFxcnCIjI5WXlycPDw8tWbJEbdq0MToWDPDFF18oJiZG0dHRRkeBwbp27aoFCxaoRYsWSk1N1csvv6zu3btr165dqlu3rtHxUIH++c9/KisrS61atZLZbJbFYtErr7yiYcOGGR2tUqN010Amk8nmc6vVWmoGoGaaOHGiduzYobVr1xodBQZp2bKlYmNjlZmZqa+//lr33XefVq1aRfGuYRITE/XYY4/pl19+kaurq9FxYLC+ffuW/DksLEyRkZFq2rSpPv74Y02ePNnAZKhoixYt0qeffqqFCxeqbdu2io2N1aRJk1S/fn3dd999RsertCjdNYivr6/MZnOps9ppaWmlzn4DqHkeeeQRff/991q9erWCgoKMjgODODs7q1mzZpKkiIgIbdmyRW+99ZZmz55tcDJUpJiYGKWlpSk8PLxkZrFYtHr1ar377rvKz8+X2Ww2MCGM5O7urrCwMO3fv9/oKKhg//jHPzRlyhTdfffdks79JczRo0c1ffp0Svdf4J7uGsTZ2Vnh4eGKioqymUdFRal79+4GpQJgNKvVqokTJ+qbb77RypUr1bhxY6MjoRKxWq3Kz883OgYq2A033KC4uDjFxsaWfEREROiee+5RbGwshbuGy8/P1+7duxUYGGh0FFSwM2fOyMHBtkKazWa2DPsbnOmuYSZPnqwRI0YoIiJCkZGRmjNnjhISEjRu3Dijo6GCnT59WgcOHCj5/PDhw4qNjVWdOnUUEhJiYDJUtAkTJmjhwoX67rvv5OnpWXI1jLe3t9zc3AxOh4r09NNPq2/fvgoODlZOTo6++OIL/f777/rpp5+MjoYK5unpWeq5Du7u7qpbty7Pe6iBnnzySd12220KCQlRWlqaXn75ZWVnZ3Nmswa67bbb9MorrygkJERt27bVtm3b9Prrr2v06NFGR6vUKN01zNChQ5WRkaFp06YpOTlZoaGhWrZsmRo2bGh0NFSw6OhoXX/99SWf/3FP1n333af58+cblApG+GMLweuuu85m/tFHH+n++++v+EAwTGpqqkaMGKHk5GR5e3urXbt2+umnn3TTTTcZHQ2AgY4dO6Zhw4YpPT1d9erVU7du3bRx40Z+f6yB3nnnHT333HMaP3680tLSVL9+fT300EN6/vnnjY5WqbFPNwAAAAAAdsI93QAAAAAA2AmlGwAAAAAAO6F0AwAAAABgJ5RuAAAAAADshNINAAAAAICdULoBAAAAALATSjcAAAAAAHZC6QYAAAAAwE4o3QAA1DAmk0nffvutJOnIkSMymUyKjY01NBMAANUVpRsAgGomLS1NDz30kEJCQuTi4qKAgADdfPPN2rBhgyQpOTlZffv2vaz3/Prrr9W1a1d5e3vL09NTbdu21RNPPGGP+AAAVCuORgcAAADla/DgwSosLNTHH3+sJk2aKDU1VStWrNDJkyclSQEBAZf1fr/++qvuvvtu/fvf/9btt98uk8mk+Ph4rVixwh7xAQCoVkxWq9VqdAgAAFA+MjMzVbt2bf3++++69tprL7rGZDJpyZIlGjhwoI4cOaLGjRvr888/19tvv62tW7eqadOmeu+993TddddJkiZNmqTt27frt99+u+TXffHFF/Xtt9/q4Ycf1ssvv6yMjAz1799fH3zwgXx8fOzwnQIAUDVweTkAANWIh4eHPDw89O233yo/P7/Mx/3jH//QE088oW3btql79+66/fbblZGRIencmfFdu3Zp586df/keBw4c0OLFi/XDDz/op59+UmxsrCZMmHBV3w8AAFUdpRsAgGrE0dFR8+fP18cffywfHx/16NFDTz/9tHbs2PGXx02cOFGDBw9W69atNWvWLHl7e+vDDz+UJD3yyCPq3LmzwsLC1KhRI919992aN29eqVKfl5enjz/+WB06dNA111yjd955R1988YVSUlLs9v0CAFDZUboBAKhmBg8erOPHj+v777/XzTffrN9//12dOnXS/PnzL3lMZGRkyZ8dHR0VERGh3bt3S5Lc3d21dOlSHThwQM8++6w8PDz0xBNPqEuXLjpz5kzJcSEhIQoKCrJ5z+LiYu3du7f8v0kAAKoISjcAANWQq6urbrrpJj3//PNav3697r//fr3wwguX9R4mk8nm86ZNm2rs2LGaO3eutm7dqvj4eC1atOhvj7/wfQAAqEko3QAA1ABt2rRRbm7uJV/fuHFjyZ+LiooUExOjVq1aXXJ9o0aNVKtWLZv3TEhI0PHjx0s+37BhgxwcHNSiRYurTA8AQNXFlmEAAFQjGRkZuuuuuzR69Gi1a9dOnp6eio6O1quvvqoBAwZc8rj33ntPzZs3V+vWrfXGG2/o1KlTGj16tKRzTyY/c+aM+vXrp4YNGyozM1Nvv/22CgsLddNNN5W8h6urq+677z699tprys7O1qOPPqohQ4Zc9hZlAABUJ5RuAACqEQ8PD3Xt2lVvvPGGDh48qMLCQgUHB+uBBx7Q008/fcnjZsyYof/85z/atm2bmjZtqu+++06+vr6SpGuvvVbvvfeeRo4cqdTUVNWuXVsdO3bUL7/8opYtW5a8R7NmzXTHHXeoX79+OnnypPr166eZM2fa/XsGAKAyY59uAABw1f7Ypzs2NtboKAAAVCrc0w0AAAAAgJ1QugEAAAAAsBMuLwcAAAAAwE440w0AAAAAgJ1QugEAAAAAsBNKNwAAAAAAdkLpBgAAAADATijdAAAAAADYCaUbAAAAAAA7oXQDAAAAAGAnlG4AAAAAAOzk/wFJKfq8EdBjhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, col in enumerate(['SibSp', 'Parch']):\n",
    "    plt.figure(i)\n",
    "    sns.catplot(x=col, y='Survived', data = tit_data, kind = 'point', aspect = 2, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e404014e",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data['Family_cnt'] = tit_data['SibSp'] + tit_data['Parch']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45c503c",
   "metadata": {},
   "source": [
    "### C. Drop Unnecessary Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d7c7084",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data.drop(['PassengerId', 'SibSp', 'Parch'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa22bef1",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age            Ticket     Fare Cabin Embarked  Family_cnt  \n",
       "0    male  22.0         A/5 21171   7.2500   NaN        S           1  \n",
       "1  female  38.0          PC 17599  71.2833   C85        C           1  \n",
       "2  female  26.0  STON/O2. 3101282   7.9250   NaN        S           0  \n",
       "3  female  35.0            113803  53.1000  C123        S           1  \n",
       "4    male  35.0            373450   8.0500   NaN        S           0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700a5bd0",
   "metadata": {},
   "source": [
    "## 3. Clean Categorical Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c77e76",
   "metadata": {},
   "source": [
    "### A. Fill in missing and create indicator for Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c41f46d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived        0\n",
       "Pclass          0\n",
       "Name            0\n",
       "Sex             0\n",
       "Age             0\n",
       "Ticket          0\n",
       "Fare            0\n",
       "Cabin         687\n",
       "Embarked        2\n",
       "Family_cnt      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.isnull().sum ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6146cc3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "False    0.666667\n",
       "True     0.299854\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.groupby(tit_data['Cabin'].isnull())['Survived'].mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4604bb07",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data['Cabin_ind'] = np.where(tit_data['Cabin'].isnull(), 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20462e2c",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family_cnt</th>\n",
       "      <th>Cabin_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age            Ticket     Fare Cabin Embarked  Family_cnt  \\\n",
       "0    male  22.0         A/5 21171   7.2500   NaN        S           1   \n",
       "1  female  38.0          PC 17599  71.2833   C85        C           1   \n",
       "2  female  26.0  STON/O2. 3101282   7.9250   NaN        S           0   \n",
       "3  female  35.0            113803  53.1000  C123        S           1   \n",
       "4    male  35.0            373450   8.0500   NaN        S           0   \n",
       "\n",
       "   Cabin_ind  \n",
       "0          0  \n",
       "1          1  \n",
       "2          0  \n",
       "3          1  \n",
       "4          0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6f4018",
   "metadata": {},
   "source": [
    "### B. Convert Sex to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3f91644",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_num = {'male' : 0, 'female': 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eef5d0a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data['Sex'] = tit_data['Sex'].map(gender_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4ac8a4cc",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family_cnt</th>\n",
       "      <th>Cabin_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    0   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
       "2         1       3                             Heikkinen, Miss. Laina    1   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
       "4         0       3                           Allen, Mr. William Henry    0   \n",
       "\n",
       "    Age            Ticket     Fare Cabin Embarked  Family_cnt  Cabin_ind  \n",
       "0  22.0         A/5 21171   7.2500   NaN        S           1          0  \n",
       "1  38.0          PC 17599  71.2833   C85        C           1          1  \n",
       "2  26.0  STON/O2. 3101282   7.9250   NaN        S           0          0  \n",
       "3  35.0            113803  53.1000  C123        S           1          1  \n",
       "4  35.0            373450   8.0500   NaN        S           0          0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1158ed8",
   "metadata": {},
   "source": [
    "### C. Drop Unnecessary variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a8237e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data.drop(['Cabin', 'Embarked', 'Name', 'Ticket'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f90219d3",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family_cnt</th>\n",
       "      <th>Cabin_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare  Family_cnt  Cabin_ind\n",
       "0         0       3    0  22.0   7.2500           1          0\n",
       "1         1       1    1  38.0  71.2833           1          1\n",
       "2         1       3    1  26.0   7.9250           0          0\n",
       "3         1       1    1  35.0  53.1000           1          1\n",
       "4         0       3    0  35.0   8.0500           0          0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5beb5d4",
   "metadata": {},
   "source": [
    "## 3. Write out Cleaned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e9d8ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tit_data.to_csv('train_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792172ac",
   "metadata": {},
   "source": [
    "## 4. Split into Train, Validation and Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b6f98c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_data = pd.read_csv('train_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ccc1897d",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = cleaned_data.drop('Survived', axis = 1)\n",
    "labels = cleaned_data['Survived']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size = 0.4, random_state = 42)\n",
    "\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size = 0.5, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8a908697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6\n",
      "0.2\n",
      "0.2\n"
     ]
    }
   ],
   "source": [
    "for dataset in [y_train, y_val, y_test]:\n",
    "    print(round(len(dataset) / len(labels), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9773c462",
   "metadata": {},
   "source": [
    "#### Write out all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dce97ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.to_csv('train_features.csv', index=False)\n",
    "X_val.to_csv('val_features.csv', index=False)\n",
    "X_test.to_csv('test_features.csv', index=False)\n",
    "\n",
    "y_train.to_csv('train_labels.csv', index=False)\n",
    "y_val.to_csv('val_labels.csv', index=False)\n",
    "y_test.to_csv('test_labels.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ed81cc",
   "metadata": {},
   "source": [
    "## 5. Importing Boosting Algorithms for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c8bb05e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'init': None,\n",
       " 'learning_rate': 0.1,\n",
       " 'loss': 'deviance',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_iter_no_change': None,\n",
       " 'random_state': None,\n",
       " 'subsample': 1.0,\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier\n",
    "\n",
    "GradientBoostingClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1be08205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'SAMME.R',\n",
       " 'base_estimator': None,\n",
       " 'learning_rate': 1.0,\n",
       " 'n_estimators': 50,\n",
       " 'random_state': None}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AdaBoostClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0e5e045",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a61994bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_features = X_train\n",
    "tr_labels = y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d253c499",
   "metadata": {},
   "source": [
    "### A. Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ec44972",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(results):\n",
    "    print('BEST PARAMS : {}\\n'.format(results.best_params_))\n",
    "    \n",
    "    means = results.cv_results_['mean_test_score']\n",
    "    stds = results.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, results.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "288e83ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST PARAMS : {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 500}\n",
      "\n",
      "0.624 (+/-0.007) for {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 5}\n",
      "0.796 (+/-0.115) for {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.796 (+/-0.115) for {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.811 (+/-0.117) for {'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 500}\n",
      "0.624 (+/-0.007) for {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 5}\n",
      "0.811 (+/-0.069) for {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.824 (+/-0.086) for {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.826 (+/-0.079) for {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 500}\n",
      "0.624 (+/-0.007) for {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 5}\n",
      "0.809 (+/-0.046) for {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 50}\n",
      "0.822 (+/-0.059) for {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 250}\n",
      "0.816 (+/-0.042) for {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 500}\n",
      "0.624 (+/-0.007) for {'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 5}\n",
      "0.817 (+/-0.053) for {'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 50}\n",
      "0.805 (+/-0.025) for {'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 250}\n",
      "0.798 (+/-0.019) for {'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 500}\n",
      "0.624 (+/-0.007) for {'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 5}\n",
      "0.796 (+/-0.046) for {'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 50}\n",
      "0.788 (+/-0.034) for {'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 250}\n",
      "0.775 (+/-0.019) for {'learning_rate': 0.01, 'max_depth': 9, 'n_estimators': 500}\n",
      "0.796 (+/-0.115) for {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 5}\n",
      "0.815 (+/-0.119) for {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.818 (+/-0.109) for {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.818 (+/-0.118) for {'learning_rate': 0.1, 'max_depth': 1, 'n_estimators': 500}\n",
      "0.813 (+/-0.071) for {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 5}\n",
      "0.822 (+/-0.072) for {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.802 (+/-0.035) for {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.79 (+/-0.038) for {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 500}\n",
      "0.813 (+/-0.055) for {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 5}\n",
      "0.813 (+/-0.048) for {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 50}\n",
      "0.798 (+/-0.048) for {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 250}\n",
      "0.794 (+/-0.048) for {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 500}\n",
      "0.807 (+/-0.045) for {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 5}\n",
      "0.792 (+/-0.021) for {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 50}\n",
      "0.802 (+/-0.024) for {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 250}\n",
      "0.803 (+/-0.032) for {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 500}\n",
      "0.8 (+/-0.048) for {'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 5}\n",
      "0.798 (+/-0.049) for {'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 50}\n",
      "0.79 (+/-0.034) for {'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 250}\n",
      "0.798 (+/-0.041) for {'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 500}\n",
      "0.818 (+/-0.099) for {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 5}\n",
      "0.818 (+/-0.114) for {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.815 (+/-0.069) for {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.79 (+/-0.08) for {'learning_rate': 1, 'max_depth': 1, 'n_estimators': 500}\n",
      "0.813 (+/-0.065) for {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 5}\n",
      "0.798 (+/-0.043) for {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.803 (+/-0.033) for {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.8 (+/-0.05) for {'learning_rate': 1, 'max_depth': 3, 'n_estimators': 500}\n",
      "0.781 (+/-0.035) for {'learning_rate': 1, 'max_depth': 5, 'n_estimators': 5}\n",
      "0.792 (+/-0.063) for {'learning_rate': 1, 'max_depth': 5, 'n_estimators': 50}\n",
      "0.807 (+/-0.046) for {'learning_rate': 1, 'max_depth': 5, 'n_estimators': 250}\n",
      "0.803 (+/-0.058) for {'learning_rate': 1, 'max_depth': 5, 'n_estimators': 500}\n",
      "0.785 (+/-0.016) for {'learning_rate': 1, 'max_depth': 7, 'n_estimators': 5}\n",
      "0.794 (+/-0.035) for {'learning_rate': 1, 'max_depth': 7, 'n_estimators': 50}\n",
      "0.773 (+/-0.049) for {'learning_rate': 1, 'max_depth': 7, 'n_estimators': 250}\n",
      "0.779 (+/-0.06) for {'learning_rate': 1, 'max_depth': 7, 'n_estimators': 500}\n",
      "0.76 (+/-0.048) for {'learning_rate': 1, 'max_depth': 9, 'n_estimators': 5}\n",
      "0.79 (+/-0.049) for {'learning_rate': 1, 'max_depth': 9, 'n_estimators': 50}\n",
      "0.8 (+/-0.062) for {'learning_rate': 1, 'max_depth': 9, 'n_estimators': 250}\n",
      "0.809 (+/-0.041) for {'learning_rate': 1, 'max_depth': 9, 'n_estimators': 500}\n",
      "0.204 (+/-0.115) for {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 5}\n",
      "0.204 (+/-0.115) for {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.204 (+/-0.115) for {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.204 (+/-0.115) for {'learning_rate': 10, 'max_depth': 1, 'n_estimators': 500}\n",
      "0.369 (+/-0.374) for {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 5}\n",
      "0.369 (+/-0.374) for {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.369 (+/-0.374) for {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.369 (+/-0.374) for {'learning_rate': 10, 'max_depth': 3, 'n_estimators': 500}\n",
      "0.487 (+/-0.153) for {'learning_rate': 10, 'max_depth': 5, 'n_estimators': 5}\n",
      "0.498 (+/-0.141) for {'learning_rate': 10, 'max_depth': 5, 'n_estimators': 50}\n",
      "0.502 (+/-0.137) for {'learning_rate': 10, 'max_depth': 5, 'n_estimators': 250}\n",
      "0.494 (+/-0.128) for {'learning_rate': 10, 'max_depth': 5, 'n_estimators': 500}\n",
      "0.614 (+/-0.202) for {'learning_rate': 10, 'max_depth': 7, 'n_estimators': 5}\n",
      "0.601 (+/-0.207) for {'learning_rate': 10, 'max_depth': 7, 'n_estimators': 50}\n",
      "0.595 (+/-0.192) for {'learning_rate': 10, 'max_depth': 7, 'n_estimators': 250}\n",
      "0.599 (+/-0.205) for {'learning_rate': 10, 'max_depth': 7, 'n_estimators': 500}\n",
      "0.729 (+/-0.139) for {'learning_rate': 10, 'max_depth': 9, 'n_estimators': 5}\n",
      "0.697 (+/-0.129) for {'learning_rate': 10, 'max_depth': 9, 'n_estimators': 50}\n",
      "0.715 (+/-0.157) for {'learning_rate': 10, 'max_depth': 9, 'n_estimators': 250}\n",
      "0.693 (+/-0.121) for {'learning_rate': 10, 'max_depth': 9, 'n_estimators': 500}\n",
      "0.376 (+/-0.007) for {'learning_rate': 100, 'max_depth': 1, 'n_estimators': 5}\n",
      "0.376 (+/-0.007) for {'learning_rate': 100, 'max_depth': 1, 'n_estimators': 50}\n",
      "0.376 (+/-0.007) for {'learning_rate': 100, 'max_depth': 1, 'n_estimators': 250}\n",
      "0.376 (+/-0.007) for {'learning_rate': 100, 'max_depth': 1, 'n_estimators': 500}\n",
      "0.281 (+/-0.118) for {'learning_rate': 100, 'max_depth': 3, 'n_estimators': 5}\n",
      "0.281 (+/-0.118) for {'learning_rate': 100, 'max_depth': 3, 'n_estimators': 50}\n",
      "0.281 (+/-0.118) for {'learning_rate': 100, 'max_depth': 3, 'n_estimators': 250}\n",
      "0.281 (+/-0.118) for {'learning_rate': 100, 'max_depth': 3, 'n_estimators': 500}\n",
      "0.462 (+/-0.179) for {'learning_rate': 100, 'max_depth': 5, 'n_estimators': 5}\n",
      "0.472 (+/-0.178) for {'learning_rate': 100, 'max_depth': 5, 'n_estimators': 50}\n",
      "0.464 (+/-0.181) for {'learning_rate': 100, 'max_depth': 5, 'n_estimators': 250}\n",
      "0.462 (+/-0.179) for {'learning_rate': 100, 'max_depth': 5, 'n_estimators': 500}\n",
      "0.599 (+/-0.211) for {'learning_rate': 100, 'max_depth': 7, 'n_estimators': 5}\n",
      "0.58 (+/-0.127) for {'learning_rate': 100, 'max_depth': 7, 'n_estimators': 50}\n",
      "0.571 (+/-0.144) for {'learning_rate': 100, 'max_depth': 7, 'n_estimators': 250}\n",
      "0.565 (+/-0.128) for {'learning_rate': 100, 'max_depth': 7, 'n_estimators': 500}\n",
      "0.622 (+/-0.161) for {'learning_rate': 100, 'max_depth': 9, 'n_estimators': 5}\n",
      "0.633 (+/-0.108) for {'learning_rate': 100, 'max_depth': 9, 'n_estimators': 50}\n",
      "0.653 (+/-0.087) for {'learning_rate': 100, 'max_depth': 9, 'n_estimators': 250}\n",
      "0.655 (+/-0.152) for {'learning_rate': 100, 'max_depth': 9, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "parameters = {\n",
    "    'n_estimators': [5, 50, 250 ,500],\n",
    "    'max_depth' : [1, 3, 5, 7, 9],\n",
    "    'learning_rate' : [0.01, 0.1, 1, 10 , 100]\n",
    "}\n",
    "\n",
    "cv = GridSearchCV(gb, parameters, cv = 5)\n",
    "cv.fit(tr_features, tr_labels.values.ravel())\n",
    "\n",
    "print_results(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "09ccc268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.01, n_estimators=500)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f400b59d",
   "metadata": {},
   "source": [
    "### B.Write out pickled model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "98ed0d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GB_model.pkl']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(cv.best_estimator_, 'GB_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dcfbb8f",
   "metadata": {},
   "source": [
    "## 6. Importing Bagging Algorithms for Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b1e26041",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28da2ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestClassifier().get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfd57e3",
   "metadata": {},
   "source": [
    "### A. Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6806de63",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(results):\n",
    "    print('BEST PARAMS : {}\\n'.format(results.best_params_))\n",
    "    \n",
    "    means = results.cv_results_['mean_test_score']\n",
    "    stds = results.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, results.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f66bb498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST PARAMS : {'max_depth': 8, 'n_estimators': 250}\n",
      "\n",
      "0.792 (+/-0.098) for {'max_depth': 4, 'n_estimators': 5}\n",
      "0.807 (+/-0.113) for {'max_depth': 4, 'n_estimators': 50}\n",
      "0.815 (+/-0.115) for {'max_depth': 4, 'n_estimators': 250}\n",
      "0.815 (+/-0.119) for {'max_depth': 4, 'n_estimators': 500}\n",
      "0.796 (+/-0.123) for {'max_depth': 8, 'n_estimators': 5}\n",
      "0.809 (+/-0.09) for {'max_depth': 8, 'n_estimators': 50}\n",
      "0.822 (+/-0.086) for {'max_depth': 8, 'n_estimators': 250}\n",
      "0.813 (+/-0.089) for {'max_depth': 8, 'n_estimators': 500}\n",
      "0.796 (+/-0.096) for {'max_depth': 16, 'n_estimators': 5}\n",
      "0.805 (+/-0.072) for {'max_depth': 16, 'n_estimators': 50}\n",
      "0.815 (+/-0.066) for {'max_depth': 16, 'n_estimators': 250}\n",
      "0.818 (+/-0.08) for {'max_depth': 16, 'n_estimators': 500}\n",
      "0.8 (+/-0.063) for {'max_depth': 32, 'n_estimators': 5}\n",
      "0.805 (+/-0.087) for {'max_depth': 32, 'n_estimators': 50}\n",
      "0.809 (+/-0.073) for {'max_depth': 32, 'n_estimators': 250}\n",
      "0.811 (+/-0.068) for {'max_depth': 32, 'n_estimators': 500}\n",
      "0.792 (+/-0.119) for {'max_depth': None, 'n_estimators': 5}\n",
      "0.815 (+/-0.068) for {'max_depth': None, 'n_estimators': 50}\n",
      "0.807 (+/-0.077) for {'max_depth': None, 'n_estimators': 250}\n",
      "0.813 (+/-0.079) for {'max_depth': None, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "\n",
    "parameters = {\n",
    "    'n_estimators' : [5, 50, 250, 500],\n",
    "    'max_depth' : [4, 8, 16, 32, None]\n",
    "}\n",
    "\n",
    "cv = GridSearchCV(rf,parameters, cv = 5)\n",
    "cv.fit(tr_features, tr_labels.values.ravel())\n",
    "\n",
    "print_results(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a71a39a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=8, n_estimators=250)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e9415b",
   "metadata": {},
   "source": [
    "### B. Write out pickled model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "08887568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RF_model.pkl']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(cv.best_estimator_, 'RF_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6aae66",
   "metadata": {},
   "source": [
    "### 7. Importing Stacking Algorithms for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bc4e7683",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7261505d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b9aa949f",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [('gb',GradientBoostingClassifier()), ('rf',RandomForestClassifier())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c6ea65e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cv': None,\n",
       " 'estimators': [('gb', GradientBoostingClassifier()),\n",
       "  ('rf', RandomForestClassifier())],\n",
       " 'final_estimator': None,\n",
       " 'n_jobs': None,\n",
       " 'passthrough': False,\n",
       " 'stack_method': 'auto',\n",
       " 'verbose': 0,\n",
       " 'gb': GradientBoostingClassifier(),\n",
       " 'rf': RandomForestClassifier(),\n",
       " 'gb__ccp_alpha': 0.0,\n",
       " 'gb__criterion': 'friedman_mse',\n",
       " 'gb__init': None,\n",
       " 'gb__learning_rate': 0.1,\n",
       " 'gb__loss': 'deviance',\n",
       " 'gb__max_depth': 3,\n",
       " 'gb__max_features': None,\n",
       " 'gb__max_leaf_nodes': None,\n",
       " 'gb__min_impurity_decrease': 0.0,\n",
       " 'gb__min_samples_leaf': 1,\n",
       " 'gb__min_samples_split': 2,\n",
       " 'gb__min_weight_fraction_leaf': 0.0,\n",
       " 'gb__n_estimators': 100,\n",
       " 'gb__n_iter_no_change': None,\n",
       " 'gb__random_state': None,\n",
       " 'gb__subsample': 1.0,\n",
       " 'gb__tol': 0.0001,\n",
       " 'gb__validation_fraction': 0.1,\n",
       " 'gb__verbose': 0,\n",
       " 'gb__warm_start': False,\n",
       " 'rf__bootstrap': True,\n",
       " 'rf__ccp_alpha': 0.0,\n",
       " 'rf__class_weight': None,\n",
       " 'rf__criterion': 'gini',\n",
       " 'rf__max_depth': None,\n",
       " 'rf__max_features': 'auto',\n",
       " 'rf__max_leaf_nodes': None,\n",
       " 'rf__max_samples': None,\n",
       " 'rf__min_impurity_decrease': 0.0,\n",
       " 'rf__min_samples_leaf': 1,\n",
       " 'rf__min_samples_split': 2,\n",
       " 'rf__min_weight_fraction_leaf': 0.0,\n",
       " 'rf__n_estimators': 100,\n",
       " 'rf__n_jobs': None,\n",
       " 'rf__oob_score': False,\n",
       " 'rf__random_state': None,\n",
       " 'rf__verbose': 0,\n",
       " 'rf__warm_start': False}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StackingClassifier(estimators = estimators).get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0cbf1412",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eeb5739",
   "metadata": {},
   "source": [
    "### A. Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bf1691a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_results(results):\n",
    "    print('BEST PARAMS : {}\\n'.format(results.best_params_))\n",
    "    \n",
    "    means = results.cv_results_['mean_test_score']\n",
    "    stds = results.cv_results_['std_test_score']\n",
    "    for mean, std, params in zip(means, stds, results.cv_results_['params']):\n",
    "        print('{} (+/-{}) for {}'.format(round(mean, 3), round(std * 2, 3), params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "04d85016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cv': None,\n",
       " 'estimators': [('gb', GradientBoostingClassifier()),\n",
       "  ('rf', RandomForestClassifier())],\n",
       " 'final_estimator': None,\n",
       " 'n_jobs': None,\n",
       " 'passthrough': False,\n",
       " 'stack_method': 'auto',\n",
       " 'verbose': 0,\n",
       " 'gb': GradientBoostingClassifier(),\n",
       " 'rf': RandomForestClassifier(),\n",
       " 'gb__ccp_alpha': 0.0,\n",
       " 'gb__criterion': 'friedman_mse',\n",
       " 'gb__init': None,\n",
       " 'gb__learning_rate': 0.1,\n",
       " 'gb__loss': 'deviance',\n",
       " 'gb__max_depth': 3,\n",
       " 'gb__max_features': None,\n",
       " 'gb__max_leaf_nodes': None,\n",
       " 'gb__min_impurity_decrease': 0.0,\n",
       " 'gb__min_samples_leaf': 1,\n",
       " 'gb__min_samples_split': 2,\n",
       " 'gb__min_weight_fraction_leaf': 0.0,\n",
       " 'gb__n_estimators': 100,\n",
       " 'gb__n_iter_no_change': None,\n",
       " 'gb__random_state': None,\n",
       " 'gb__subsample': 1.0,\n",
       " 'gb__tol': 0.0001,\n",
       " 'gb__validation_fraction': 0.1,\n",
       " 'gb__verbose': 0,\n",
       " 'gb__warm_start': False,\n",
       " 'rf__bootstrap': True,\n",
       " 'rf__ccp_alpha': 0.0,\n",
       " 'rf__class_weight': None,\n",
       " 'rf__criterion': 'gini',\n",
       " 'rf__max_depth': None,\n",
       " 'rf__max_features': 'auto',\n",
       " 'rf__max_leaf_nodes': None,\n",
       " 'rf__max_samples': None,\n",
       " 'rf__min_impurity_decrease': 0.0,\n",
       " 'rf__min_samples_leaf': 1,\n",
       " 'rf__min_samples_split': 2,\n",
       " 'rf__min_weight_fraction_leaf': 0.0,\n",
       " 'rf__n_estimators': 100,\n",
       " 'rf__n_jobs': None,\n",
       " 'rf__oob_score': False,\n",
       " 'rf__random_state': None,\n",
       " 'rf__verbose': 0,\n",
       " 'rf__warm_start': False}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimators = [('gb',GradientBoostingClassifier()), ('rf',RandomForestClassifier())]\n",
    "\n",
    "sc = StackingClassifier(estimators = estimators)\n",
    "\n",
    "sc.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "382f0a27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Advait\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEST PARAMS : {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "\n",
      "0.818 (+/-0.134) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 50, 'passthrough': True, 'rf__n_estimators': 50}\n",
      "0.817 (+/-0.137) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 50, 'passthrough': True, 'rf__n_estimators': 100}\n",
      "0.828 (+/-0.06) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 50, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "0.828 (+/-0.06) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 50, 'passthrough': False, 'rf__n_estimators': 100}\n",
      "0.822 (+/-0.123) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 100, 'passthrough': True, 'rf__n_estimators': 50}\n",
      "0.822 (+/-0.122) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 100, 'passthrough': True, 'rf__n_estimators': 100}\n",
      "0.815 (+/-0.05) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "0.817 (+/-0.057) for {'final_estimator': LogisticRegression(C=0.1), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 100}\n",
      "0.822 (+/-0.099) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 50, 'passthrough': True, 'rf__n_estimators': 50}\n",
      "0.817 (+/-0.098) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 50, 'passthrough': True, 'rf__n_estimators': 100}\n",
      "0.824 (+/-0.067) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 50, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "0.817 (+/-0.069) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 50, 'passthrough': False, 'rf__n_estimators': 100}\n",
      "0.824 (+/-0.111) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 100, 'passthrough': True, 'rf__n_estimators': 50}\n",
      "0.82 (+/-0.096) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 100, 'passthrough': True, 'rf__n_estimators': 100}\n",
      "0.828 (+/-0.048) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "0.818 (+/-0.059) for {'final_estimator': LogisticRegression(C=1), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 100}\n",
      "0.82 (+/-0.096) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 50, 'passthrough': True, 'rf__n_estimators': 50}\n",
      "0.817 (+/-0.091) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 50, 'passthrough': True, 'rf__n_estimators': 100}\n",
      "0.817 (+/-0.068) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 50, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "0.82 (+/-0.069) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 50, 'passthrough': False, 'rf__n_estimators': 100}\n",
      "0.818 (+/-0.087) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 100, 'passthrough': True, 'rf__n_estimators': 50}\n",
      "0.822 (+/-0.093) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 100, 'passthrough': True, 'rf__n_estimators': 100}\n",
      "0.818 (+/-0.059) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 50}\n",
      "0.822 (+/-0.055) for {'final_estimator': LogisticRegression(C=10), 'gb__n_estimators': 100, 'passthrough': False, 'rf__n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "parameters = {\n",
    "    'gb__n_estimators' : [50 , 100],\n",
    "    'rf__n_estimators' : [50, 100],\n",
    "    'final_estimator' : [LogisticRegression(C=0.1),\n",
    "                         LogisticRegression(C=1),\n",
    "                         LogisticRegression(C=10)],\n",
    "    'passthrough' : [True, False]\n",
    "}\n",
    "\n",
    "cv = GridSearchCV(sc, parameters, cv = 5)\n",
    "cv.fit(tr_features, tr_labels.values.ravel())\n",
    "\n",
    "print_results(cv)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bf286af",
   "metadata": {},
   "source": [
    "### B. Write out pickled model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "54512963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['stacked_model.pkl']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(cv.best_estimator_, 'stacked_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c41d32",
   "metadata": {},
   "source": [
    "### 8. Final: Compare Model results and final model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbdc7bf1",
   "metadata": {},
   "source": [
    "### A. Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b518d66d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "from time import time\n",
    "\n",
    "val_features = pd.read_csv('val_features.csv')\n",
    "val_labels = pd.read_csv('val_labels.csv')\n",
    "\n",
    "te_features = pd.read_csv('test_features.csv')\n",
    "te_labels = pd.read_csv('test_labels.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc921dd",
   "metadata": {},
   "source": [
    "### B. Read in Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "abaa7557",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_mdl = joblib.load('GB_model.pkl')\n",
    "rf_mdl = joblib.load('RF_model.pkl')\n",
    "stacked_mdl = joblib.load('stacked_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7277fa",
   "metadata": {},
   "source": [
    "### C. Evaluate Models on the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "169b85a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, features, labels):\n",
    "    start = time()\n",
    "    pred = model.predict(features)\n",
    "    end = time()\n",
    "    accuracy = round(accuracy_score(labels, pred), 3)\n",
    "    precision = round(precision_score(labels, pred), 3)\n",
    "    recall = round(recall_score(labels, pred), 3)\n",
    "    print('\\n {} -- Accuracy : {} / Precision : {} / Recall : {} \\n/ Latency : {}ms'.format(str(model), accuracy, precision, recall,\n",
    "                                                                                      round((end-start)*3, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "061aee73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " GradientBoostingClassifier(learning_rate=0.01, n_estimators=500) -- Accuracy : 0.809 / Precision : 0.804 / Recall : 0.631 \n",
      "/ Latency : 0.01ms\n",
      "\n",
      " RandomForestClassifier(max_depth=8, n_estimators=250) -- Accuracy : 0.826 / Precision : 0.84 / Recall : 0.646 \n",
      "/ Latency : 0.13ms\n",
      "\n",
      " StackingClassifier(estimators=[('gb', GradientBoostingClassifier()),\n",
      "                               ('rf', RandomForestClassifier(n_estimators=50))],\n",
      "                   final_estimator=LogisticRegression(C=1)) -- Accuracy : 0.809 / Precision : 0.816 / Recall : 0.615 \n",
      "/ Latency : 0.02ms\n"
     ]
    }
   ],
   "source": [
    "for mdl in [gb_mdl, rf_mdl, stacked_mdl]:\n",
    "    evaluate_model(mdl, val_features, val_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921b5159",
   "metadata": {},
   "source": [
    "### C. Evaluating the Best Model on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3c61f3ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " RandomForestClassifier(max_depth=8, n_estimators=250) -- Accuracy : 0.816 / Precision : 0.864 / Recall : 0.671 \n",
      "/ Latency : 0.13ms\n"
     ]
    }
   ],
   "source": [
    "evaluate_model(rf_mdl, te_features, te_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b66d9fc3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}