stock prediction .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12291bdb",
   "metadata": {},
   "source": [
    "Data Project from NeuroTrade \n",
    "## Table of Contents\n",
    "\n",
    "1. [Data Exploration](#data-exploration)\n",
    "2. [Feature Engineering](#feature-engineering)\n",
    "43. [Classical Machine Learning Algorithms](#classical-machine-learning-algorithms)\n",
    "   - [Logistic Regression](#logistic-regression)\n",
    "   - [Decision Tree](#decision-tree)\n",
    "   - [Random Forest](#random-forest)\n",
    "   - [Gradient Boosting Ensemble](#gradient-boosting-ensemble)\n",
    "4. [Deep Learning Algorithm](#deep-learning-algorithm)\n",
    "5. [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d193105f",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e782baf9",
   "metadata": {},
   "source": [
    "\n",
    "-In our initial exploration, we will load the data sets and see what data attributes are available to us. We will also plot the variables, to see if we can find some trends in the data, and explore the possibility of engineering some additional features. We will do the data loading and analysis in pandas, so let us load that library and begin exploring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68bcd892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4781, 7)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# load the training set\n",
    "df_train = pd.read_csv(\"AMZN_train.csv\")\n",
    "# (#rows, #columns)\n",
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "282b9f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4781 entries, 0 to 4780\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       4781 non-null   object \n",
      " 1   Open       4781 non-null   float64\n",
      " 2   High       4781 non-null   float64\n",
      " 3   Low        4781 non-null   float64\n",
      " 4   Close      4781 non-null   float64\n",
      " 5   Adj Close  4781 non-null   float64\n",
      " 6   Volume     4781 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 261.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905068af",
   "metadata": {},
   "source": [
    "The training data set consists of 4781 rows and 7 columns. These are presented above. In the cell below, we use the describe() method to get a quick overview of our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffe40d78",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4781.000000</td>\n",
       "      <td>4781.000000</td>\n",
       "      <td>4781.000000</td>\n",
       "      <td>4781.000000</td>\n",
       "      <td>4781.000000</td>\n",
       "      <td>4.781000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>127.619845</td>\n",
       "      <td>129.480122</td>\n",
       "      <td>125.697925</td>\n",
       "      <td>127.662449</td>\n",
       "      <td>127.662449</td>\n",
       "      <td>8.225935e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>145.693083</td>\n",
       "      <td>147.132537</td>\n",
       "      <td>144.053633</td>\n",
       "      <td>145.677581</td>\n",
       "      <td>145.677581</td>\n",
       "      <td>7.810188e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.406250</td>\n",
       "      <td>1.447917</td>\n",
       "      <td>1.312500</td>\n",
       "      <td>1.395833</td>\n",
       "      <td>1.395833</td>\n",
       "      <td>4.872000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.299999</td>\n",
       "      <td>34.849998</td>\n",
       "      <td>33.660000</td>\n",
       "      <td>34.310001</td>\n",
       "      <td>34.310001</td>\n",
       "      <td>4.200900e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62.880001</td>\n",
       "      <td>64.750000</td>\n",
       "      <td>60.937500</td>\n",
       "      <td>62.750000</td>\n",
       "      <td>62.750000</td>\n",
       "      <td>6.200100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>189.009995</td>\n",
       "      <td>191.600006</td>\n",
       "      <td>186.210007</td>\n",
       "      <td>189.029999</td>\n",
       "      <td>189.029999</td>\n",
       "      <td>9.239900e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>717.380005</td>\n",
       "      <td>722.450012</td>\n",
       "      <td>711.510010</td>\n",
       "      <td>717.929993</td>\n",
       "      <td>717.929993</td>\n",
       "      <td>1.043292e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  4781.000000  4781.000000  4781.000000  4781.000000  4781.000000   \n",
       "mean    127.619845   129.480122   125.697925   127.662449   127.662449   \n",
       "std     145.693083   147.132537   144.053633   145.677581   145.677581   \n",
       "min       1.406250     1.447917     1.312500     1.395833     1.395833   \n",
       "25%      34.299999    34.849998    33.660000    34.310001    34.310001   \n",
       "50%      62.880001    64.750000    60.937500    62.750000    62.750000   \n",
       "75%     189.009995   191.600006   186.210007   189.029999   189.029999   \n",
       "max     717.380005   722.450012   711.510010   717.929993   717.929993   \n",
       "\n",
       "             Volume  \n",
       "count  4.781000e+03  \n",
       "mean   8.225935e+06  \n",
       "std    7.810188e+06  \n",
       "min    4.872000e+05  \n",
       "25%    4.200900e+06  \n",
       "50%    6.200100e+06  \n",
       "75%    9.239900e+06  \n",
       "max    1.043292e+08  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79c79ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           4781\n",
       "unique          4781\n",
       "top       1997-05-15\n",
       "freq               1\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the above call to describe() works only for numerical columns\n",
    "# 'Date' is an object and we need to call it separately\n",
    "df_train[\"Date\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc500819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1997-05-15', '2016-05-13')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the time range\n",
    "df_train[\"Date\"].min(), df_train[\"Date\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf988f32",
   "metadata": {},
   "source": [
    "We can plot the stock prices as a function of time. These plots are the first thing you see on any stock market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15991607",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 9)\n",
    "_ = df_train.plot(x=\"Date\", y=[\"Close\", \"Open\", \"High\", \"Low\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388c24a1",
   "metadata": {},
   "source": [
    "We would like to repeat the same analysis for the validation and testing set, to make sure that they follow a similar distribution and that there are no surprising errors there. We create a function to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c64c986c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape (503, 7) \n",
      "\n",
      "Columns Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object') \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 503 entries, 0 to 502\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       503 non-null    object \n",
      " 1   Open       503 non-null    float64\n",
      " 2   High       503 non-null    float64\n",
      " 3   Low        503 non-null    float64\n",
      " 4   Close      503 non-null    float64\n",
      " 5   Adj Close  503 non-null    float64\n",
      " 6   Volume     503 non-null    int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 27.6+ KB\n",
      "\n",
      "               Open         High          Low        Close    Adj Close  \\\n",
      "count   503.000000   503.000000   503.000000   503.000000   503.000000   \n",
      "mean    992.201292   999.898131   982.574513   991.828966   991.828966   \n",
      "std     255.496588   259.220381   250.100614   254.885469   254.885469   \n",
      "min     689.559998   696.820007   682.119995   691.359985   691.359985   \n",
      "25%     780.500000   785.625000   772.410004   780.294983   780.294983   \n",
      "50%     948.000000   954.400024   941.140015   948.229980   948.229980   \n",
      "75%    1125.349976  1131.750000  1120.369995  1126.500000  1126.500000   \n",
      "max    1634.010010  1638.099976  1603.439941  1609.079956  1609.079956   \n",
      "\n",
      "             Volume  \n",
      "count  5.030000e+02  \n",
      "mean   3.918924e+06  \n",
      "std    2.069197e+06  \n",
      "min    1.458800e+06  \n",
      "25%    2.655050e+06  \n",
      "50%    3.324800e+06  \n",
      "75%    4.469000e+06  \n",
      "max    1.656500e+07   \n",
      "\n",
      "The data ranges from 2016-05-16 to 2018-05-14\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def analyse(dataframe):\n",
    "    \"\"\"Runs an exploration analysis of the dataframe.\"\"\"\n",
    "    print(\"Shape\", dataframe.shape, \"\\n\")\n",
    "    print(\"Columns\", dataframe.columns, \"\\n\")\n",
    "    dataframe.info()\n",
    "    print(\"\\n\", dataframe.describe(), \"\\n\")\n",
    "    print(\"The data ranges from\", dataframe[\"Date\"].min(), \"to\", dataframe[\"Date\"].max())\n",
    "    dataframe.plot(x=\"Date\", y=[\"Close\", \"Open\", \"High\", \"Low\"])\n",
    "# read validation and test sets and then analyse them\n",
    "df_val = pd.read_csv(\"AMZN_val.csv\")\n",
    "analyse(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f20604e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape (504, 7) \n",
      "\n",
      "Columns Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object') \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 504 entries, 0 to 503\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       504 non-null    object \n",
      " 1   Open       504 non-null    float64\n",
      " 2   High       504 non-null    float64\n",
      " 3   Low        504 non-null    float64\n",
      " 4   Close      504 non-null    float64\n",
      " 5   Adj Close  504 non-null    float64\n",
      " 6   Volume     504 non-null    int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 27.7+ KB\n",
      "\n",
      "               Open         High          Low        Close    Adj Close  \\\n",
      "count   504.000000   504.000000   504.000000   504.000000   504.000000   \n",
      "mean   1823.927758  1843.069246  1803.067065  1824.040536  1824.040536   \n",
      "std     178.129809   179.294163   177.457741   178.470078   178.470078   \n",
      "min    1346.000000  1396.030029  1307.000000  1343.959961  1343.959961   \n",
      "25%    1712.924988  1730.602447  1691.637543  1713.642517  1713.642517   \n",
      "50%    1806.539978  1824.244995  1791.289978  1806.119995  1806.119995   \n",
      "75%    1908.154968  1921.580048  1887.580017  1902.842468  1902.842468   \n",
      "max    2443.199951  2475.000000  2396.010010  2474.000000  2474.000000   \n",
      "\n",
      "             Volume  \n",
      "count  5.040000e+02  \n",
      "mean   4.705042e+06  \n",
      "std    2.288185e+06  \n",
      "min    8.813000e+05  \n",
      "25%    3.078725e+06  \n",
      "50%    4.068450e+06  \n",
      "75%    5.763300e+06  \n",
      "max    1.556730e+07   \n",
      "\n",
      "The data ranges from 2018-05-15 to 2020-05-14\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"AMZN_test.csv\")\n",
    "analyse(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a6dfd0",
   "metadata": {},
   "source": [
    "Once we have done our exploration of the data, we can move on to the predictive modeling part of the task. The task was to predict if the next day's closing price will be higher than the opening price. We do not have that information explicitly in our data, so we have to infer it.\n",
    "\n",
    "This is relatively simple, we just need to compare the closing and opening prices one day in advance.\n",
    "\n",
    "To achieve that, first, we will make sure that the data is sorted by the date. We can use the sort_values method and pass in the Date column as a parameter, sorting it in ascending order.\n",
    "\n",
    "Next, we need to shift the DataFrame by one row / one day and compare the prices. Pandas has a method for doing exactly that, the shift method. We specify a period of minus one (so that we shift the data from the next day back). Because it is a logical operation, Pandas would return a True / False result for each comparison. We want this to be presented as 1 / 0 for the machine learning models, so we will map it to type int. To store all of this information, we will create a new column, called Target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3f48055",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1997-05-15</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.927083</td>\n",
       "      <td>1.958333</td>\n",
       "      <td>1.958333</td>\n",
       "      <td>72156000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1997-05-16</td>\n",
       "      <td>1.968750</td>\n",
       "      <td>1.979167</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>14700000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1997-05-19</td>\n",
       "      <td>1.760417</td>\n",
       "      <td>1.770833</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>1.708333</td>\n",
       "      <td>6106800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1997-05-20</td>\n",
       "      <td>1.729167</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>5467200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1997-05-21</td>\n",
       "      <td>1.635417</td>\n",
       "      <td>1.645833</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.427083</td>\n",
       "      <td>1.427083</td>\n",
       "      <td>18853200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4776</th>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>673.950012</td>\n",
       "      <td>686.979980</td>\n",
       "      <td>671.409973</td>\n",
       "      <td>679.750000</td>\n",
       "      <td>679.750000</td>\n",
       "      <td>3982200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4777</th>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>694.000000</td>\n",
       "      <td>704.549988</td>\n",
       "      <td>693.500000</td>\n",
       "      <td>703.070007</td>\n",
       "      <td>703.070007</td>\n",
       "      <td>6105600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4778</th>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>705.789978</td>\n",
       "      <td>719.000000</td>\n",
       "      <td>701.650024</td>\n",
       "      <td>713.229980</td>\n",
       "      <td>713.229980</td>\n",
       "      <td>7338200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4779</th>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>717.380005</td>\n",
       "      <td>722.450012</td>\n",
       "      <td>711.510010</td>\n",
       "      <td>717.929993</td>\n",
       "      <td>717.929993</td>\n",
       "      <td>5048200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4780</th>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>714.640015</td>\n",
       "      <td>719.250000</td>\n",
       "      <td>706.510010</td>\n",
       "      <td>709.919983</td>\n",
       "      <td>709.919983</td>\n",
       "      <td>4763400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4781 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     1997-05-15    2.437500    2.500000    1.927083    1.958333    1.958333   \n",
       "1     1997-05-16    1.968750    1.979167    1.708333    1.729167    1.729167   \n",
       "2     1997-05-19    1.760417    1.770833    1.625000    1.708333    1.708333   \n",
       "3     1997-05-20    1.729167    1.750000    1.635417    1.635417    1.635417   \n",
       "4     1997-05-21    1.635417    1.645833    1.375000    1.427083    1.427083   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "4776  2016-05-09  673.950012  686.979980  671.409973  679.750000  679.750000   \n",
       "4777  2016-05-10  694.000000  704.549988  693.500000  703.070007  703.070007   \n",
       "4778  2016-05-11  705.789978  719.000000  701.650024  713.229980  713.229980   \n",
       "4779  2016-05-12  717.380005  722.450012  711.510010  717.929993  717.929993   \n",
       "4780  2016-05-13  714.640015  719.250000  706.510010  709.919983  709.919983   \n",
       "\n",
       "        Volume  Target  \n",
       "0     72156000       0  \n",
       "1     14700000       0  \n",
       "2      6106800       0  \n",
       "3      5467200       0  \n",
       "4     18853200       0  \n",
       "...        ...     ...  \n",
       "4776   3982200       1  \n",
       "4777   6105600       1  \n",
       "4778   7338200       1  \n",
       "4779   5048200       0  \n",
       "4780   4763400       0  \n",
       "\n",
       "[4781 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sure that our data is sorted by date\n",
    "df_train.sort_values(by=\"Date\", inplace=True)\n",
    "df_val.sort_values(by=\"Date\", inplace=True)\n",
    "df_test.sort_values(by=\"Date\", inplace=True)\n",
    "# notice that we shift by a period of '-1', this takes the next day's price direction for the current day\n",
    "# a positive period will take the days from the past\n",
    "df_train[\"Target\"] = (df_train[\"Close\"] > df_train[\"Open\"]).shift(periods=-1, fill_value=0).astype(int)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31481aa",
   "metadata": {},
   "source": [
    "Running the value_counts method on the Target column gives us the distribution. We have 2292 days where the closing price is higher than the opening and 2389 where it is lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d1dfe986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2392\n",
       "0    2389\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Target\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c94a41",
   "metadata": {},
   "source": [
    "We are going to repeat the same procedure for the validation as well as the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d32c014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    255\n",
       "1    248\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val[\"Target\"] = (df_val[\"Close\"] > df_val[\"Open\"]).shift(periods=-1, fill_value=0).astype(int)\n",
    "df_val[\"Target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83747c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    255\n",
       "0    249\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[\"Target\"] = (df_test[\"Close\"] > df_test[\"Open\"]).shift(periods=-1, fill_value=0).astype(int)\n",
    "df_test[\"Target\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7151d187",
   "metadata": {},
   "source": [
    "At this point, we can start building some machine learning models to predict the target variable. But, before we do that, it might be useful to engineer some additional features that may help us better predict the price direction. In the next section, we will explore some possibilities for doing feature engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788017af",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9465d64",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We know that the stock prices are time-dependent and that the next day's price depends on prices (and many other things) from previous days.\n",
    "\n",
    "We want to somehow take into account all the values in the last n days, capturing the trend, or the magnitude of price change.\n",
    "\n",
    "A simple solution would be to calculate a moving average. With Pandas, we can use the rolling method to calculate moving averages. It provides us with an interface for sliding (in Pandas terminology - rolling) window calculations. The following cells calculate the 3- and 7-days moving average, and add them as a feature into the data set.\n",
    "\n",
    "Remember that we have our data sorted from before. If the data is not sorted by the date, the results from the rolling() method would be invalid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d61a686",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train[\"Moving_Average_3\"] = (df_train[\"Close\"] - df_train[\"Open\"]).rolling(window=3, min_periods=1).mean()\n",
    "df_val[\"Moving_Average_3\"] = (df_val[\"Close\"] - df_val[\"Open\"]).rolling(window=3, min_periods=1).mean()\n",
    "df_test[\"Moving_Average_3\"] = (df_test[\"Close\"] - df_test[\"Open\"]).rolling(window=3, min_periods=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a3b9ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train[\"Moving_Average_7\"] = (df_train[\"Close\"] - df_train[\"Open\"]).rolling(window=7, min_periods=1).mean()\n",
    "df_val[\"Moving_Average_7\"] = (df_val[\"Close\"] - df_val[\"Open\"]).rolling(window=7, min_periods=1).mean()\n",
    "df_test[\"Moving_Average_7\"] = (df_test[\"Close\"] - df_test[\"Open\"]).rolling(window=7, min_periods=1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96424ff",
   "metadata": {},
   "source": [
    "Feature engineering can be simpler, we can just take the current day's price direction or the price range. Anything that comes to mind, that can be considered to be useful, should be tested and tried."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5669facb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current price direction\n",
    "df_train[\"Today_Direction\"] = df_train[\"Close\"] - df_train[\"Open\"]\n",
    "df_val[\"Today_Direction\"] = df_val[\"Close\"] - df_val[\"Open\"]\n",
    "df_test[\"Today_Direction\"] = df_test[\"Close\"] - df_test[\"Open\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32cea8e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# price range\n",
    "df_train[\"Price_Range\"] = df_train[\"High\"] - df_train[\"Low\"]\n",
    "df_val[\"Price_Range\"] = df_val[\"High\"] - df_val[\"Low\"]\n",
    "df_test[\"Price_Range\"] = df_test[\"High\"] - df_test[\"Low\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ef79dc",
   "metadata": {},
   "source": [
    "The next cell displays a sample of the data with the new features included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f090a582",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Target</th>\n",
       "      <th>Moving_Average_3</th>\n",
       "      <th>Moving_Average_7</th>\n",
       "      <th>Today_Direction</th>\n",
       "      <th>Price_Range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>2008-11-14</td>\n",
       "      <td>43.610001</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>41.750000</td>\n",
       "      <td>41.750000</td>\n",
       "      <td>11949700</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.253335</td>\n",
       "      <td>-0.601429</td>\n",
       "      <td>-1.860001</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4430</th>\n",
       "      <td>2014-12-22</td>\n",
       "      <td>301.940002</td>\n",
       "      <td>307.359985</td>\n",
       "      <td>301.940002</td>\n",
       "      <td>306.540009</td>\n",
       "      <td>306.540009</td>\n",
       "      <td>4003800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.436666</td>\n",
       "      <td>-0.705710</td>\n",
       "      <td>4.600007</td>\n",
       "      <td>5.419983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3618</th>\n",
       "      <td>2011-09-29</td>\n",
       "      <td>234.169998</td>\n",
       "      <td>234.300003</td>\n",
       "      <td>216.289993</td>\n",
       "      <td>222.440002</td>\n",
       "      <td>222.440002</td>\n",
       "      <td>9378500</td>\n",
       "      <td>0</td>\n",
       "      <td>-6.126663</td>\n",
       "      <td>-2.434283</td>\n",
       "      <td>-11.729996</td>\n",
       "      <td>18.010010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>2000-05-24</td>\n",
       "      <td>46.437500</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>40.437500</td>\n",
       "      <td>48.562500</td>\n",
       "      <td>48.562500</td>\n",
       "      <td>11666600</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.937500</td>\n",
       "      <td>-0.580357</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>9.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4392</th>\n",
       "      <td>2014-10-28</td>\n",
       "      <td>289.760010</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>289.760010</td>\n",
       "      <td>295.589996</td>\n",
       "      <td>295.589996</td>\n",
       "      <td>5572600</td>\n",
       "      <td>0</td>\n",
       "      <td>4.253326</td>\n",
       "      <td>2.681423</td>\n",
       "      <td>5.829986</td>\n",
       "      <td>8.239990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4657</th>\n",
       "      <td>2015-11-16</td>\n",
       "      <td>640.919983</td>\n",
       "      <td>649.989990</td>\n",
       "      <td>622.289978</td>\n",
       "      <td>647.809998</td>\n",
       "      <td>647.809998</td>\n",
       "      <td>7435900</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.243347</td>\n",
       "      <td>-0.264299</td>\n",
       "      <td>6.890015</td>\n",
       "      <td>27.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>2013-04-22</td>\n",
       "      <td>259.350006</td>\n",
       "      <td>264.600006</td>\n",
       "      <td>258.029999</td>\n",
       "      <td>263.549988</td>\n",
       "      <td>263.549988</td>\n",
       "      <td>2119100</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.343333</td>\n",
       "      <td>-0.287140</td>\n",
       "      <td>4.199982</td>\n",
       "      <td>6.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>1999-07-29</td>\n",
       "      <td>51.187500</td>\n",
       "      <td>52.187500</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.781250</td>\n",
       "      <td>50.781250</td>\n",
       "      <td>18748000</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.841146</td>\n",
       "      <td>-0.713170</td>\n",
       "      <td>-0.406250</td>\n",
       "      <td>2.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2754</th>\n",
       "      <td>2008-04-28</td>\n",
       "      <td>80.639999</td>\n",
       "      <td>82.500000</td>\n",
       "      <td>80.120003</td>\n",
       "      <td>81.970001</td>\n",
       "      <td>81.970001</td>\n",
       "      <td>10991900</td>\n",
       "      <td>0</td>\n",
       "      <td>1.453336</td>\n",
       "      <td>1.212857</td>\n",
       "      <td>1.330002</td>\n",
       "      <td>2.379997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1997-07-02</td>\n",
       "      <td>1.515625</td>\n",
       "      <td>1.593750</td>\n",
       "      <td>1.510417</td>\n",
       "      <td>1.588542</td>\n",
       "      <td>1.588542</td>\n",
       "      <td>3882000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.026042</td>\n",
       "      <td>0.004464</td>\n",
       "      <td>0.072917</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "2895  2008-11-14   43.610001   44.500000   41.500000   41.750000   41.750000   \n",
       "4430  2014-12-22  301.940002  307.359985  301.940002  306.540009  306.540009   \n",
       "3618  2011-09-29  234.169998  234.300003  216.289993  222.440002  222.440002   \n",
       "763   2000-05-24   46.437500   49.750000   40.437500   48.562500   48.562500   \n",
       "4392  2014-10-28  289.760010  298.000000  289.760010  295.589996  295.589996   \n",
       "4657  2015-11-16  640.919983  649.989990  622.289978  647.809998  647.809998   \n",
       "4008  2013-04-22  259.350006  264.600006  258.029999  263.549988  263.549988   \n",
       "555   1999-07-29   51.187500   52.187500   50.000000   50.781250   50.781250   \n",
       "2754  2008-04-28   80.639999   82.500000   80.120003   81.970001   81.970001   \n",
       "33    1997-07-02    1.515625    1.593750    1.510417    1.588542    1.588542   \n",
       "\n",
       "        Volume  Target  Moving_Average_3  Moving_Average_7  Today_Direction  \\\n",
       "2895  11949700       0         -0.253335         -0.601429        -1.860001   \n",
       "4430   4003800       0          0.436666         -0.705710         4.600007   \n",
       "3618   9378500       0         -6.126663         -2.434283       -11.729996   \n",
       "763   11666600       0         -0.937500         -0.580357         2.125000   \n",
       "4392   5572600       0          4.253326          2.681423         5.829986   \n",
       "4657   7435900       0         -7.243347         -0.264299         6.890015   \n",
       "4008   2119100       1         -0.343333         -0.287140         4.199982   \n",
       "555   18748000       0         -0.841146         -0.713170        -0.406250   \n",
       "2754  10991900       0          1.453336          1.212857         1.330002   \n",
       "33     3882000       1          0.026042          0.004464         0.072917   \n",
       "\n",
       "      Price_Range  \n",
       "2895     3.000000  \n",
       "4430     5.419983  \n",
       "3618    18.010010  \n",
       "763      9.312500  \n",
       "4392     8.239990  \n",
       "4657    27.700012  \n",
       "4008     6.570007  \n",
       "555      2.187500  \n",
       "2754     2.379997  \n",
       "33       0.083333  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.sample(10, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f46eabe7",
   "metadata": {},
   "source": [
    "## Classical Machine Learning Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8474199e",
   "metadata": {},
   "source": [
    "The sklearn library is the most popular library in Python for implementing classical machine learning algorithms. We can use it to try and test a few of them. In the following cells we implement:\n",
    "\n",
    "Logistic regression\n",
    "Decision tree\n",
    "Random forest\n",
    "Gradient boosting ensemble\n",
    "After fitting them to the training data, we are going to evaluate their performance on the validation set by estimating the AUC metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10467d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the target column that we aim to predict\n",
    "y_col = \"Target\"\n",
    "# these are the input features for the models\n",
    "X_cols = [\n",
    "    \"Open\",\n",
    "    \"Close\",\n",
    "    \"High\",\n",
    "    \"Low\",\n",
    "    \"Volume\",\n",
    "    \"Adj Close\",\n",
    "    \"Today_Direction\",\n",
    "    \"Price_Range\",\n",
    "    \"Moving_Average_3\",\n",
    "    \"Moving_Average_7\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d85c2cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train[X_cols]\n",
    "y_train = df_train[y_col]\n",
    "\n",
    "X_val = df_val[X_cols]\n",
    "y_val = df_val[y_col]\n",
    "\n",
    "X_test = df_val[X_cols]\n",
    "y_test = df_val[y_col]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9684da",
   "metadata": {},
   "source": [
    "### LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43d63c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/waijayale/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.0\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fba1a970ac0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for reproducibility\n",
    "RANDOM_SEED = 42\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "# use default parameters\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# fit to train set\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# plot ROC curve, and show AUC for the validation set\n",
    "plot_roc_curve(lr, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6505d4d",
   "metadata": {},
   "source": [
    "Logistic regression does not work well for this problem. Having AUC < 0.5 means that the classifier is worse than just randomly guessing the output. Given that we are dealing with a very difficult problem, any AUC > 0.5 would suffice for this task."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9bdd52",
   "metadata": {},
   "source": [
    "### DECISION TREE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3b60bc3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fba1bd700a0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# remember to use the random seed to be able to reproduce the same results\n",
    "dt = DecisionTreeClassifier(random_state=RANDOM_SEED)\n",
    "dt.fit(X_train, y_train)\n",
    "\n",
    "plot_roc_curve(dt, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec38d77",
   "metadata": {},
   "source": [
    "The decision tree outperforms the logistic regression model by 0.02, and its AUC is above 0.5!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607f31de",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e5e93de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fba1c97bbe0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xNpcnz+/PPh0bZzqSKhhLtCRJKlNGzFrGL5/8jiqVKjHoor3o0ap+piOpArJES5KkMuOjCQu59LkRNK9Xg6d/OYC2jWplOpIqKEu0JEkqE14cNpvrXh1Dtxb1eOL8fjSuUz3TkVSBWaIlSVKplp8feeDjKdz9wST269SYh37Rl9rVrTDKLP8ESpKkUmvZ6g1c/dL3/HfCIk7coxW3n9yLalXSea04KTWWaEmSVCoNn7mMK54fweJVG/jzcd05Z+CuXkRFpYYlWpIklSoxRh79fDq3vzeBFg1q8PIlA+nVukGmY0k/YomWJEmlxvI1ifGND8cv4ojuzbn9lF7Ur1k107Gkn7BES5KkUmHErGVc8fxIFq1cx03HduPcvds5vqFSyxItSZIyKsbIY19M57Z3J9C8fg1evnhverdpkOlY0nZZoiVJUsYkxjdG8+H4hRzevRl3nNLb8Q2VCZZoSZKUESNnLePy5PjGjcd04/x9HN9Q2WGJliRJJarg+EazejV46eK96eP4hsoYS7QkSSoxK9bkcvXL3/PBuIUc1q0Zd53Sm/q1HN9Q2WOJliRJJWLU7OVc9twIFv6wjhuO6cYvHd9QGWaJliRJaRVj5PEvZ3Dbu+NpWrcGL108kD3a7pLpWNJOsURLkqS0WL8xj3fHLOCpr2YwctZyDu3ajLtO7UWDWtUyHU3aaZZoSZJUrOYuX8vz38xk8HezWbxqA+0a1eLWE3twZv+2jm+o3LBES5KknRZj5MspS3j6qxl8OH4hETikS1POHtiO/To2plIly7PKF0u0JEnaYSvW5vLK8Dk8+81MpuWsZpdaVblo/904a0Bb2jSslel4UtpYoiVJUpGNn/8DT381k9dHzmVtbh592jTgntN6c1TPFtSoWjnT8aS0s0RLkqSUbNiYz3tjF/DMVzP4bsYyqlepxHG9W3LOwHb0bF0/0/GkEmWJliRJ2zV/xVpe+GYWz387m8Wr1tO2YS2uP6oLp/Ztwy61XWlDFZMlWpIk/USMka+mLuHpr2bywfiF5MfIQbs35eyBu3JApya+UVAVniVakiRttnJdLq+OmMszX89kyqJVNKhVlQv3bc9ZA3albSPfKChtYomWJEmsy83jiS9n8ODHU1i5fiO9WtfnzlN6cWzvlr5RUNoKS7QkSRVYjJG3Rs/ntncnMHf5Wg7t2pQrDu5E7zYNMh1NKtUs0ZIkVVAjZi3jlrfGMWLWcrq2qMcdp/Rin46NMx1LKhMs0ZIkVTBzlq3hjvcmMuT7eTSpW507Tu7FyX1bU9k3C0ops0RLklRBrFyXy78+mcqjX0wnAFce3JFfH7AbtatbB6Si8rtGkqQK4N0x87nhjWwWr9rAiXu04prDd6dlg5qZjiWVWZZoSZLKuWe/nskNb2TTq1V9Hju3n28alIqBJVqSpHLsX59M5fb3JnBwl6Y8eNaeLlcnFRNLtCRJ5VCMkTvfn8iDn0zl2N4tuee03lStXCnTsaRywxItSVI5k58f+dOQsTzz9UzO6N+WW07o4cobUjGzREuSVI7k5uXz+5dH89rIufx6/w5ce2QXQrBAS8XNEi1JUjmxLjePK14YyQfjFnLN4btz6YG7WaClNLFES5JUDqxev5FfPT2MoVOXcPPx3TlnYLtMR5LKNUu0JEll3PI1Gzjvie8YM3cF95zWm5P2bJ3pSFK5l1KJDiFUAnoDLYG1wNgY48J0BpMkSds3f8VaPpuUw2NfTGfG4jU8eNaeHN69eaZjSRXCdkt0CGE34P+AQ4HJQA5QA+gcQlgD/Bt4KsaYn+6gkiRVdOs35jFsxjI+nZTDpxNzmLhwJQAt69fg8fP6sW+nxhlOKFUchZ2JvgX4F/DrGGMsuCGE0BQ4EzgbeCo98SRJqthmLlm9uTQPnbqEtbl5VKtciX7td+Hkvl04oHNTOjer4xsIpRK23RIdYzxjO9sWAf8o7kCSJFVkazfk8fW0JXw6KYdPJi5ixpI1ALRtWItTs1pzQOcm7NWhEbWr+7YmKZN2+DswhHBYjPGD4gwjSVJFE2NkyqJVibPNk3L4ZvpSNmzMp0bVSgzs0Ijz9m7Hgbs3pV3j2pmOKqmAnflv7GNA2+IKIklSRZGXH/lw/EI+mbiITyfmMG/FOgA6Na3DOXvtygG7N6Ffu4bUqFo5w0klbUthbywcsq1NQKPijyNJUvm2aOU6fjNoFEOnLqFu9Srs07ExVxzShP07N6FVg5qZjicpRYWdid4P+AWwaov7A9A/LYkkSSqnhk5ZzJWDRrFqfS63ndSTk/u2pmrlSpmOJWkHFFaivwbWxBg/3XJDCGFieiJJklS+5OVH/vnRFO797yTaN67NcxcOYPfmdTMdS9JOKGx1jiO3s23/4o8jSVL5krNyPb8ZPJIvpyzhxD1accsJPVxZQyoH/C6WJClNhk5dzFWDRvHD2lzuOLkXp2a1dj1nqZywREuSVMzy8iMPfDyFf3yYGN945oL+dGleL9OxJBUjS7QkScVo8ar1/HbwKD6fvNjxDakc87takqRi8tXUJVw1aCQr1uZy+8k9OS2rjeMbUjmV8ro6IYSbtndbkqSKKj8/cv9/J3PWo19Tp3oVXr9sH07v19YCLZVjRTkTPbyQ25IkVTgFxzeO79OSW0/sSR3HN6RyL+Xv8hjjm9u7LUlSRfPNtCVcOWgky9ckLp5yej/HN6SKorDLft8PxG1tjzFeWeyJJEkq5fLzI//6dCp3/2ci7RrV5snz+9O1hatvSBVJYWeih5VICkmSyogVa3O58oWRfDoph+N6t+SvJzm+IVVEhV2x8KmCt0MItWOMq9MbSZKk0mnmktX88snvmLV0Dbee2IMz+/vmQamiSml1jhDCwBDCOGB88nbvEMKDaU0mSVIp8u30pZzwwJcsWb2BZy8YwFkDdrVASxVYqkvc/QM4HFgCEGP8Htg/TZkkSSpVXhk+h7Me/Zpdalfj9Uv3YUCHRpmOJCnDirI6x+wt/sedV/xxJEkqPfLzI3d/MJEHPp7KPh0b8eCZfalfq2qmY0kqBVIt0bNDCHsDMYRQDbiS5GiHJEnl0doNefzuxVG8m72AM/q35ebju1O1csrXKJNUzqVaoi8G7gVaAXOB94HL0hVKkqRMWvTDOi58ehhj5q7gj0d35YJ92zv/LOlHUirRMcbFwFlpziJJUsaNnbeCC58axoq1uTxydhaHdmuW6UiSSqFUV+foEEJ4M4SQE0JYFEJ4I4TQId3hJEkqSR+MW8ipD30FwMsX722BlrRNqQ53PQ+8CLQAWgIvAS+kK5QkSSUpxsgjn03jomeG0alpHd64bB+6tfQKhJK2LdUSHWKMz8QYNyZ/Pct2LgcuSVJZkZuXz/WvjeHWd8ZzZI/mDLpoIE3r1ch0LEml3HZnokMIDZMffhxCuBYYRKI8nw68neZskiSl1Yo1uVzy3HCGTl3CFQd35LeHdqZSJd9AKKlwhb2xcDiJ0rzpb5RfF9gWgb+kI5QkSek2ffFqLnjyO+YsW8s9p/XmpD1bZzqSpDJkuyU6xti+pIJIklRSvp62hIufHU4Anr1wAP3bNyz0MZJUUMpXLAwh9AC6AZsHxWKMT6cjlCRJ6fLisNn84bUxtG1Yi8fP68eujWpnOpKkMiilEh1C+BNwIIkS/Q5wJPAFYImWJJUJ+fmR29+fwL8/ncZ+nRrzzzP3pH5NL+Etacekeib6FKA3MDLGeH4IoRnwaPpiSZJUfFasyeWal7/nP+MWctaAttx0nJfwlrRzUi3Ra2OM+SGEjSGEesAiwIutSJJKvY8nLuLaV0azZNUGbjymG+fv085LeEvaaamW6GEhhAbAIyRW7FgFfJuuUJIk7ayV63K55a3xDB42m87N6vDYuf3o0ap+pmNJKidSKtExxkuTHz4UQngPqBdjHJ2+WJIk7bgvpyzm9y+PZv6KtVxy4G785tBOVK9SOdOxJJUjhV1sZc/tbYsxjij+SJIk7Zg1GzZy27sTePqrmXRoUpuXL9mbPdvukulYksqhws5E372dbRE4eHsPDiEcAdwLVAYejTHetpV9DgT+AVQFFscYDygkkyRJP/HdjKVc/dL3zFq6hgv2bc81h+9OjaqefZaUHoVdbOWgHX3iEEJl4AHgMGAO8F0IYUiMcVyBfRoADwJHxBhnhRCa7ujrSZIqpnW5edz1/kQe+3I6bXapxaBf7cWADo0yHUtSOZfyxVZ2QH9gSoxxGkAIYRBwPDCuwD5nAq/GGGcBxBgXpTGPJKmcGTlrGf/vpe+ZlrOas/falWuP7ELt6un8p02SEtL5N00rYHaB23OAAVvs0xmoGkL4BKgL3OtVECVJhVm/MY97P5zMQ59OpXm9Gjx7wQD27dQ407EkVSDpLNFbW4QzbuX1+wKHADWBr0IIX8cYJ/3oiUK4CLgIoG3btmmIKkkqK7LnruDql75nwoKVnJbVmj8e0416NbzyoKSSleplvwNwFtAhxnhzCKEt0DzGuL21oucAbQrcbg3M28o+i2OMq4HVIYTPSFwZ8UclOsb4MPAwQFZW1pZFXJJUAeTm5fPAx1P450dTaFi7Go+fl8XBXZplOpakCirVa54+CAwEzkjeXkniTYPb8x3QKYTQPoRQDfg5MGSLfd4A9gshVAkh1CIx7jE+xUySpApi4oKVnPjgl/zjw8kc06sF//nt/hZoSRmV6jjHgBjjniGEkQAxxmXJYrxNMcaNIYTLgfdJLHH3eIxxbAjh4uT2h2KM45MXbxkN5JNYBi97hz8bSVK5sjEvn4c/n8Y/PphM3RpVeOgXfTmiR/NMx5KklEt0bnLJuggQQmhCovRuV4zxHeCdLe57aIvbdwJ3pphDklRBTM1Zxf978XtGzV7OkT2ac8sJPWhUp3qmY0kSkHqJvg94DWgaQrgVOAX4Y9pSSZIqrPz8yONfTufO9ydSs1pl7jtjD47t1YLE23MkqXRIqUTHGJ8LIQwnsYpGAE6IMTq7LEkqVjOXrOaal0bz7YylHNKlKX87qSdN69XIdCxJ+olUV+e4FxgcYyzszYSSJBXZxrx8nv92Fre9O4HKIXDnKb04pW9rzz5LKrVSHecYAfwxhNCZxFjH4BjjsPTFkiRVBDFGPhy/iNvfm8CURavYr1Njbj+5Fy0b1Mx0NEnarlTHOZ4CngohNAROBm4PIbSNMXZKazpJUrk1ctYy/vbOBL6dsZQOjWvz77P78rNuzTz7LKlMKOoVCzsCXYB2wLhiTyNJKvdmLF7Nne9P5O0x82lcpxq3nNCD0/u1oWrlVC9dIEmZl+pM9O3AScBU4EXgLzHG5WnMJUkqZ5asWs/9H03h2a9nUq1KJa46pBO/2r8DdaoX9XyOJGVeqn9zTQcGxhgXpzOMJKn8Wbshj8e/nM6/PpnK2tw8Tu/Xht8c0slVNySVadst0SGELjHGCcC3QNsQQtuC22OMI9IZTpJUduXlR14ZPoe7P5jIwh/W87Nuzfj9EV3o2LROpqNJ0k4r7Ez074CLgLu3si0CBxd7IklSmRZj5OOJi7jt3QlMWriKPdo24J9n7km/dg0zHU2Sis12S3SM8aLkh0fGGNcV3BZC8OdwkqQfGT1nOX99ZzxfT1tKu0a1+NdZe3JEj+auuCGp3El1JnoosGcK90mSKqBZS9Zw538m8ub382hUuxo3H9+dM/q3dcUNSeVWYTPRzYFWQM0Qwh4kLvkNUA+oleZskqRSbtnqDdz/0RSe+XoGVSpV4oqDO3LR/h2oW6NqpqNJUloVdib6cOA8oDVwT4H7VwLXpymTJKmUW5f7vxU3Vq/fmFhx49DONHPFDUkVRGEz0ZuuVHhyjPGVEsokSSql8vIjr46Ywz0fTGL+inUc2rUp/3dEFzo1q5vpaJJUogob5/hFjPFZoF0I4Xdbbo8x3rOVh0mSypkYI59OyuG2dycwYcFKereuz99P78NeHRplOpokZURh4xy1k7+7qKckVVDZc1fwt3fH8+WUJezaqBYPnLknR/V0xQ1JFVth4xz/Tv7+55KJI0kqLcbP/4H7/juZd7MX0LB2NW46thtnDtiValVccUOSUlriLoRwB3ALsBZ4D+gN/CY56iFJKkcKlue61atw5cEduXD/DtRzxQ1J2izVdaJ/FmP8fQjhRGAOcCrwMWCJlqRyYsKCRHl+Z0yyPB/SiQv2aU/9WpZnSdpSqiV609+gRwEvxBiXOgsnSeVDwfJcJ3nm+Zf7tqdBrWqZjiZJpVaqJfrNEMIEEuMcl4YQmgDrCnmMJKkUm7hgJff9dzJvj5lveZakIkqpRMcYrw0h3A78EGPMCyGsBo5PbzRJUjpsWZ6vOLgjF1ieJalIUn1jYVXgbGD/5BjHp8BDacwlSSpmExes5L6PJvPOmPnUrmZ5lqSdkeo4x79IzEU/mLx9dvK+C9MRSpJUfCYtXMm9//1feb78IMuzJO2sVEt0vxhj7wK3PwohfJ+OQJKk4lGwPNeqWpnLDkyU511qW54laWelWqLzQgi7xRinAoQQOgB56YslSdpRkxb+b+bZ8ixJ6ZFqib4G+DiEMA0IwK7A+WlLJUkqsmk5q7jng0mby/OlB+7Ghft2sDxLUhoUWqKTy9mtAPoDTUmU6AkxxvVpziZJStHLw+dww+vZVApYniWpBGy3RIcQLgT+CkwF2gMXxRiHlEQwSVLh1m7I44Y3snl5+Bz26tCQ+36+B03r1ch0LEkq9wo7E/0boHuMMSc5B/0cYImWpFJg8sKVXPb8CCYvWsWVh3TiqkM6UbmSV5OVpJJQWIneEGPMAYgxTgshVC+BTJKkQrwyfA5/fD2bWtUq8/Qv+7NfpyaZjiRJFUphJbp1COG+bd2OMV6ZnliSpK1ZuyGPPw3J5sVhcxjQviH3nbEHzRzfkKQSV1iJvmaL28PTFUSStH1TFq3k0ucS4xtXHNyRqw7pRJXKlTIdS5IqpO2W6BjjUyUVRJK0ba+OmMMfXkuMbzx1fn/27+z4hiRlUmGrczwM3BdjzN7KttrA6cD6GONzaconSRXa2g153DRkLIOHzaZ/+4bc7/iGJJUKhY1zPAjcGELoCWQDOUANoBNQD3icxIodkqRiNmXRKi57bgQTF67k8oM68ptDHd+QpNKisHGOUcBpIYQ6QBbQAlgLjI8xTkx/PEmqmF4fOZfrXxtDjaqVeeqX/TnA8Q1JKlVSuux3jHEV8El6o0iS1uUmxjcGfTeb/u0Sq280r+/4hiSVNimVaElS+k1ZtIrLnx/BhAUrufTA3fjdYZ0d35CkUsoSLUmlwBuj5nLdq2OoXqUST57fjwN3b5rpSJKk7ShSiQ4h1I4xrk5XGEmqaNbl5vHnN8fywrez6dduF+47Yw9a1K+Z6ViSpEKk9HPCEMLeIYRxwPjk7d4hhAfTmkySyrmpOas44YEveeHb2Vxy4G688Ku9LNCSVEakeib678DhwBCAGOP3IYT905ZKksqxGCNDvp/H9a+OoVqVSjxxfj8OcnxDksqUlMc5YoyzQwgF78or/jiSVH6tXr+R10fN5ZmvZjJhwUr67roL95+xBy0bePZZksqaVEv07BDC3kAMIVQDriQ52iFJ2r4pi1bx7NczeWX4HFau30jXFvX420k9OaVva6q6+oYklUmpluiLgXuBVsAc4D/ApekKJUll3ca8fD4cv4hnvp7Bl1OWULVy4KieLThn4K7s2XYXtvjJniSpjEm1RO8eYzyr4B0hhH2AL4s/kiSVXTkr1zP4u1k8/80s5q1YR4v6Nbj6Z505vV9bmtStnul4kqRikmqJvh/YM4X7JKnCiTEyfOYynvl6Ju+MmU9uXmTfjo258djuHNq1qRdMkaRyaLslOoQwENgbaBJC+F2BTfWAyukMJkml3ZoNG3lj1Dye+Wom4+b/QN3qVThrwK78Yq9d6di0TqbjSZLSqLAz0dWAOsn96ha4/wfglHSFkqTSbFrOKp79ehYvDZ/NynUb6dK8Lree2IMT+rSidnUvBCtJFcF2/7aPMX4KfBpCeDLGOLOEMklSqZOXH/nv+IU88/VMPp+8mCqVAkf2bMHZe+1Kv3a+UVCSKppUT5msCSHcCXQHamy6M8Z4cFpSSVIpsWz1Bp7/NvFGwbnL19K8Xg1+d1hnft6/DU3r1ij8CSRJ5VKqJfo5YDBwDInl7s4FctIVSpIyLcbIayPn8pe3xrFsTS4DOzTij0d35dBuzVzbWZKUcoluFGN8LIRwVYERj0/TGUySMmXOsjX84bVsPp2Uwx5tG/DcCT3p1rJepmNJkkqRVEt0bvL3+SGEo4F5QOv0RJKkzMjLjzz91QzufH8iADcd242zB7ajciXnnSVJP5Zqib4lhFAf+H8k1oeuB/wmXaEkqaRNWriS/3tlNCNnLeeAzk249cQetN6lVqZjSZJKqZRKdIzxreSHK4CDYPMVCyWpTFu/MY8HP57Kg59MoU71Kvz99N6c0KeVq21IkrarsIutVAZOA1oB78UYs0MIxwDXAzWBPdIfUZLSY8SsZfzfy6OZvGgVx/dpyY3HdKNRHS/NLUkqXGFnoh8D2gDfAveFEGYCA4FrY4yvpzmbJKXF6vUbufP9iTz11Qxa1KvB4+dlcXCXZpmOJUkqQwor0VlArxhjfgihBrAY6BhjXJD+aJJU/D6dlMP1r45h3oq1nL3Xrvz+iC7U8SqDkqQiKuxfjg0xxnyAGOO6EMIkC7Sksmjp6g3c8tY4Xh05l92a1OalXw8kq13DTMeSJJVRhZXoLiGE0cmPA7Bb8nYAYoyxV1rTSdJOijEy5Pt53PzmOFaszeXKgzty6UEdqVG1cqajSZLKsMJKdNcSSSFJaTB3+VpufD2b/05YRO/W9XnuVwPo0tyLpkiSdt52S3SMcWZJBZGk4rJ+Yx6Pfj6d+z+aTCDwx6O7cv4+7b1oiiSp2PhuGknlyqeTcrhpyFimL17NEd2bc8Ox3WjVoGamY0mSyhlLtKRyYe7ytdzy1jjezV5A+8a1eeqX/Tmgc5NMx5IklVMpl+gQQk2gbYxxYhrzSFKRbBrd+OdHU4hErjl8dy7crz3Vq/jGQUlS+qRUokMIxwJ3AdWA9iGEPsDNMcbj0phNkrbrs+ToxrTFqzm8ezNuOKYbrXeplelYkqQKINUz0TcB/YFPAGKMo0II7dITSZK2b97ytdzy9jjeGbOAdo1q8cT5/Tho96aZjiVJqkBSLdEbY4wrQvCd7ZIyZ8PGfB79Yhr3/zcxunH1zzpz4X4dXPNZklTiUi3R2SGEM4HKIYROwJXA0PTFkqQf+2LyYm4cks20nNX8rFtidKNNQ0c3JEmZkWqJvgL4A7AeeB54H7glXaEkaZP5K9Zyy1vjeXvMfHZtVIsnzuvHQV0c3ZAkZVaqJXr3GOMfSBRpSUq7DRvzefzL6dz338nk5Ud+d1hnLtrf0Q1JUumQaom+J4TQAngJGBRjHJvGTJIquC+nLObGN7KZmrOaw7o140ZHNyRJpUxKJTrGeFAIoTlwGvBwCKEeMDjG6EiHpGIzf8Vabnl7PG+Pnk/bhrV4/LwsDu7SLNOxJEn6iZQvthJjXADcF0L4GPg9cCPORUsqBhs25vPEl9O5Nzm68dtDO/PrAxzdkCSVXqlebKUrcDpwCrAEGAT8vzTmklRBDJ2ymBuHjGXKolUc2rUZfzrW0Q1JUumX6pnoJ4AXgJ/FGOelMY+kCuSdMfO59LkRtGlYk8fOzeKQro5uSJLKhlRnovdKdxBJFcviVev54+vZ9Gpdnxd/PdDRDUlSmVJpextDCC8mfx8TQhhd4NeYEMLowp48hHBECGFiCGFKCOHa7ezXL4SQF0I4peifgqSy6E9vjGXVuo3cdWpvC7Qkqcwp7Ez0VcnfjynqE4cQKgMPAIcBc4DvQghDYozjtrLf7SQu4CKpAnh79HzeHjOfaw7fnc7N6mY6jiRJRbbdM9ExxvnJDy+NMc4s+Au4tJDn7g9MiTFOizFuIPFmxOO3st8VwCvAoiJml1QGLVm1nhveyKZnq/r8ev8OmY4jSdIO2W6JLuCwrdx3ZCGPaQXMLnB7TvK+zUIIrYATgYdSzCGpjLtxyP/GOKpUTvWvIEmSSpftjnOEEC4hcca5wxYz0HWBLwt57rCV++IWt/8B/F+MMS+Ere2+OcdFwEUAbdu2LeRlJZVW74yZz9ujE2Mcuzd3jEOSVHYVNhP9PPAu8Deg4BsDV8YYlxby2DlAmwK3WwNbLo+XBQxKFujGwFEhhI0xxtcL7hRjfBh4GCArK2vLIi6pDFiyaj03vO4YhySpfCisRMcY44wQwmVbbgghNCykSH8HdAohtAfmAj8HztziydsXeL4ngbe2LNCSyocbh4zlh3W5PHfqAMc4JEllXipnoo8BhpMYxSg4cxGBbZ5OijFuDCFcTmLVjcrA4zHGsSGEi5PbnYOWKohNYxxX/6wzXZrXy3QcSZJ2WoixbE1HZGVlxWHDhmU6hqQULV29gcPu+ZQWDWrw2qX7UNWz0JKkMiKEMDzGmLW1bSn9axZC2CeEUDv58S9CCPeEEHyHn6RC3fhGNj+sy+WuU3tboCVJ5Uaq/6L9C1gTQugN/B6YCTyTtlSSyoV3x8znrdHzufLgTo5xSJLKlVRL9MaYmPs4Hrg3xngviWXuJGmrlq7ewA1vZNO9ZT0uPnC3TMeRJKlYFfbGwk1WhhCuA84G9kteqrtq+mJJKsvy8yN/eG0MK9bm8swFAxzjkCSVO6n+y3Y6sB74ZYxxAYkrD96ZtlSSyrTb35/Au9kLuPpnu9O1hWMckqTyJ6USnSzOzwH1QwjHAOtijE+nNZmkMumpoTP496fT+MVebbnIi6pIksqpVFfnOA34FjgVOA34JoRwSjqDSSp73suez01vjuWwbs3483E9SF6NVJKkcifVmeg/AP1ijIsAQghNgA+Bl9MVTFLZMmzGUq4aNIo+bRpw38/3oHIlC7QkqfxKdSa60qYCnbSkCI+VVM5NWbSSC54aRssGNXns3H7UrFY505EkSUqrVM9EvxdCeB94IXn7dOCd9ESSVJYs+mEd5z7+HVUrB546vz8Na1fLdCRJktIupRIdY7wmhHASsC8QgIdjjK+lNZmkUm/lulzOe+I7lq3ZwOCLBtK2Ua1MR5IkqURst0SHEDoBdwG7AWOAq2OMc0simKTSbcPGfC59bgQTF67k0XOz6Nm6fqYjSZJUYgqba34ceAs4GRgO3J/2RJJKvRgj1746ms8nL+ZvJ/XkoN2bZjqSJEklqrBxjroxxkeSH08MIYxIdyBJpd9d/5nIqyPm8rvDOnNaVptMx5EkqcQVVqJrhBD2IDEHDVCz4O0Yo6VaqmCe+XomD3w8lTP6t+GKgztmOo4kSRlRWImeD9xT4PaCArcjcHA6Qkkqnf4zdgF/eiObQ7o05S/HezEVSVLFtd0SHWM8qKSCSCrdhs9cxhUvjKRn6wbcf+YeVKnsUvGSpIrLfwUlFWpazioufOo7mtevwWPnZlGrWqpLzEuSVD5ZoiVt16KV6zj3iW+pFBIXU2lcp3qmI0mSlHGeTpK0Tety8/jlk9+xeOUGXrhoL9o1rp3pSJIklQopnYkOCb8IIdyYvN02hNA/vdEkZdrd/5lI9twfuO+MPejTpkGm40iSVGqkOs7xIDAQOCN5eyXwQFoSSSoVhs9cyqNfTOfMAW05rFuzTMeRJKlUSXWcY0CMcc8QwkiAGOOyEEK1NOaSlEHrcvO45uXRtKxfk+uO7JLpOJIklTqplujcEEJlEmtDE0JoAuSnLZWkjLrng0lMy1nNsxcMoG6NqpmOI0lSqZPqOMd9wGtA0xDCrcAXwF/TlkpSxgyfuYxHP5/GGf3bsm+nxpmOI0lSqZTSmegY43MhhOHAISQu+X1CjHF8WpNJKnGJMY7vaVG/Jtcf5RiHJEnbklKJDiG0BdYAbxa8L8Y4K13BJJW8vyfHOJ65oL9jHJIkbUeqM9Fvk5iHDkANoD0wEeieplySStiIWct45PNpnNG/Dft1apLpOJIklWqpjnP0LHg7hLAn8Ou0JJJU4tbl5nHNS9/TvF4Nrj+qa6bjSJJU6u3QZb9jjCOAfsWcRVKG/P3DSUzNWc1tJ/dyjEOSpBSkOhP9uwI3KwF7AjlpSSSpRH05ZTGPfDaNn/drw/6dHeOQJCkVqc5E1y3w8UYSM9KvFH8cSSVp+Myl/OrpYXRqWpc/HO0YhyRJqSq0RCcvslInxnhNCeSRVEKy567gvCe+o1m9GjxzoatxSJJUFNudiQ4hVIkx5pEY35BUTkxeuJJzHv+WejWq8uyFA2hat0amI0mSVKYUdib6WxIFelQIYQjwErB608YY46tpzCYpDWYuWc1Zj35D5UqB5y4cQKsGNTMdSZKkMifVmeiGwBLgYP63XnQELNFSGTJ/xVrOfOQbcvPyGXTRQNo1rp3pSJIklUmFleimyZU5svlfed4kpi2VpGKXs3I9Zz3yDT+szeX5X+3F7s3rFv4gSZK0VYWV6MpAHX5cnjexREtlxPI1Gzj7sW+Yt2Itz1wwgJ6t62c6kiRJZVphJXp+jPHmEkkiKS1Wrd/IuU98x7Sc1Tx2Xhb92jXMdCRJksq8wkr01s5ASyoj1m7I44InvyN77gr+ddae7NfJi6lIklQcCrvs9yElkkJSsduwMZ9LnhvOtzOWcs9pvflZ9+aZjiRJUrmx3RIdY1xaUkEkFZ+NeflcNWgkn0zM4W8n9uT4Pq0yHUmSpHKlsDPRksqYvPzI718ZzbvZC7jhmG78vH/bTEeSJKncSXWdaEllQM7K9fxm8Ei+nLKE3x3WmQv2bZ/pSJIklUuWaKmcGDp1MVcNGsXKdbnccXIvTuvXJtORJEkqtyzRUhmXlx/550dTuPe/k2jfuDbPXjDAC6lIkpRmlmipDMtZuZ7fDh7FF1MWc+IerbjlhB7Uru63tSRJ6ea/tlIZ9dXUJVw5aCQ/rM3l9pN7clpWG0JwaXdJkkqCJVoqY/LyIw9+PIW/fziJdo1r88wF/enSvF6mY0mSVKFYoqUyZPGqxPjG55MXc0Kfltx6Yk/HNyRJygD/9ZXKiK+nLeHKF0ayYm0ut53Uk9P7Ob4hSVKmWKKlUi4/P/LgJ1O454NJtGtUm6d+2Z+uLRzfkCQpkyzRUilWcHzjuN4t+etJPanj+IYkSRnnv8ZSKfXNtMTqG8vW5PLXE3tyRn/HNyRJKi0s0VIpk58f+denU7n7PxPZtVFtnjivP91aOr4hSVJpYomWSpElq9bz2xe/57NJORzbuyV/c3xDkqRSyX+dpVLi2+lLueKFESxbk8utJ/bgzP5tHd+QJKmUskRLGZafH3nos6nc/Z9JtNmlJo9f2o/uLetnOpYkSdoOS7SUQUtXb+C3g0fx6aQcju7VgttO6kndGlUzHUuSJBXCEi1lyLAZS7n8+ZEsXb2Bv5zQg18McHxDkqSywhItlbD8/MjDn0/jzvcn0nqXmrx66d70aOX4hiRJZYklWipBS1dv4P+9OIqPJ+ZwdM8W3Hay4xuSJJVFlmiphIydt4ILnxrGklUb+Mvx3fnFXrs6viFJUhlliZZKwPI1G7jo6eHECK9csjc9Wzu+IUlSWWaJltIsPz/yuxe/Z9HKdbx0sQVakqTyoFKmA0jl3UOfTeWjCYv449Hd6NOmQabjSJKkYmCJltLo62lLuOv9iRzdqwXnDNw103EkSVIxsURLaZKzcj1XvDCSdo1qc/vJvXwToSRJ5Ygz0VIa5OVHrho0kh/W5vLMBf2pU91vNUmSyhP/ZZfS4B8fTmLo1CXccUovujSvl+k4kiSpmDnOIRWzTyYu4v6PpnBq39acltUm03EkSVIaWKKlYjRv+Vp+O3gUXZrX5ebje2Q6jiRJShNLtFRMcvPyufz5EeTmRR48a09qVquc6UiSJClNnImWislt705gxKzl/PPMPejQpE6m40iSpDTyTLRUDN7LXsBjX0zn3IG7ckyvlpmOI0mS0swz0dJOyM+PvPDdLG59ezy9W9fn+qO7ZjqSJEkqAZZoaQdNy1nFda+O4ZvpS9l7t0bcc1ofqldxDlqSpIrAEi0VUW5ePo98Po1/fDiZGlUqccfJvTg1q7VXJJQkqQKxREtFkD13Bb9/eTTj5v/AkT2a8+fjutO0Xo1Mx5IkSSXMEi2lYO2GPP7x4SQe/WI6jWpX46Ff9OWIHs0zHUuSJGVIWkt0COEI4F6gMvBojPG2LbafBfxf8uYq4JIY4/fpzCQV1dCpi7nu1THMXLKGM/q34doju1K/ZtVMx5IkSRmUthIdQqgMPAAcBswBvgshDIkxjiuw23TggBjjshDCkcDDwIB0ZZKKYsXaXP72zngGfTebdo1q8fyvBrD3bo0zHUuSJJUC6TwT3R+YEmOcBhBCGAQcD2wu0THGoQX2/xponcY8Usrey57PDW+MZenqDfz6gA789tDO1KjqyhuSJCkhnSW6FTC7wO05bP8s8wXAu2nMIxVq0Q/ruPGNsbw3dgHdWtTjifP60aNV/UzHkiRJpUw6S/TW1vuKW90xhINIlOh9t7H9IuAigLZt2xZXPmmzGCMvDpvNrW+PZ/3GfP7viC5cuF97qlb2op6SJOmn0lmi5wBtCtxuDczbcqcQQi/gUeDIGOOSrT1RjPFhEvPSZGVlbbWISztq1pI1XPvqaIZOXcKA9g257eRetG9cO9OxJElSKZbOEv0d0CmE0B6YC/wcOLPgDiGEtsCrwNkxxklpzCJt1Zvfz+PaV0ZTKQT+emJPft6vDZUqedEUSZK0fWkr0THGjSGEy4H3SSxx93iMcWwI4eLk9oeAG4FGwIPJq71tjDFmpSuTtMm63Dz+8tY4nvtmFn133YX7z9iDlg1qZjqWJEkqI0KMZWs6IisrKw4bNizTMVSGzVi8mkufG8G4+T/w6wM6cPXPdnf2WZIk/UQIYfi2TvB6xUJVKG+Nnse1r4yhSuXA4+dlcXCXZpmOJEmSyiBLtCqEdbl53Pr2eJ75eiZ7tm3A/WfuSSvHNyRJ0g6yRKtcm7JoFe+Mmc9rI+cyffFqfr1/B64+3PENSZK0cyzRKnc2Fed3xsxnwoKVhABZu+7CDcd0dXxDkiQVC0u0yoWpOat4e/RPi/NNx3bjyJ4taFavRqYjSpKkcsQSrTJras4q3hk9n7eTxRmgX7td+NOx3TiyRwua17c4S5Kk9LBEq0zZWnHO2tXiLEmSSpYlWqXetOSoxpbF+cZjunFkz+a0qO8qG5IkqWRZolUqTctJvDnwrdH/K859Lc6SJKmUsESrVJmw4Ad+O/h7xs//AUgU5xuO6cZRFmdJklSKWKJVauTlR655aTQ5K9dxwzHdOLJHc1p6QRRJklQKWaJVarw4bDZj5q7g3p/34fg+rTIdR5IkaZu8bJtKheVrNnDHexPo364hx/Vumek4kiRJ22WJVqlwzweTWLE2l5uO604IIdNxJEmStssSrYwbN+8Hnv16Jr/Ya1e6tayX6TiSJEmFskQro2KM3DRkLA1qVeN3h3XOdBxJkqSUWKKVUUO+n8e3M5ZyzeG706BWtUzHkSRJSoklWhmzav1Gbn17PL1a1+e0rDaZjiNJkpQyl7hTxtz/0WQWrVzPv8/uS+VKvplQkiSVHZ6JVkZMzVnF419M59S+rdmj7S6ZjiNJklQklmiVuE1vJqxRpTK/P6JLpuNIkiQVmSVaJe4/4xby+eTF/PawzjSpWz3TcSRJkorMEq0StS43j7+8NY7Ozepw9sBdMx1HkiRph/jGQpWohz6dypxla3n+VwOoWtn/w0mSpLLJFqMSM3vpGv71yVSO7tWCvXdrnOk4kiRJO8wSrRKxdkMeN7yRTaUQ+MNRXTMdR5Ikaac4zqG0+3RSDn98fQyzl67lxmO60bJBzUxHkiRJ2imWaKXNopXruOWt8Qz5fh4dmtTmhV/txcDdGmU6liRJ0k6zRKvY5edHBn03m9veHc+63Hx+c2gnLjlwN6pXqZzpaJIkScXCEq1iNXHBSq5/bQzDZy5jQPuG3HpiTzo2rZPpWJIkScXKEq1isS43j/s/msy/P51GnRpVuPOUXpzStzUhhExHkyRJKnaWaO20zyfn8IfXspm1dA0n79ma64/qQqM6XolQkiSVX5Zo7bDFq9Zzy1vjeH3UPNo3rs3zFw5g746u/yxJkso/S7SKLD8/8uKw2fzt3Qms2bCRKw/pxKUH7kaNqr5xUJIkVQyWaKVk+ZoNjJv/A+Pm/cB72QsYNnMZ/ds35K8n9qBj07qZjidJklSiLNH6kRgjc5atZey8HzaX5vHzf2Du8rWb92lRvwZ3nJx442ClSr5xUJIkVTyWaAGQlx/54+vZvDV6HivXbQSgUoAOTerQd9ddOHvgrnRrUY+uLerRpK5vGpQkSRWbJVrEGPnzm2N54dtZnNCnJf3aN6Rbi3p0aV6PmtWcc5YkSdqSJVo89Ok0nv5qJhft34Hrj+qa6TiSJEmlXqVMB1BmvTZyDre/N4Fje7fk2iO6ZDqOJElSmWCJrsC+mLyYa14azcAOjbjr1F6+SVCSJClFjnNUQLl5+Xw+OYcrXxhFx6Z1+Pc5falexdlnSZKkVFmiK4h1uXl8MXkx741dwIfjF7J8TS6tGtTkifP7Ua9G1UzHkyRJKlMs0eXY6vUb+WRiDu9mz+fjCYtYvSGPujWqcFjXZhzRozn7d27iVQYlSZJ2gCW6nFmxJpcPxy/k3ewFfDY5hw0b82lcpxrH9WnFET2aM7BDI6pVcRRekiRpZ1iiy4FFK9fxwbiFvJe9gK+mLmFjfqRl/RqcNaAtR3RvTla7hlT2TYOSJEnFxhJdRs1dvpb3shfwfvYCvpu5lBihXaNaXLhfB47s0ZxeresTgsVZkiQpHSzRZdB72fO59LkR5Efo0rwuVx3SiSN7tKBzszoWZ0mSpBJgiS5jlqxaz/WvZdO9ZX3uO2MP2jeunelIkiRJFY7vMCtjbhwyllXrNnL3ab0t0JIkSRliiS5D3hg1l7dHz+eqQzvRuVndTMeRJEmqsBznKAPy8iP//GgK9/53En3aNODX+3fIdCRJkqQKzRJdyuWsXM9vB4/iiymLOXGPVtxyQg+qVPYHCJIkSZlkiS7Fvpq6hCsHjeSHtbncfnJPTstq4+obkiRJpYAluhTKy488+PEU/v7hJNo1rs0zF/SnS/N6mY4lSZKkJEt0KbN4VWJ84/PJizmhT0tuPbEntat7mCRJkkoT21kpsmbDRk596CvmLV/LbSf15PR+jm9IkiSVRpboUuSO9yYyffFqnr9wAHt3bJzpOJIkSdoGl3koJb6ZtoQnh87gvL3bWaAlSZJKOc9EZ9jq9RsZNnMZN76RTduGtfj9EbtnOpIkSZIKYYkuYavXb2T4zGV8NW0JX09bwpg5K9iYH6lRtRJPnd+fWtU8JJIkSaWdja2EfDYph3v/O5nvZy9nY36kSqVAr9b1uWj/DuzVoRF9d93FVTgkSZLKCFtbmm3My+cfH07mgU+m0K5RbUuzJElSOWCLS7Mb3hjLC9/O4rSs1vz5uB7UrFY505EkSZK0kyzRaTZu3goGdmjEHaf0znQUSZIkFROXuEuz9RvzqeXZZ0mSpHLFEp1GK9bkMmnhSnq0qp/pKJIkSSpGlug0+mraEvIj7NvJi6dIkiSVJ5boNPpyymJqV6tMnzYNMh1FkiRJxcgSnUZfTlnMgA6NqFrZL7MkSVJ5YrtLk7nL1zJt8Wr26egohyRJUnljiU6TLycvBmBfS7QkSVK5Y4lOky+mLKZJ3ep0blYn01EkSZJUzCzRaZCXHxk6dTH7dmxMCCHTcSRJklTMvGLhTlqzYSMTFqxk3LwfGDf/B8bN+4EJC35gXW6+oxySJEnllCW6iGYsXs272QuShXkF0xevJj8mttWrUYVuLetxZv9d6dm6Hkf3bJnZsJIkSUoLS3QR3fzWOD6asIjWu9SkW4t6HNu7Jd1a1KNby3q0alDT8Q1JkqQKwBJdRBs25rNH2wa8duk+mY4iSZKkDPGNhUUwc8lqhs9cRtuGtTIdRZIkSRlkiU5Rfn7kmpdHU6VS4Noju2Q6jiRJkjIorSU6hHBECGFiCGFKCOHarWwPIYT7kttHhxD2TGeenfH0VzP4dvpSbjimGy3q18x0HEmSJGVQ2kp0CKEy8ABwJNANOCOE0G2L3Y4EOiV/XQT8K115dsaq9Ru5/b2JHNC5Cadmtc50HEmSJGVYOs9E9wemxBinxRg3AIOA47fY53jg6ZjwNdAghNAijZl2yA9rc1mbm8dRPZu7+oYkSZLSujpHK2B2gdtzgAEp7NMKmJ/GXJIklVq5ubnMmTOHdevWZTqKVGHUqFGD1q1bU7Vq1ZQfk84SvbVTtnEH9iGEcBGJcQ/atm2788mKaJda1fj32X3p3rJeib+2JKlimTNnDnXr1qVdu3b+9FMqATFGlixZwpw5c2jfvn3Kj0vnOMccoE2B262BeTuwDzHGh2OMWTHGrCZNmhR70MLUrFaZw7s3p/UuLm0nSUqvdevW0ahRIwu0VEJCCDRq1KjIP/1JZ4n+DugUQmgfQqgG/BwYssU+Q4Bzkqt07AWsiDE6yiFJqtAs0FLJ2pHvubSNc8QYN4YQLgfeByoDj8cYx4YQLk5ufwh4BzgKmAKsAc5PVx5JkiSpuKR1negY4zsxxs4xxt1ijLcm73soWaBJrspxWXJ7zxjjsHTmkSRJhatcuTJ9+vShR48eHHvssSxfvrxYnvfJJ5/k8ssvL5bnateuHT179qRPnz706dOHoUOHFsvzbmnUqFG88847P7rv3XffJSsri65du9KlSxeuvvpqAG666SbuuuuuYnvtvffee/PH11xzDd27d+eaa67hoYce4umnn96p5x45ciQXXnjhj+47/vjjGThw4I/uO++883j55Zd/dF+dOnU2fzxp0iSOOuooOnbsSNeuXTnttNNYuHDhTmVbunQphx12GJ06deKwww5j2bJlW92v4J+BrKyszfe/9NJLdO/enUqVKjFs2P+q5ZgxYzjvvPN2KltBXrFQkiT9SM2aNRk1ahTZ2dk0bNiQBx54INORturjjz9m1KhRjBo16keFc3s2btxYpNfYskRnZ2dz+eWX8+yzzzJ+/Hiys7Pp0KFDkZ4zVQX/Y/Dvf/+bESNGcOedd3LxxRdzzjnnpPw8W/uc//rXv3LFFVdsvr18+XJGjBjB8uXLmT59ekrPu27dOo4++mguueQSpkyZwvjx47nkkkvIyclJOdvW3HbbbRxyyCFMnjyZQw45hNtuu22b+276M1CwLPfo0YNXX32V/fff/0f79uzZkzlz5jBr1qydyrdJOlfnkCRJO+HPb45l3LwfivU5u7Wsx5+O7Z7y/gMHDmT06NEAfPvtt/zmN79h7dq11KxZkyeeeILdd9+dJ598kiFDhrBmzRqmTp3KiSeeyB133AHAE088wd/+9jdatGhB586dqV69OgAzZ87kl7/8JTk5OTRp0oQnnniCtm3bct5551GzZk0mTJjAzJkzeeKJJ3jqqaf46quvGDBgAE8++eQ2s27vORs2bMjIkSPZc889ufTSS7nsssvIycmhVq1aPPLII3Tp0oWXXnqJP//5z1SuXJn69evz4YcfcuONN7J27Vq++OILrrvuOt5++23+8Ic/0KVLFwCqVKnCpZde+pMsjzzyCA8//DAbNmygY8eOPPPMM9SqVesnr/HZZ58xduxYzj//fDZs2EB+fj6vvPIKnTp1ok6dOqxatYrjjjuO1atXM2DAAK677jrGjx9PnTp1uPrqq5k6depWP5ctP+e77757c7aVK1cyevRoevfuvfm+V155hWOPPZZmzZoxaNAgrrvuukL/bDz//PMMHDiQY489dvN9Bx10UKGPK8wbb7zBJ598AsC5557LgQceyO23357y47t27brNbcceeyyDBg3i97///c7G9Ey0JEnaury8PP773/9y3HHHAdClSxc+++wzRo4cyc0338z111+/ed9Ro0YxePBgxowZw+DBg5k9ezbz58/nT3/6E19++SUffPAB48aN27z/5ZdfzjnnnMPo0aM566yzuPLKKzdvW7ZsGR999BF///vfOfbYY/ntb3/L2LFjGTNmDKNGjdq830EHHUSfPn0YMGBAoc85adIkPvzwQ+6++24uuugi7r//foYPH85dd921uQTffPPNvP/++3z//fcMGTKEatWqcfPNN3P66aczatQoTj/9dLKzs+nbt2+hX7uTTjqJ7777ju+//56uXbvy2GOPbfU1AB566CGuuuqqzWdUW7f+8dWRhwwZsvmnA6effvqPtm3rc9nycy5o2LBh9OjR40f3vfDCC5xxxhmcccYZvPDCC4V+fkDKX4uVK1duHrvZ8lfBPxObLFy4kBYtEtfea9GiBYsWLdrq84YQ+NnPfkbfvn15+OGHU8qclZXF559/ntK+hfFMtCRJpVRRzhgXp7Vr19KnTx9mzJhB3759OeywwwBYsWIF5557LpMnTyaEQG5u7ubHHHLIIdSvXx+Abt26MXPmTBYvXsyBBx7IpuVpTz/9dCZNmgTAV199xauvvgrA2Wef/aMzg8ceeywhBHr27EmzZs3o2bMnAN27d2fGjBn06dMHSPwov3Hjxpsft73nPPXUU6lcuTKrVq1i6NChnHrqqZu3rV+/HoB99tmH8847j9NOO42TTjppp76G2dnZ/PGPf2T58uWsWrWKww8/fJuvMXDgQG699VbmzJnDSSedRKdOnVJ6je19LgU/5y3Nnz+fgksGL1y4kClTprDvvvsSQqBKlSpkZ2fTo0ePra5aUdSVLOrWrfuj//wUly+//JKWLVuyaNEiDjvsMLp06fKTEY4tNW3alHnzfrKa8g7xTLQkSfqRTWc9Z86cyYYNGzbPRN9www0cdNBBZGdn8+abb/5oXd1NYxqQeGPipjncVAtXwf02PVelSpV+9LyVKlUq0kxzweesXbs2APn5+TRo0GDzLPWoUaMYP348kDgjfMsttzB79mz69OnDkiVLfvKc3bt3Z/jw4YW+9nnnncc///lPxowZw5/+9KfNX6utvcaZZ565+Wzz4YcfzkcffZTS57e9z6Xg57ylmjVr/ujYDR48mGXLltG+fXvatWvHjBkzGDRoEACNGjX60Rv7li5duvk/Lql+LYp6JrpZs2bMn59Y8Xj+/Pk0bdp0q8/bsmVLIFGMTzzxRL799ttCs6xbt46aNWsWul8qLNGSJGmr6tevz3333cddd91Fbm4uK1asoFWrVgDbnU3eZMCAAXzyyScsWbKE3NxcXnrppc3b9t57781F7bnnnmPffffd6bypPGe9evVo37795iwxRr7//nsApk6dyoABA7j55ptp3Lgxs2fPpm7duqxcuXLz46+55hr++te/bj6jnp+fzz333POT11m5ciUtWrQgNzeX5557bvP9W3uNadOm0aFDB6688kqOO+64zTPohdne57I9Xbt2ZcqUKZtvv/DCC7z33nvMmDGDGTNmMHz48M1fxwMPPJDBgwezYcMGIHHcN809n3nmmQwdOpS3335783O99957jBkz5kevt+lM9NZ+devW7Sf5jjvuOJ566ikAnnrqKY4//vif7LN69erNx2X16tX85z//+cmIytZMmjQppf1SYYmWJEnbtMcee9C7d+/Nb8a67rrr2GeffcjLyyv0sS1atOCmm25i4MCBHHrooey5556bt91333088cQT9OrVi2eeeYZ77713p7Om+pzPPfccjz32GL1796Z79+688cYbQKIg9+zZkx49erD//vvTu3dvDjroIMaNG0efPn0YPHgwvXr14h//+AdnnHEGXbt2pUePHpvPmhb0l7/8hQEDBmweM9hka68xePBgevToQZ8+fZgwYUKRVt7Y1ueyPV26dGHFihWsXLmSGTNmMGvWLPbaa6/N29u3b0+9evX45ptvOOaYY9hvv/3o27cvffr04csvv9z8Jr+aNWvy1ltvcf/999OpUye6devGk08+uc0zx6m69tpr+eCDD+jUqRMffPAB1157LQDz5s3jqKOOAhIjKPvuuy+9e/emf//+HH300RxxxBEAvPbaa7Ru3ZqvvvqKo48+evMoDSRGgI4++uidyrdJiDEWyxOVlKysrFhwGRNJksqT8ePHb3d1Aak4/P3vf6du3bo/WSu6PFu/fj0HHHAAX3zxBVWq/PRtgVv73gshDI8xZv1kZzwTLUmSVOFccsklP5o3rwhmzZrFbbfdttUCvSNcnUOSJKmCqVGjBmeffXamY5SoTp06pbzySSo8Ey1JUilT1kYtpbJuR77nLNGSJJUiNWrUYMmSJRZpqYTEGFmyZAk1atQo0uMc55AkqRRp3bo1c+bMIScnJ9NRpAqjRo0aP7lSZGEs0ZIklSJVq1alffv2mY4hqRCOc0iSJElFZImWJEmSisgSLUmSJBVRmbtiYQghB5iZoZdvDCzO0GurZHiMKwaPc8Xgca4YPM7lXyaP8a4xxiZb21DmSnQmhRCGbevSjyofPMYVg8e5YvA4Vwwe5/KvtB5jxzkkSZKkIrJES5IkSUVkiS6ahzMdQGnnMa4YPM4Vg8e5YvA4l3+l8hg7Ey1JkiQVkWeiJUmSpCKyRG8hhHBECGFiCGFKCOHarWwPIYT7kttHhxD2zERO7ZwUjvNZyeM7OoQwNITQOxM5tXMKO84F9usXQsgLIZxSkvm081I5xiGEA0MIo0IIY0MIn5Z0Ru28FP7Orh9CeDOE8H3yOJ+fiZzacSGEx0MIi0II2dvYXur6lyW6gBBCZeAB4EigG3BGCKHbFrsdCXRK/roI+FeJhtROS/E4TwcOiDH2Av5CKZ3H0raleJw37Xc78H7JJtTOSuUYhxAaAA8Cx8UYuwOnlnRO7ZwUv5cvA8bFGHsDBwJ3hxCqlWhQ7awngSO2s73U9S9L9I/1B6bEGKfFGDcAg4Djt9jneODpmPA10CCE0KKkg2qnFHqcY4xDY4zLkje/BlqXcEbtvFS+nwGuAF4BFpVkOBWLVI7xmcCrMcZZADFGj3PZk8pxjkDdEEIA6gBLgY0lG1M7I8b4GYnjti2lrn9Zon+sFTC7wO05yfuKuo9Kt6IewwuAd9OaSOlQ6HEOIbQCTgQeKsFcKj6pfC93BnYJIXwSQhgeQjinxNKpuKRynP8JdAXmAWOAq2KM+SUTTyWk1PWvKpl88VIobOW+LZcvSWUflW4pH8MQwkEkSvS+aU2kdEjlOP8D+L8YY17iBJbKmFSOcRWgL3AIUBP4KoTwdYxxUrrDqdikcpwPB0YBBwO7AR+EED6PMf6Q5mwqOaWuf1mif2wO0KbA7dYk/ldb1H1UuqV0DEMIvYBHgSNjjEtKKJuKTyrHOQsYlCzQjYGjQggbY4yvl0hC7axU/85eHGNcDawOIXwG9AYs0WVHKsf5fOC2mFi3d0oIYTrQBfi2ZCKqBJS6/uU4x499B3QKIbRPviHh58CQLfYZApyTfJfoXsCKGOP8kg6qnVLocQ4htAVeBc72jFWZVehxjjG2jzG2izG2A14GLrVAlymp/J39BrBfCKFKCKEWMAAYX8I5tXNSOc6zSPy0gRBCM2B3YFqJplS6lbr+5ZnoAmKMG0MIl5N4l35l4PEY49gQwsXJ7Q8B7wBHAVOANST+96syJMXjfCPQCHgweZZyY4wxK1OZVXQpHmeVYakc4xjj+BDCe8BoIB94NMa41SW0VDql+L38F+DJEMIYEj/2/78Y4+KMhVaRhRBeILGySuMQwhzgT0BVKL39yysWSpIkSUXkOIckSZJURJZoSZIkqYgs0ZIkSVIRWaIlSZKkIrJES5IkSUVkiZYkSZKKyBItKaNCCHkhhFEFfrXbzr6riuH1ngwhTE++1ogQwsAdeI5HQwjdkh9fv8W2oTubMfk8m74u2SGEN0MIDQrZv08I4agdeJ0WIYS3kh8fGEJYEUIYGUIYH0L40w4833EhhGuTH5+w6euUvH1zCOHQoj7nVl7jyRDCKYXs80kIIeW13ZOf+1sp7Pd4CGFRCCF7i/vvCiEcnOrrSSr7LNGSMm1tjLFPgV8zSuA1r4kx9gGuBf5d1AfHGC+MMY5L3rx+i21773w84H9flx7AUuCyQvbvQ+JCBEX1O+CRArc/jzHuQeKS6L8IIfQtypPFGIfEGG9L3jwB6FZg240xxg93IGNp8iRwxFbuv5/EnydJFYQlWlKpEkKoE0L4b/Is8ZgQwvFb2adFCOGzAmdq90ve/7MQwlfJx74UQqhTyMt9BnRMPvZ3yefKDiH8Jnlf7RDC2yGE75P3n568/5MQQlYI4TagZjLHc8ltq5K/Dy54Zjh59vTkEELlEMKdIYTvQgijQwi/TuHL8hXQKvk8/UMIQ5Nni4eGEHZPXgr5ZuD0ZJbTk9kfT77OyK19HZNOBt7b8s4Y42pgOLBb8iz318m8r4UQdklmuTKEMC55/6DkfeeFEP4ZQtgbOA64M5lpt01nkEMIR4YQXizwtTkwhPBm8uMiHcMQwo3JzzE7hPBwCIlLjCb9Ivk1yg4h9E/un+rXZatijJ+R+E/NlvfPBBqFEJoX5fkklV2WaEmZtqmEjgohvAasA06MMe4JHATcvUUxAjgTeD95Nrk3MCqE0Bj4I3Bo8rHDSJxl3Z5jgTHJs63nAwOAvYBfhRD2IHHGcV6MsXfyjPCPymaM8Vr+d8b4rC2eexCwqXRXAw4hcdnaC4AVMcZ+QL/ka7XfVsAQQuXkY4ck75oA7J88W3wj8NcY44bkx4OTWQYDfwA+Sr7OQSTKbO0tnrs9sCzGuH4rr9so+bUYCzxN4jLKvYAxJC7HC4kzr3sk7794i6/N0GTma5KZphbY/AGwV4E8pwODd/AY/jPG2C95fGoCxxTYVjv5k4FLgceT96XydckKITxayOtuzQhgnx14nKQyqEqmA0iq8NYmyzAAIYSqwF9DCPsD+STOwDYDFhR4zHfA48l9X48xjgohHEBidODLZOeuRuIM7tbcGUL4I5BDotQeAryWPPtKCOFVYD8SpfmuEMLtwFsxxs+L8Hm9C9wXQqhOoox/FmNcG0L4GdAr/G+mtz7QCZi+xeNrhhBGAe1InBH+oMD+T4UQOgERqLqN1/8ZcFwI4erk7RpAW2B8gX1aJL8GBe0XQhhJ4mt/GzAHaBBj/DS5/SngpeTHo4HnQgivA69vI8dPxBg3hhDeA44NIbwMHA38HijKMdzkoBDC74FaQEMSpf/N5LYXkq/3WQihXkjMlW/r61Iw3zDgwlQ/nwIWAS134HGSyiBLtKTS5iygCdA3xpgbQphBouhslixF+5MoX8+EEO4ElgEfxBjPSOE1rokxvrzpRtjGm91ijJOSZ6mPAv4WQvhPjPHmVD6JGOO6EMInwOEkzrS+sOnlgCtijO8X8hRrY4x9Qgj1gbdIzETfB/wF+DjGeGJIvAnzk208PgAnxxgnbu812OJrS2ImevPZ3OTrb8vRwP4kxjZuCCF0386+WxpM4nNaCnwXY1yZ/IlDqseQEEIN4EEgK8Y4O4RwEz/+fOIWD4ls4+sSQmhWhOzbUoPE11RSBeA4h6TSpj6wKFmgDwJ23XKHEMKuyX0eAR4D9gS+BvYJIWyaca4VQuic4mt+BpyQfExt4ETg8xBCS2BNjPFZ4K7k62wpN3lGfGsGkRgT2Q/YVJrfBy7Z9JgQQuctxwkKijGuAK4Erk4+pj4wN7n5vAK7rgTqFrj9PnDFplGY5HjKliaRONO9TcnXXxaSc+fA2cCnIYRKQJsY48ckziI3ALacX94yU0GfkPh6/opEoYaiH8NNhXlxcnZ6yxU7No3T7EtihGYFqX1ddlRnILvQvSSVC5ZoSaXNc0BWCGEYibPSE7ayz4Ek5qBHknhj3L0xxhwSpfKFEMJoEoWsSyovGGMcQWLVhW+Bb4BHY4wjgZ7At8mxij8At2zl4Q8Do0PyjYVb+A+JM7UfJueWAR4FxgEjQmKZtH9TyE8Fk1m+B34O3EHirPiXQOUCu30MdEvOlp9O4ox11WS27OTtLZ93NTB1U2ndjnNJjMCMJrEKyM3J1342hDAGGAn8Pca4fIvHDQKuSb6Bb7ctXjuPxBn2I5O/U9RjmHy9R0jMab9OYsynoGUhseTgQyTGdiCFr8v2ZqJDCC+QGDHZPYQwJ4RwQfL+qiTepDpsW3kllS8hxi1/2iVJqihCCCeSGJ35Y6azlGXJr+OeMcYbMp1FUslwJlqSKrAY42vJlTi0c6oAd2c6hKSS45loSZIkqYiciZYkSZKKyBItSZIkFZElWpIkSSoiS7QkSZJURJZoSZIkqYj+P8jsKbvbg+6fAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(random_state=RANDOM_SEED)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "plot_roc_curve(rf, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aecabf7a",
   "metadata": {},
   "source": [
    "Contrary to our expectations, the model does not outperform the decision tree, it actually performs the same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69568bd",
   "metadata": {},
   "source": [
    "### Gradient Boosting Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1b5ce0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fba1c9ed760>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lFOQFZnz3qCZKJml3WKIlScqgjaXlPDxnGcN6dWBg1+Kdnte9fREj+nRqwmSSdoclWpKkDHhkzjIuvnMmlVWJOc7n7TeY8/Ybkt1QkhqNJVqSpAxYsGozlVWRiw/dk3ZF+Ry/d99sR5LUiCzRkiRl0KWHD6Ndkf+7lVoa/1ZLktSIZi1axxuL1zHjg7XZjiIpgyzRkiQ1oivufZ25yzcC0K19EYX5eVlOJCkTLNGSJDWi8qoqjhrdm6s/tjcd2hRYoqUWyhItSVIja1OQR48ObbIdQ1IGWaIlSdpF/5i1hJuemLfdvkVrtjC6r+s9Sy2dJVqSpBRtragkxo+2n5q7kg9Wb+Hwkb2q9+3ZswNnTBqQhXSSmpIlWpKkFDzz7ko+fftLVMXt9w/sVsxvPjkpO6EkZY0lWpKkFCxeW0JVhIsP3ZOObQur948b0DmLqSRliyVakqQ0nLffEPp0bpvtGJKyzBItSVI9Xl+8jpfeX8PMhd48RdJHLNGSJNXjqn++ySvJuw92altAx7b+r1NSiiU6hJAHjAf6ASXAnBjj8kwGkySpqT06ZxkPvr6Ump8dnLdyEwcM687Nn5xEm4J8igq8eYqkBkp0CGFP4ArgSOBdYCXQFtgrhLAFuAX4fYyxKtNBJUnKlK0VlVz90Nvc8dwCenZsQ8c2H/3vsVu7Ig4b0Wu7DxNKUkMj0T8GfgNcGGPcblGfEEIv4BzgU8DvMxNPkqTMmLdiE5fcOZPSiko2b61g1aYyPnfgUK44dqSjzZIaVG+JjjF+op5jK4BfNHYgSZIyobS8kq3lH/3g9NWFa5m7fCOHj+xF5+JCThzXlyNG9c5iQknNyS5/OiKEcFSM8bHGDCNJUiZs3lrBvj/5Dxu3Vuxw7NsnjGLPnh2ykEpSc7Y7HzH+HTCosYJIkpQpm7ZWsHFrBSeM68ukQV2r93dpV8gePdpnMZmk5qqhDxY+sLNDQPfGjyNJUuYcsGcPzpnm+I+k3dfQSPRBwCeBTbX2B2BqRhJJkiRJOa6hEv0CsCXG+FTtAyGEuZmJJEmSJOW2hlbnOK6eYwc3fhxJkiQp97kQpiRJkpQmS7QkSZKUJku0JEmSlCZLtCRJkpSmlEt0COEH9W1LkiRJrUU6I9EzGtiWJEmSWoWUS3SM8Z/1bUuSJEmtRUO3/f4lEHd2PMZ4eaMnkiRJknJcQ3csfKVJUkiS1Ih+/eQ8Xpi/pnp7a3llFtNIaokaumPh72tuhxDaxxg3ZzaSJEm7584XFlJSXsmgbu2q900d0o1Jg7tmMZWklqShkWgAQgj7Ab8DOgCDQgjjgQtjjBdnMpwkSbvq8JG9uO7j47MdQ1ILleoHC38BHAOsBogxvgYcnKFMkiTtkpUbt7Jw9RbKK6uyHUVSC5fSSDRAjHFRCKHmLieYSZJyxpwP13PCjdOrt4sKvJ+YpMxJtUQvCiHsD8QQQhFwOfBW5mJJkpSaFRtKefH9NcxdthGASw8bxtAe7TloeI8sJ5PUkqVaoi8CbgD6A0uAR4BLMhVKkqRU/eyRudw7Y3H19ikT+jG8d8csJpLUGqRUomOMq4BzM5xFkqQ6PfH2Cp57b1Wdx2YuXEv/LsX8/rNTaN+mgL6di5s4naTWKNXVOfYgMRK9L4mbrzwPfDnGOD+D2SRJAuC6R+fy1tINtC3Mr/P4UaN7M6yXo8+Smk6q0zn+DNwEnJbcPhu4C5iWiVCSpNbnBw/M4c2lG+o8Nn/lZo4Y1Zvfnje5iVNJUt1S/ehyiDH+McZYkfz1J+q5HbgkSen684sLWbxmC3mBHX6NH9iZE8f1zXZESapW70h0CKFb8ssnQghXAneTKM9nAf/KcDZJUitzyj79ueLYkdmOIUkNamg6xwwSpXnbAtEX1jgWgR9lIpQkSZKUy+ot0THGoU0VRJIkSWouUr5jYQhhLDAaaLttX4zxD5kIJUlqHUrLK3n0zeWUVVRRGf2ojaTmI9Ul7r4PHEqiRD8EHAdMByzRkqRd9vhby7n8rlert7u3L8piGklKXaoj0WcA44FXY4yfCSH0Bm7LXCxJUmtQVlEFwF1f2JeB3Yrp38UbpUhqHlJd4q4kxlgFVIQQOgErgD0yF0uS1Jr069KWAV3bEUJo+GRJygGpjkS/EkLoAvyWxIodm4CXMhVKktSy3fHs+9w/60PWbi7LdhRJ2iUplegY48XJL28OITwMdIoxvp65WJKkluzfs5exYNVmJgzswuTBXennNA5JzUxDN1uZWN+xGOPMxo8kSWqJNpSWc/h1T7J6cxkxwv57duf3n52a7ViStEsaGom+vp5jETi8vgeHEI4FbgDygdtijNfUcc6hwC+AQmBVjPGQBjJJkpqhtZvLWLWpjKNG92ZUn44cvFfPbEeSpF3W0M1WDtvVJw4h5AM3AUcBi4GXQwgPxBjfrHFOF+DXwLExxoUhhF67+nqSpObhuLF9+NjEAdmOIUm7JeWbreyCqcC8GON8gBDC3cApwJs1zjkHuC/GuBAgxrgig3kkSRmyYNVmps9bVe85a/wQoaQWJJMluj+wqMb2YmBarXP2AgpDCE8CHYEbvAuiJDU/1z46l3+9vjSlc3t1bNvwSZKU4zJZouta7LP2PV0LgEnAEUAx8HwI4YUY4zvbPVEIFwAXAAwaNCgDUSVJqVqwajO3PD2fyqqq6n2zFq5jWK8O/PkLtcdKtleUn0eXdt6VUFLzl+ptvwNwLrBHjPGqEMIgoE+Msb61ohcDA2tsDwA+rOOcVTHGzcDmEMLTJO6MuF2JjjHeCtwKMHny5NpFXJLUhB6es4y7XlpI705tyKtxc5SDh/d0lFlSq5HqSPSvgSoSq3FcBWwE/gZMqecxLwPDQwhDgSXA2STmQNf0D+BXIYQCoIjEdI//TTm9JKnJ/PTht3ni7RWsTs5tfvJrh1FclJ/lVJKUHamW6GkxxokhhFcBYoxrQwj1/jwuxlgRQrgUeITEEne3xxjnhBAuSh6/Ocb4VvLmLa+TKOm3xRhn7/J3I0lqdBWVVVRFeHj2MkrKKtlnYBcGd29H28K8bEeTpKxJtUSXJ5esiwAhhJ4kSm+9YowPAQ/V2ndzre1rgWtTzCFJaiJVVZHbps/nukffoawi8Z/80/bpz/+eNSG7wSQpB6Raom8E7gd6hRD+BzgD+E7GUkmSsmrdljK++pfX+M/bKzhyVC/2GdQVgKNH985yMknKDSmV6BjjnSGEGSRW0QjAqTHGtzKaTJKUNb/87zyefGclPzhpNJ/efwgh1LXgkiS1XqmuznEDcE+M8aYM55Ek5YD1JeX06dSW8w8Ymu0okpSTUv1UyEzgOyGEeSGEa0MIkzMZSpIkScplKZXoGOPvY4zHk7iV9zvAT0MI72Y0mSRJkpSj0l2faBgwEhgCvN3oaSRJkqRmIKUSHULYNvJ8FTAHmBRjPCmjySRJkqQcleoSd+8D+8UYV2UyjCQpu8oqqiitqKS8ssFbAUhSq1ZviQ4hjIwxvg28BAwKIQyqeTzGODOT4SRJTefh2cu44m+vs76kHIDB3dtlOZEk5a6GRqK/AlwAXF/HsQgc3uiJJEmNbktZBa8uXEeMdR9//K3l3PHcAsYP6MxJ4/sBMLZ/5yZMKEnNS70lOsZ4QfLL42KMpTWPhRDaZiyVJKlR3fzke9z433n1nvOZA4bwzeNGUVSQ7mfOJan1SXVO9HPAxBT2SZJy0OayStoW5vHHz02r83jXdoUM69WxiVNJUvPV0JzoPkB/oDiEsA+JW34DdAKcLCdJzUhBXh5ThnTLdgxJahEaGok+BjgfGAD8vMb+jcC3MpRJkiRJymkNzYn+PfD7EMLpMca/NVEmSZIkKac1NJ3jkzHGPwFDQghfqX08xvjzOh4mScqid5dv5MI/zqC0vLJ63/qSckII9TxKkpSOhqZztE/+3iHTQSRJjeOd5ZuYv2ozR4/uTefiwur9LlknSY2noekctyR//2HTxJEkNZavHTOCvXq74oYkZUJKi4GGEH4WQugUQigMIfwnhLAqhPDJTIeTJEmSclGqK+ofHWPcAJwILAb2Ar6esVSSJElSDku1RG+bVHc8cFeMcU2G8kiSJEk5L9U7Fv4zhPA2UAJcHELoCZQ28BhJkiSpRUqpRMcYrwwh/BTYEGOsDCFsBk7JbDRJUjr+MWsJ97+6hBUbtmY7iiS1eKl+sLAQ+BRwTwjhXuBzwOpMBpMkpedvM5fw4vw1FOYHDt6rJwO6Fmc7kiS1WKlO5/gNiXnRv05ufyq57/OZCCVJSk1peSWn3vQsKzZuZUNJOXsP6Mz9Fx+Q7ViS1OKlWqKnxBjH19j+bwjhtUwEkiSlbt2Wct5etpFpQ7uxV++OHDqiZ7YjSVKrkGqJrgwh7BljfA8ghLAHUNnAYyRJTeTUffrziamDsh1DklqNVEv014EnQgjzgQAMBj6TsVSSJElSDmuwRCeXs1sPTAV6kSjRb8cY/fi3JEmSWqV6V+cIIXwemAP8EpgFDIkxvmaBliRJUmvW0Ej0l4AxMcaVyXnQdwIPZDyVJEmSlMMaKtFlMcaVADHG+SGENk2QSZK0Ezc8/i4vzP9omf6yyqosppGk1quhEj0ghHDjzrZjjJdnJpYkqS5/eWURWysq2aNHBwDyQ+CAYd2ZMqRrlpNJUuvSUIn+eq3tGZkKIklKzaEjenHdx8c3fKIkKWPqLdExxt83VRBJkiSpuWhodY5bQwhjd3KsfQjhsyGEczMTTZIkScpNDU3n+DXwvRDC3sBsYCXQFhgOdAJuJ7FihyQpQyoqq3j8rRVsKatgc1lFtuNIkmh4Oscs4MwQQgdgMtAXKAHeijHOzXw8SdLMheu46E8ffSSle/uiLKaRJEGKt/2OMW4CnsxsFElqvZauL+F3z7xPRVXc4diSdSUA/PIT+zBuQGcGdG3X1PEkSbWkVKIlSZn1yOxl3Db9fTq2LSAvhB2O9+9SzOQhXenbuTgL6SRJtVmiJSkHbBt/fuYbh9GlndM1JCnX1bs6R20hhPaZCiJJkiQ1FymNRIcQ9gduAzoAg0II44ELY4wXZzKcJLVEKzaUcswvnmZD6UcrbVTFxFh0qGMqhyQp96Q6neN/gWOABwBijK+FEA7OWCpJakFWbtzK8g2l1dvvrtjI2i3lnDCuL0O7f/QDvr5d2tK5uDAbESVJaUp5TnSMcVGtEZLKxo8jSS3Psb94mtWby3bYf+7UQew/rEcWEkmSdleqJXpRckpHDCEUAZcDb2UuliQ1T/NWbOTlBWu327d2SxnHje3Dafv0r95XXJTPtD26N3U8SVIjSbVEXwTcAPQHFgOPAs6HlqRafvjPN3nm3VU77J82tBtHj+mThUSSpExItUSPiDGeW3NHCOEA4NnGjyRJzUtVVeT6x+ayZnMZby/byD6DuvCbcydVH88L0LNjmywmlCQ1tlRL9C+BiSnsk6RWZ/HaEm564j06ti2guDCfg4b1oE/nttmOJUnKoHpLdAhhP2B/oGcI4Ss1DnUC8jMZTJJy1ff/MXu7ec9llVUA/PDkMXxs4oBsxZIkNaGGRqKLSKwNXQB0rLF/A3BGpkJJUi578PWltC3MZ1TfTtX7RvTp6AcFJakVqbdExxifAp4KIdwRY/ygiTJJUs47bGRPfnzq3tmOIUnKklTnRG8JIVwLjAGqJ/rFGA/PSCpJkiQph+WleN6dwNvAUOCHwALg5QxlkiRJknJaqiPR3WOMvwshfLHGFI+nMhlMknLBvBWbeObdldvtKyn3hq2S1NqlWqLLk78vDSGcAHwI+BF0SS3ezx+by0NvLNthf78uxVlII0nKFamW6B+HEDoDXyWxPnQn4EuZCiVJuaK8MrJX7w785cL9qvcFAp3bFWYxlSQp21Iq0THGB5NfrgcOg+o7FkpSi5efl0eXdkXZjiFJyiEN3WwlHzgT6A88HGOcHUI4EfgWUAzsk/mIktS0KiqrOP//XubD9SUsW1/K4O7tsx1JkpRjGhqJ/h0wEHgJuDGE8AGwH3BljPHvGc4mSU1uS1kFqzeVMX3eKsb068ThI3tx6Ihe2Y4lScoxDZXoycC4GGNVCKEtsAoYFmPc8VM2ktTM/fO1D7nsrlert8+eOohP7Ts4i4kkSbmqoRJdFmOsAogxloYQ3rFAS8pVcz5cz/qS8oZP3Inn3lsFwNePGUG7onxOGte3saJJklqYhkr0yBDC68mvA7BncjsAMcY4LqPpJClFi9Zs4YQbp+/287QpyOMLB+1BUUGq96KSJLVGDZXoUU2SQpJ20eK1W/jX60tZsXErAF86cjj77tF9l5+vd6e2FmhJUoPqLdExxg+aKogk7Yo/PP8Btz49H4CCvMAhe/Vkn0Fds5xKktTSpXqzFUlqUlVVkasefJMVG0vrPe/NDzfQoU0BL3/7SPLyoE1BfhMllCS1ZpZoSTlpxcat3PHcAnp1bEPn4p3fHbAwP49jx/ahuMjyLElqOimX6BBCMTAoxjg3g3kktQJ/fH4BNz81v95zKqsiAF8+ai8+MXVQU8SSJCllKZXoEMJJwHVAETA0hDABuCrGeHIGs0lqoV58fw0bSso5ekyfes8rKkjMcZYkKdekOhL9A2Aq8CRAjHFWCGFIZiJJag16dmrD9WeOz3YMSZJ2SaoluiLGuD6EkNEwklq2Nz/cwPINpazYsDXbUSRJ2i2plujZIYRzgPwQwnDgcuC5zMWS1NJsrajklJumU16ZmOs8fmCX7AaSJGk3pFqiLwO+DWwF/gw8Avw4U6EktRyrNm3lnpcXsaWsgvLKyKf3G8xpEwcwuFu7bEeTJGmXpVqiR8QYv02iSEtSyh56YynXPpJY1Cc/L7D/sB5McBRaktTMpVqifx5C6Av8Fbg7xjgng5kktSDblqp79btH0aVdIX62QpLUEuSlclKM8TDgUGAlcGsI4Y0QwncyGUxSyxICFmhJUouR8s1WYozLgBtDCE8A3wC+h/OiJdWwatNWzrz5eTaUllfvKymrBCBggZYktRyp3mxlFHAWcAawGrgb+GoGc0nKMeWVVazcWP/SdG8t3cD8VZs5ZK+eDOhaXL2/X5diOhWn/G92SZJyXqr/V/s/4C7g6BjjhxnMIylHfemeWfzr9aUpnfv5g4Zy0HDvNChJarlSKtExxn0zHURSbnpx/mrWbilj7rKN7NGjPRceske95xcXFbDvHt2bKJ0kSdlRb4kOIfwlxnhmCOENINY8BMQY47gGHn8scAOQD9wWY7xmJ+dNAV4Azoox3pvONyApc5atL+WsW1+o3j5mTG/OmjIoi4kkScoNDY1EfzH5+4npPnEIIR+4CTgKWAy8HEJ4IMb4Zh3n/ZTEDVwkNbHNWyv43fT3KSmv3OHYui2JDwh+/ZgRHD6yF0O6t2/qeJIk5aR6S3SMcdsEyItjjFfUPBZC+ClwxY6PqjYVmBdjnJ88/27gFODNWuddBvwNmJJGbkmN5MX3V/Pzx96hIC+QV8cSdB3bFLDfnt0Z1bdTFtJJkpSbUv1g4VHsWJiPq2NfTf2BRTW2FwPTap4QQugPnAYcjiVayoqqqsTv9198AHsP6JzdMJIkNRMNzYn+f8DFwB4hhNdrHOoIPNvAc9e1KGystf0L4IoYY2V9N2EIIVwAXAAwaJDzMSVJkpRdDY1E/xn4N3A1cGWN/RtjjGsaeOxiYGCN7QFA7eXxJgN3Jwt0D+D4EEJFjPHvNU+KMd4K3AowefLk2kVckiRJalINlegYY1wQQrik9oEQQrcGivTLwPAQwlBgCXA2cE6tJx9a4/nuAB6sXaAlSZKkXJPKSPSJwAwSUzFqzrmIwE4XjI0xVoQQLiWx6kY+cHuMcU4I4aLk8Zt3J7ik3belrIL7Zy0BoLgoL8tpJElqPhpanePE5O9D6zuvnsc/BDxUa1+d5TnGeP6uvIaknZu7bCNvLd1Q57GKqsjNT73Heys38aUjhzOsV8cmTidJUvOV0uocIYQDgFkxxs0hhE8CE4FfxBgXZjSdpN3yxbtf5e1lG3d6vEeHIv70uWkcMKxHE6aSJKn5S3WJu98A40MI44FvAL8D/ggckqlgknbf1ooqDh/Zi++eOLrO4707taFdUar/GZAkSduk+n/PihhjDCGcAtwQY/xdCOHTmQwmqXF0aFPA0B7eaVCSpMaUaoneGEL4JvAp4KDkrboLMxdL0q5au7mML94zi81bK1iyroS9+3sDFUmSGluqH8c/C9gKfDbGuIzE3QivzVgqSbvs3RWbePqdlZSWVzJ1SDdOGNc325EkSWpxUhqJjjEuCyHcCUwJIZwIvBRj/ENmo0lKR2VV5Ln3VvH75xYA8O0TRrH/nn5gUJKkTEh1dY4zSYw8P0lirehfhhC+HmO8N4PZJNWyeWsFC9ds2W7f1ooqHntzGffNXMLS9aV0Li7k/P2HsM/ArllKKUlSy5fqnOhvA1NijCsAQgg9gccBS7TUhP7fnTN5+p2VO+zPC3DoiMQqHEeM6kWbgvwspJMkqfVItUTnbSvQSatJfT61pEayfksZo/t24vIjhtXYG5g4qAu9OrXNWi5JklqbVEv0wyGER4C7kttnUetOhJIyp6oq8usn5/HGkvWcMWkAx471w4KSJGVTqh8s/HoI4WPAgSTmRN8aY7w/o8mkVu7NDzdw54sfEIF3l2/k5QVrOXl8P7530phsR5MkqdWrt0SHEIYD1wF7Am8AX4sxLmmKYFJrd++Mxdz54kJ6dGhDm4I8fnLa3nxi6kBCCNmOJklSq9fQSPTtwB+Ap4GTgF8CH8t0KEkQiXRsW8Ar3zky21EkSVItDZXojjHG3ya/nhtCmJnpQFJrF2MkRogx20kkSdLONFSi24YQ9iExDxqguOZ2jNFSLTWyU256ltcXrwegS7vCLKeRJEl1aahELwV+XmN7WY3tCByeiVBSa/beik1MHNSFQ/bqxci+HbMdR5Ik1aHeEh1jPKypgkj6yMRBXfnikcOzHUOSJO1EqutES0pDeWUVf391CVvKKnfhsU6GliQp11mipQx4ZcFavn7v67v8+L5dihsxjSRJamyWaCkDKqqqALj9/MlMGNg1rcfmBejSrigTsSRJUiNJqUSHxN0dzgX2iDFeFUIYBPSJMb6U0XRSM9epbSHd2luIJUlqafJSPO/XwH7AJ5LbG4GbMpJIkiRJynGpTueYFmOcGEJ4FSDGuDaE4PCaJEmSWqVUR6LLQwj5JNaGJoTQE6jKWCpJkiQph6Vaom8E7gd6hRD+B5gO/CRjqaRmamNpOS/MX81bSzdkO4okScqglKZzxBjvDCHMAI4gccvvU2OMb2U0mdQM/c+/3uLulxdVb7dv4wI4kiS1RKmuzjEI2AL8s+a+GOPCTAWTmqNNWyvo27kt1585no5tChnZx9t2S5LUEqU6TPYvEvOhA9AWGArMBcZkKJfUbBUX5bP/nj2yHUOSJGVQqtM59q65HUKYCFyYkURSMxNj5KcPz2XR2i3M/GAtxUX52Y4kSZIybJcmbMYYZ4YQpjR2GKk52lJWyc1PvUf39kV0aVfI4SN7ZTuSJEnKsFTnRH+lxmYeMBFYmZFEUjN14SF7cMHBe2Y7hiRJagKpjkTX/HRUBYk50n9r/DiSJElS7muwRCdvstIhxvj1JsgjSZIk5bx6S3QIoSDGWJH8IKHUasUYmblwHRtLy3c4VlruzTslSWptGhqJfonE/OdZIYQHgL8Cm7cdjDHel8FsUs6Yt2ITp//muXrP6dCmsInSSJKkbEt1TnQ3YDVwOB+tFx0BS7RahS1llQB8+/hRTBrSdYfjBXmBMf06N3UsSZKUJQ2V6F7JlTlm81F53iZmLJWUo/bs1Z6Jg3Ys0ZIkqXVpqETnAx3YvjxvY4lWi/fce6v4v2cXsH7LjnOhJUlS69VQiV4aY7yqSZJIOejB15fy37dXMKJ3RyYP7sqIPp2yHUmSJOWAhkp0XSPQUqvStV0RD33xoGzHkCRJOSSvgeNHNEkKSZIkqRmpt0THGNc0VRBJkiSpuWhoJFqSJElSLZZoSZIkKU2WaEmSJClNlmhJkiQpTZZoSZIkKU2WaEmSJClNlmhJkiQpTQ3dsVBqlTaUlrNmUxkbSyuyHUWSJOUgS7RUhyOvf4oVG7cC0L9LcZbTSJKkXGOJluqwZnMZR47qxQnj+jK8V8dsx5EkSTnGEi3txIg+HTltnwHZjiFJknKQJVpKWrO5jNuemU9ZRRWVMWY7jiRJymGWaCnpqXdW8Osn36O4MJ8ORQWM7NMp25EkSVKOskSrVdpaUckX75rFms1l1ftWbUp8kPCRLx3MoO7tshVNkiQ1A5ZotUpL15Xy8JxlDOvVgZ4d2gDQu1NbRvfrRJ/ObbOcTpIk5TpLtFqV+19dzLfum01lVWLO82WHD+OUCf2znEqSJDU3lmi1OFsrKlmwakudx55/bzVbKyr5wsF70KYgn0P26tnE6SRJUktgiVaL8+37Z3PvjMU7Pd6pbQHfPG5UEyaSJEktjSVaLc66LeX071LMt0+ouygP6uaHBiVJ0u6xRKtF6lxcyPF79812DEmS1EJZotUixBj59ZPvsXR9CW8t3UDn4sJsR5IkSS2YJVotwrot5Vz7yFzaFeVTXJjPEaN6ZTuSJElqwSzRajZeen8N1z86l5Lyyh2OlVcmlqy74tiRfHr/IU2cTJIktTaWaOW8qqrIb556j+sfnUvfzsXs1btDnecN6NqbA4b1aOJ0kiSpNbJEK2eUVVTxs4ff5o7nFlCRvBlKTSeO68vVH9ubjm2d7yxJkrLLEq2csHjtFi7986vMWrSO0/bpv8MydMN7d+CEvfsSQshSQkmSpI9YopUTvnX/bOat2MSvz53o0nSSJCnn5WU7gASwfksZk4d0tUBLkqRmwRItSZIkpcnpHMqa8soqfvrvt1lfUs7CNVvo2r4o25EkSZJSYolW1ry3chO3TX+fru0KKS7MZ9rQ7tmOJEmSlBJLtJrco3OW8fPH3qE0edOUn5y2N8c5F1qSJDUjlmg1uefnr2beik0cOao3EwZ2YdKQrtmOJEmSlBZLtLKiuCifmz81KdsxJEmSdoklWk1myboSlqwtYfmG0mxHkSRJ2i2WaDWZ0256lhUbtwLQp1PbLKeRJEnadZZoZcRbSzfw7LxV2+1bt6Wc48b24ZP7DmZg13Y7eaQkSVLus0QrI3728Ns8MXflDvunDe3GAcN6ZCGRJElS48loiQ4hHAvcAOQDt8UYr6l1/FzgiuTmJuD/xRhfy2QmNY2Kqsi4AZ258/PTqveFEOjQxn+3SZKk5i9jjSaEkA/cBBwFLAZeDiE8EGN8s8Zp7wOHxBjXhhCOA24Fpu34bGqO8vMCHdsWZjuGJElSo8vksOBUYF6McT5ACOFu4BSgukTHGJ+rcf4LwIAM5lGGlZZX8snbXmTlpq0sW1/K6H6dsh1JkiQpIzJZovsDi2psL6b+UebPAf/OYB5l2JrNZbzywVomDOzCPgO7cOTo3tmOJEmSlBGZLNGhjn2xzhNDOIxEiT5wJ8cvAC4AGDRoUGPlU4Z8YupAzpridZIkSS1XXgafezEwsMb2AODD2ieFEMYBtwGnxBhX1/VEMcZbY4yTY4yTe/bsmZGwkiRJUqoyWaJfBoaHEIaGEIqAs4EHap4QQhgE3Ad8Ksb4TgazSJIkSY0mY9M5YowVIYRLgUdILHF3e4xxTgjhouTxm4HvAd2BX4cQACpijJMzlUm7bu6yjTz+1vJ6z9lQUt5EaSRJkrIro4v2xhgfAh6qte/mGl9/Hvh8JjOocdz0xDweeG2H2Tg7KMgL3o1QkiS1eN75QimpjJE9erbn4S8eXO95eQEK8jM5S0iSJCn7LNFKWQCKCizIkiRJNiJJkiQpTY5Eq15byirYVFrB1vLKbEeRJEnKGZZo7VR5ZRX7Xf1f1idX3RjZp2OWE0mSJOUGS7R2UFpeyfPzV7OptIL1JeUcO6YPB+3Vg3H9u2Q7miRJUk6wRGsH97+6hG/e90b19qEjenL2VG/jLUmStI0lWtWeeXclL72/hlmL1gFw1xf2pUu7Qkb0dhqHJElSTZZoVfvJQ2/z1tIN5AUY0LWYSYO7uqSdJElSHSzRqhZj5JgxvbnlU955XZIkqT4OM0qSJElpskRLkiRJabJES5IkSWmyREuSJElp8oOFrVyMkaffXcXG0nI2JO9MKEmSpPpZolu5ucs38unbX6rePnivnllMI0mS1DxYolu5reVVAPzo1LHsO7Qbg7u3z3IiSZKk3GeJFgD9u7RluHcmlCRJSokfLJQkSZLSZImWJEmS0mSJliRJktLknOhWqqKyigWrN7NwzZZsR5EkSWp2LNGt1M8fe4dfP/le9XbbgvwsppEkSWpeLNGt1Not5XRsW8DVH9ubdkX5TB3aLduRJEmSmg1LdCvWtjCfE8f1y3YMSZKkZscPFkqSJElpskRLkiRJaXI6Rytzxb2v8/byjSxes4W8vJDtOJIkSc2SJbqVuXfmYvp3KWZs/85MHtw123EkSZKaJUt0K3Ty+H587ZgR2Y4hSZLUbDknWpIkSUqTJVqSJElKkyVakiRJSpMlWpIkSUqTJVqSJElKkyVakiRJSpNL3LVA5ZVVPPH2Cv42czEfrN6y3bHKqpilVJIkSS2HJbqZq6qKVCSL8bwVm7h3xmL+MWsJqzeX0aNDG/YZ1IWa9yUc0r09R4/pnZ2wkiRJLYQlupk74+bnmLlwXfV2UX4eR47uxRmTBnDw8J4U5DtjR5IkqbFZopuhGCNzPtxAaXkl767YxISBXThqdG+6tS/i2DF96Nq+KNsRJUmSWjRLdDP0wvw1fOK3L1RvT9ujG5ccNiyLiSRJkloXS3QzMm/FRp56ZxXvLNsIwI9OGcPQHh3YZ1CX7AaTJElqZSzRzcj1j77Dv2cvA6CoII8jR/emb+fiLKeSJElqfSzRzUh5ZWRE74785aL9aFOQR9vC/GxHkiRJapUs0c1Mfl6gc3FhtmNIkiS1aq5/JkmSJKXJkehmoKyiiq0VlVRUVWU7iiRJkrBE57zS8kr2u/o/rN1SDsD4AZ2znEiSJEmW6By3eWsFa7eUc8yY3kwZ0o1Jg7tmO5IkSVKrZ4luJg4Y1oPz9huS7RiSJEnCEp2zVm7cyv2vLmZDSUW2o0iSJKkWS3SOuv/VxfzkobcByAswoKs3VZEkScoVlugcVVEVAXjt+0dTXJhPUYGrEUqSJOUKm1mOa1OQZ4GWJEnKMbYzSZIkKU1O58gx5ZVVrNtSzuatfqBQkiQpV1mic8wnb3uRF99fAyQ+UJgXQpYTSZIkqTZLdI5ZvqGUcQM68/HJAxnQtdj50JIkSTnIEp2DhvZoz6f2HZztGJIkSdoJhzklSZKkNFmiJUmSpDQ5nSMHrC8p51v3v8HmrRUs21DK+GwHkiRJUr0cic4Bby/dwL9eX8rCNVsY0bsjh4/sle1IkiRJqocj0Vn0h+cXcMPj71JWWQXAj08dy/579shyKkmSJDXEEt2EVmworS7MAM/NW01peSWnTexP+zYFTBjYJXvhJEmSlDJLdBN5ZM4yLvzjjB3279GzPT8+de8sJJIkSdKuskRn2MLVW5j94XqeeXclAN85YRSdigurj4/u2ylb0SRJkrSLLNEZ9rV7X+Ol5G28C/MDZ00ZSMe2hQ08SpIkSbnMEp1hW8srmTKkKz8+dW+6tiu0QEuSJLUAlugm0L5NASP6dMx2DEmSJDUSS3QGlJRV8uV7ZrG+pJx5KzYxZWi3bEeSJElSI/JmKxmwYPVmHp6zjJWbtjKmX2dO2LtvtiNJkiSpETkSnUFfO3ovjh1rgZYkSWppLNGNqKKyig/WbGHRmi3ZjiJJkqQMskQ3kgWrNnPJn2cy58MN1fvaFORnMZEkSZIyxRLdCB6Zs4yv/uU18vMCPzplDJ3bFdG2II8Dh/fIdjRJkiRlgCW6EXzzvjcY0LWY350/hf5dirMdR5IkSRnm6hyNoKSskoOG97BAS5IktRKW6N308OyllJRXMrBbu2xHkSRJUhOxRO+GNZvL+M7fZzOmXyc+MXVQtuNIkiSpiWS0RIcQjg0hzA0hzAshXFnH8RBCuDF5/PUQwsRM5mlsP3hgDutLyrnu4+MpzPffI5IkSa1FxppfCCEfuAk4DhgNfCKEMLrWaccBw5O/LgB+k6k8jamyKvKLx9/hgdc+5LLDhzOqb6dsR5IkSVITyuTqHFOBeTHG+QAhhLuBU4A3a5xzCvCHGGMEXgghdAkh9I0xLs1grrSVVVTx7oqNAFRURq59ZC7T563itH368/8O3TPL6SRJktTUMlmi+wOLamwvBqalcE5/IKdK9KpNWznhxunV220K8vjp6Xtz5uSBhBCymEySlIvKy8tZvHgxpaWl2Y4iKQVt27ZlwIABFBYWpvyYTJboutpl3IVzCCFcQGK6B4MGNf0H+Lq2K+KWT02q3h7VpxODursahySpbosXL6Zjx44MGTLEwRYpx8UYWb16NYsXL2bo0KEpPy6TJXoxMLDG9gDgw104hxjjrcCtAJMnT96hZGdacVE+x4zp09QvK0lqpkpLSy3QUjMRQqB79+6sXLkyrcdlckmJl4HhIYShIYQi4GzggVrnPACcl1ylY19gfa7Nh5YkaVdYoKXmY1f+vmasRMcYK4BLgUeAt4C/xBjnhBAuCiFclDztIWA+MA/4LXBxpvJIktSaLF++nHPOOYc99tiDSZMmsd9++3H//ffv1nP+4Ac/4LrrrgPge9/7Ho8//vguPc+sWbN46KGHqrfvuOMOevbsyYQJExgzZgxnnHEGW7Zs2a2s9b3eAw88wDXXXLPLz1deXs6VV17J8OHDGTt2LFOnTuXf//43AEOGDGHVqlW7nbl2zpUrVzJt2jT22WcfnnnmGY4//njWrVu3W8//pS99iaeffrp6e+XKlRQWFnLLLbdsd16HDh22277jjju49NJLq7f/8Ic/MHbsWMaMGcPo0aOr/4zsjocffpgRI0YwbNiwnV6rJ598ks6dOzNhwgQmTJjAVVddVX1syJAh7L333kyYMIHJkydX7//a177Gf//7393OB5mdzkGM8SESRbnmvptrfB2BSzKZQZKk1ibGyKmnnsqnP/1p/vznPwPwwQcf8MADtX8gDBUVFRQUpF8HahaWdM2aNYtXXnmF448/vnrfWWedxa9+9SsAzjnnHO655x4+85nP7PJr1Pd6J598MieffPIuP993v/tdli5dyuzZs2nTpg3Lly/nqaeeapSsNdXM+Z///IeRI0fy+9//HoCDDjooreeqrKwkPz+/envNmjW88MIL/OIXv6je99e//pV9992Xu+66iwsvvDCl5/33v//NL37xCx599FH69etHaWkpf/zjH9PKVlfWSy65hMcee4wBAwYwZcoUTj75ZEaPrr1ScuJ9ePDBB+t8nieeeIIePXpst++yyy7jC1/4AocffvhuZQTvWChJUovz3//+l6KiIi666KLqfYMHD+ayyy4DEiOJH//4xznppJM4+uij2bRpE0cccQQTJ05k77335h//+Ef14/7nf/6HESNGcOSRRzJ37tzq/eeffz733nsvADNmzOCQQw5h0qRJHHPMMSxdmpiZeeihh3LFFVcwdepU9tprL5555hnKysr43ve+xz333MOECRO45557tsteUVHB5s2b6dq1K5Ao/0cccQTjxo3jiCOOYOHChfXu/+tf/8rYsWMZP348Bx98cJ2vV3Mk9fzzz+fyyy9n//33Z4899qj+nqqqqrj44osZM2YMJ554Iscffzz33nsvW7Zs4be//S2//OUvadOmDQC9e/fmzDPP3OE6nHrqqUyaNIkxY8Zw6623AomCeP755zN27Fj23ntv/vd//xeAG2+8kdGjRzNu3DjOPvvs6ut06aWXMmvWLL7xjW/w0EMPMWHCBEpKSrYb8f7Tn/7E1KlTmTBhAhdeeCGVlZVAYgT5e9/7HtOmTeP555/fLtu9997Lscceu92+u+66i+uvv57FixezZMmSOv5k7ejqq6/muuuuo1+/fkBilYsvfOELKT12Z1566SWGDRvGHnvsQVFREWefffZ2fyZ3x+DBg1m9ejXLli3b7efK6Ei0JEmt3Q//OYc3P9zQqM85ul8nvn/SmJ0enzNnDhMn1n8T4Oeff57XX3+dbt26UVFRwf3330+nTp1YtWoV++67LyeffDIzZ87k7rvv5tVXX6WiooKJEycyadKk7Z6nvLycyy67jH/84x/07NmTe+65h29/+9vcfvvtQKIUv/TSSzz00EP88Ic/5PHHH+eqq67ilVdeqR55vuOOO7jnnnuYPn06S5cuZa+99uKkk04C4NJLL+W8887j05/+NLfffjuXX345f//733e6/6qrruKRRx6hf//+rFu3jqKiojpfr6alS5cyffp03n77bU4++WTOOOMM7rvvPhYsWMAbb7zBihUrGDVqFJ/97GeZN28egwYNolOnhm+0dvvtt9OtWzdKSkqYMmUKp59+OgsWLGDJkiXMnj0boHpKxjXXXMP7779PmzZtdpimsW2qQs3vYZu33nqLe+65h2effZbCwkIuvvhi7rzzTs477zw2b97M2LFj6/ypwbPPPssZZ5xRvb1o0SKWLVvG1KlTOfPMM7nnnnv4yle+0uD3OHv27B3+TNTlzjvv5Nprr91h/7Bhw6r/4bLNkiVLGDjwo3UnBgwYwIsvvljn8z7//POMHz+efv36cd111zFmTOLvRQiBo48+mhACF154IRdccEH1YyZOnMizzz7L6aef3mDu+liiJUlq4S655BKmT59OUVERL7/8MgBHHXUU3bp1AxLTP771rW/x9NNPk5eXx5IlS1i+fDnPPPMMp512Gu3aJZZ1rWsKxNy5c5k9ezZHHXUUkBhp7du3b/Xxj33sYwBMmjSJBQsW7DTjtukcMUYuueQSrr32Wq688kqef/557rvvPgA+9alP8Y1vfANgp/sPOOAAzj//fM4888zq127IqaeeSl5eHqNHj2b58uUATJ8+nY9//OPk5eXRp08fDjvssJSeq6Ybb7yxeh76okWLePfddxkxYgTz58/nsssu44QTTuDoo48GYNy4cZx77rmceuqpnHrqqSm/xn/+8x9mzJjBlClTACgpKaFXr14A5Ofn77QoLl26lJ49e1Zv33333dWj6WeffTaf+9zn6i3R6X4Q79xzz+Xcc89N6dzEbN+GX2/ixIl88MEHdOjQgYceeohTTz2Vd999F0j8I6Ffv36sWLGCo446ipEjR3LwwQcD0KtXLz78cIfF4NJmiZYkKYPqGzHOlDFjxvC3v/2tevumm25i1apV233Aqn379tVf33nnnaxcuZIZM2ZQWFjIkCFDqm8U01BZijEyZsyYHaYLbLNtykN+fj4VFRUNZg8hcNJJJ/HLX/6SK6+8ss7jO3scwM0338yLL77Iv/71LyZMmMCsWbMafM1tGeGjAldXkYPEyOnChQvZuHEjHTt23OlzPvnkkzz++OM8//zztGvXjkMPPZTS0lK6du3Ka6+9xiOPPMJNN93EX/7yF26//Xb+9a9/8fTTT/PAAw/wox/9iDlz5jSYe1vOT3/601x99dU7HGvbtu1286BrKi4u3u5mQHfddRfLly/nzjvvBODDDz/k3XffZfjw4RQXF1NWVkZRURGQmE+9ba7xmDFjmDFjRoNzjNMZiR4wYACLFn10L77FixdXTxepqeZPA44//nguvvhiVq1aRY8eParP79WrF6eddhovvfRSdYkuLS2luLi43rypcE60JEktzOGHH05paSm/+c1vqvfVt9rF+vXr6dWrF4WFhTzxxBN88MEHABx88MHcf//9lJSUsHHjRv75z3/u8NgRI0awcuXK6hJdXl7eYAHs2LEjGzdu3Onx6dOns+eeewKw//77c/fddwOJInbggQfWu/+9995j2rRpXHXVVfTo0YNFixY1+Hp1OfDAA/nb3/5GVVUVy5cv58knnwSgXbt2fO5zn+Pyyy+nrKwMSIzq/ulPf9ru8evXr6dr1660a9eOt99+mxdeeAGAVatWUVVVxemnn86PfvQjZs6cSVVVFYsWLeKwww7jZz/7GevWrWPTpk0p5TziiCO49957WbFiBZAouNuuX31GjRrFvHnzgMRPEzZv3sySJUtYsGABCxYs4Jvf/Gb1+3vIIYdUf38lJSX85S9/qR6Z/+Y3v8k3vvGN6jnGW7du5cYbb9zh9c4991xmzZq1w6/aBRpgypQpvPvuu7z//vuUlZVx99131/lTkGXLllX/Y+ell16iqqqK7t27s3nz5urrvXnzZh599FHGjh1b/bh33nlnu+1d5Ui0JEktTAiBv//973z5y1/mZz/7GT179qR9+/b89Kc/rfP8c889l5NOOonJkyczYcIERo4cCSR+XH7WWWcxYcIEBg8eXOeKEEVFRdx7771cfvnlrF+/noqKCr70pS9Vz02ty2GHHcY111zDhAkT+OY3vwlQPSe6qqqKAQMGVM9bvvHGG/nsZz/LtddeS8+ePfm///u/evd//etf59133yXGyBFHHMH48eMZNGjQDq/XkNNPP53//Oc/jB07lr322otp06bRuXNnAH784x/zne98h9GjR9O2bVvat2+/w7zjY489lptvvplx48YxYsQI9t13XyAx3/czn/kMVVVVQOKDeZWVlXzyk59k/fr1xBj58pe/TJcuXVLKOXr0aH784x9z9NFHU1VVRWFhITfddBODBw+u93EnnHACt9xyC5///Oe56667OO2003b4/s8++2y++93vcsMNN3DhhRdy4403EmPkvPPOqx7VPf7441m+fDlHHnkkMUZCCHz2s59NKfvOFBQU8Ktf/YpjjjmGyspKPvvZz1b/ebr55sQibxdddBH33nsvv/nNbygoKKC4uJi7776bEALLly+v/n4qKio455xzqj9EWV5ezrx587b7qcyuCjv7cUWumjx5cnzllVeyHUOSpJ166623GDVqVLZjaDdt2rSJDh06sHr1aqZOncqzzz5Lnz4t5w7GBx54IA8++GDKhb0luP/++5k5cyY/+tGPdjhW19/bEMKMGGOdjduRaEmSpDqceOKJrFu3jrKyMr773e+2qAINcP3117Nw4cJWVaIrKir46le/2ijPZYmWJEmqw7Z50C3VtGnTsh2hyX384x9vtOfyg4WSJElSmizRkiRlQHP7zJHUmu3K31dLtCRJjaxt27asXr3aIi01AzFGVq9eTdu2bdN6nHOiJUlqZAMGDGDx4sWsXLky21EkpaBt27YMGDAgrcdYoiVJamSFhYUMHTo02zEkZZDTOSRJkqQ0WaIlSZKkNFmiJUmSpDQ1u9t+hxBWAh9k6eV7AKuy9NpqGl7j1sHr3Dp4nVsHr3PLl81rPDjG2LOuA82uRGdTCOGVnd0/XS2D17h18Dq3Dl7n1sHr3PLl6jV2OockSZKUJku0JEmSlCZLdHpuzXYAZZzXuHXwOrcOXufWwevc8uXkNXZOtCRJkpQmR6IlSZKkNFmiawkhHBtCmBtCmBdCuLKO4yGEcGPy+OshhInZyKndk8J1Pjd5fV8PITwXQhifjZzaPQ1d5xrnTQkhVIYQzmjKfNp9qVzjEMKhIYRZIYQ5IYSnmjqjdl8K/83uHEL4ZwjhteR1/kw2cmrXhRBuDyGsCCHM3snxnOtflugaQgj5wE3AccBo4BMhhNG1TjsOGJ78dQHwmyYNqd2W4nV+HzgkxjgO+BE5Oh9LO5fidd523k+BR5o2oXZXKtc4hNAF+DVwcoxxDPDxps6p3ZPi3+VLgDdjjOOBQ4HrQwhFTRpUu+sO4Nh6judc/7JEb28qMC/GOD/GWAbcDZxS65xTgD/EhBeALiGEvk0dVLulwescY3wuxrg2ufkCMKCJM2r3pfL3GeAy4G/AiqYMp0aRyjU+B7gvxrgQIMbodW5+UrnOEegYQghAB2ANUNG0MbU7YoxPk7huO5Nz/csSvb3+wKIa24uT+9I9R7kt3Wv4OeDfGU2kTGjwOocQ+gOnATc3YS41nlT+Lu8FdA0hPBlCmBFCOK/J0qmxpHKdfwWMAj4E3gC+GGOsapp4aiI5178KsvniOSjUsa/28iWpnKPclvI1DCEcRqJEH5jRRMqEVK7zL4ArYoyViQEsNTOpXOMCYBJwBFAMPB9CeCHG+E6mw6nRpHKdjwFmAYcDewKPhRCeiTFuyHA2NZ2c61+W6O0tBgbW2B5A4l+16Z6j3JbSNQwhjANuA46LMa5uomxqPKlc58nA3ckC3QM4PoRQEWP8e5Mk1O5K9b/Zq2KMm4HNIYSngfGAJbr5SOU6fwa4JibW7Z0XQngfGAm81DQR1QRyrn85nWN7LwPDQwhDkx9IOBt4oNY5DwDnJT8lui+wPsa4tKmDarc0eJ1DCIOA+4BPOWLVbDV4nWOMQ2OMQ2KMQ4B7gYst0M1KKv/N/gdwUAihIITQDpgGvNXEObV7UrnOC0n8tIEQQm9gBDC/SVMq03KufzkSXUOMsSKEcCmJT+nnA7fHGOeEEC5KHr8ZeAg4HpgHbCHxr181Iyle5+8B3YFfJ0cpK2KMk7OVWelL8TqrGUvlGscY3wohPAy8DlQBt8UY61xCS7kpxb/LPwLuCCG8QeLH/lfEGFdlLbTSFkK4i8TKKj1CCIuB7wOFkLv9yzsWSpIkSWlyOockSZKUJku0JEmSlCZLtCRJkpQmS7QkSZKUJku0JEmSlCZLtCRJkpQmS7SkrAohVIYQZtX4NaSeczc1wuvdEUJ4P/laM0MI++3Cc9wWQhid/PpbtY49t7sZk8+z7X2ZHUL4ZwihSwPnTwghHL8Lr9M3hPBg8utDQwjrQwivhhDeCiF8fxee7+QQwpXJr0/d9j4lt68KIRyZ7nPW8Rp3hBDOaOCcJ0MIKa/tnvzeH0zhvNtDCCtCCLNr7b8uhHB4qq8nqfmzREvKtpIY44QavxY0wWt+PcY4AbgSuCXdB8cYPx9jfDO5+a1ax/bf/XjAR+/LWGANcEkD508gcSOCdH0F+G2N7WdijPuQuCX6J0MIk9J5shjjAzHGa5KbpwKjaxz7Xozx8V3ImEvuAI6tY/8vSfx5ktRKWKIl5ZQQQocQwn+So8RvhBBOqeOcviGEp2uM1B6U3H90COH55GP/GkLo0MDLPQ0MSz72K8nnmh1C+FJyX/sQwr9CCK8l95+V3P9kCGFyCOEaoDiZ487ksU3J3++pOTKcHD09PYSQH0K4NoTwcgjh9RDChSm8Lc8D/ZPPMzWE8FxytPi5EMKI5K2QrwLOSmY5K5n99uTrvFrX+5h0OvBw7Z0xxs3ADGDP5Cj3C8m894cQuiazXB5CeDO5/+7kvvNDCL8KIewPnAxcm8y057YR5BDCcSGEv9R4bw4NIfwz+XVa1zCE8L3k9zg7hHBrCIlbjCZ9MvkezQ4hTE2en+r7UqcY49Mk/lFTe/8HQPcQQp90nk9S82WJlpRt20rorBDC/UApcFqMcSJwGHB9rWIEcA7wSHI0eTwwK4TQA/gOcGTysa+QGGWtz0nAG8nR1s8A04B9gS+EEPYhMeL4YYxxfHJEeLuyGWO8ko9GjM+t9dx3A9tKdxFwBInb1n4OWB9jnAJMSb7W0J0FDCHkJx/7QHLX28DBydHi7wE/iTGWJb++J5nlHuDbwH+Tr3MYiTLbvtZzDwXWxhi31vG63ZPvxRzgDyRuozwOeIPE7XghMfK6T3L/RbXem+eSmb+ezPRejcOPAfvWyHMWcM8uXsNfxRinJK9PMXBijWPtkz8ZuBi4PbkvlfdlcgjhtgZety4zgQN24XGSmqGCbAeQ1OqVJMswACGEQuAnIYSDgSoSI7C9gWU1HvMycHvy3L/HGGeFEA4hMXXg2WTnLiIxgluXa0MI3wFWkii1RwD3J0dfCSHcBxxEojRfF0L4KfBgjPGZNL6vfwM3hhDakCjjT8cYS0IIRwPjwkdzejsDw4H3az2+OIQwCxhCYkT4sRrn/z6EMByIQOFOXv9o4OQQwteS222BQcBbNc7pm3wPajoohPAqiff+GmAx0CXG+FTy+O+Bvya/fh24M4Twd+DvO8mxgxhjRQjhYeCkEMK9wAnAN4B0ruE2h4UQvgG0A7qRKP3/TB67K/l6T4cQOoXEvPKdvS81870CfD7V76eGFUC/XXicpGbIEi0p15wL9AQmxRjLQwgLSBSdaslSdDCJ8vXHEMK1wFrgsRjjJ1J4ja/HGO/dthF28mG3GOM7yVHq44GrQwiPxhivSuWbiDGWhhCeBI4hMdJ617aXAy6LMT7SwFOUxBgnhBA6Aw+SmBN9I/Aj4IkY42kh8SHMJ3fy+ACcHmOcW99rUOu9JTEnuno0N/n6O3MCcDCJaRvfDSGMqefc2u4h8T2tAV6OMW5M/sQh1WtICKEt8GtgcoxxUQjhB2z//cRaD4ns5H0JIfROI/vOtCXxnkpqBZzOISnXdAZWJAv0YcDg2ieEEAYnz/kt8DtgIvACcEAIYdsc53YhhL1SfM2ngVOTj2kPnAY8E0LoB2yJMf4JuC75OrWVJ0fE63I3iWkiBwHbSvMjwP/b9pgQwl61pxPUFGNcD1wOfC35mM7AkuTh82ucuhHoWGP7EeCybVNhktNTanuHxEj3TiVff21IzjsHPgU8FULIAwbGGJ8gMYrcBag9f7l2ppqeJPF+foFEoYb0r+G2wrwqOXe69ood26bTHEhiCs16UntfdtVewOwGz5LUIliiJeWaO4HJIYRXSIxKv13HOYeSmAf9KokPxt0QY1xJolTeFUJ4nUQhG5nKC8YYZ5JYdeEl4EXgthjjq8DewEvJaRXfBn5cx8NvBV4PyQ8W1vIoiZHax5PzlgFuA94EZobEMmm30MBPBZNZXgPOBn5GYlT8WSC/xmlPAKOTc8vPIjFiXZjMNju5Xft5NwPvbSut9fg0iSkwr5NYBeSq5Gv/KYTwBvAq8L8xxnW1Hnc38PXkB/j2rPXalSRG2I9L/k661zD5er8lMU/77ySm+dS0NiSWHLyZxLQdSOF9qW9OdAjhLhJTTEaEEBaHED6X3F9I4kOqr+wsr6SWJcRY+6ddkqTWIoRwGompM9/JdpbmLPk+TowxfjfbWSQ1DedES1IrFmO8P7kSh3ZPAXB9tkNIajqOREuSJElpck60JEmSlCZLtCRJkpQmS7QkSZKUJku0JEmSlCZLtCRJkpSm/w+aduG8ZxajiQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "gb = GradientBoostingClassifier(random_state=RANDOM_SEED)\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "plot_roc_curve(gb, X_val, y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d826b899",
   "metadata": {},
   "source": [
    "ndeed it does, the gradient boosting classifier outperform previous models and scored 0.55 AUC!\n",
    "\n",
    "We find out that gradient boosting works best for this data set. In the next section, we will train a deep learning model with the aim to outperform the baseline set here, i.e., AUC = 0.55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ff54f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8a4297b2",
   "metadata": {},
   "source": [
    "## Deep Learning Algorithm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbad8e0c",
   "metadata": {},
   "source": [
    "We are going to use Tensorflow for implementing a small neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fc5de5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-06 13:37:07.926507: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow version 2.13.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# set seed for reproducibility of results\n",
    "tf.random.set_seed(RANDOM_SEED)\n",
    "\n",
    "print(\"Tensorflow version\", tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16c60dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Normalization(axis=-1),\n",
    "    tf.keras.layers.Dense(10, input_shape=[X_train.shape[1],], activation=\"relu\", kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "    tf.keras.layers.Dropout(0.2, seed=RANDOM_SEED),\n",
    "    tf.keras.layers.Dense(5, activation=\"relu\", kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "    tf.keras.layers.Dense(1, activation=\"sigmoid\", kernel_initializer='random_normal', bias_initializer='zeros')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25c46ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this configures the model's loss function, weight optimizer, and metrics to keep track of\n",
    "model.compile(\n",
    "    loss=\"binary_crossentropy\", \n",
    "    optimizer=tf.keras.optimizers.Adam(), \n",
    "    metrics=['AUC']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcbc545",
   "metadata": {},
   "source": [
    "We are going to train our model for 50 epochs, and keep track of its loss (binary_crossentropy) and AUC values during each epoch.\n",
    "\n",
    "To prevent overfitting, we are implementing two callbacks: (1) learning rate scheduler, and (2) early stopping callback."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4d45246b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def learning_rate_scheduler(epoch, learning_rate):\n",
    "    \"\"\"Learning rate decay callback.\"\"\"\n",
    "    if epoch < 5:\n",
    "        return learning_rate\n",
    "    else:\n",
    "        return learning_rate * tf.math.exp(-0.01)\n",
    "\n",
    "early_stopping_callback = tf.keras.callbacks.EarlyStopping(patience=10, verbose=1, restore_best_weights=True)\n",
    "learning_rate_callback = tf.keras.callbacks.LearningRateScheduler(learning_rate_scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cec47425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "150/150 [==============================] - 1s 3ms/step - loss: 39.5778 - auc: 0.5091 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 3.4778 - auc: 0.5086 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 1.6181 - auc: 0.5058 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 1.3683 - auc: 0.4928 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 1.2592 - auc: 0.5019 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 0.8453 - auc: 0.5012 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 9.9005e-04\n",
      "Epoch 7/50\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 0.8038 - auc: 0.5150 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 9.8020e-04\n",
      "Epoch 8/50\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 0.7266 - auc: 0.4891 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 9.7045e-04\n",
      "Epoch 9/50\n",
      "150/150 [==============================] - 0s 1ms/step - loss: 0.7072 - auc: 0.4954 - val_loss: 0.6931 - val_auc: 0.5000 - lr: 9.6079e-04\n",
      "Epoch 10/50\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 0.7048 - auc: 0.4978 - val_loss: 0.6932 - val_auc: 0.5000 - lr: 9.5123e-04\n",
      "Epoch 11/50\n",
      "128/150 [========================>.....] - ETA: 0s - loss: 0.7002 - auc: 0.4956Restoring model weights from the end of the best epoch: 1.\n",
      "150/150 [==============================] - 0s 2ms/step - loss: 0.6998 - auc: 0.4938 - val_loss: 0.6932 - val_auc: 0.5000 - lr: 9.4176e-04\n",
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "# the model.fit(...) method returns a 'history' object with stats about the training\n",
    "history = model.fit(\n",
    "    x=X_train,\n",
    "    y=y_train,\n",
    "    validation_data=(X_val, y_val),\n",
    "    epochs=50,\n",
    "    verbose=1,\n",
    "    callbacks=[early_stopping_callback, learning_rate_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899a6ac0",
   "metadata": {},
   "source": [
    "We observe that both the training and the validation AUC are very close to 0.5, improving the overall score only over the logistic regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4b0b6ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization (Normalizati  (None, 10)                21        \n",
      " on)                                                             \n",
      "                                                                 \n",
      " dense (Dense)               (None, 10)                110       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 10)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 5)                 55        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 192 (772.00 Byte)\n",
      "Trainable params: 171 (684.00 Byte)\n",
      "Non-trainable params: 21 (88.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ce1ad2",
   "metadata": {},
   "source": [
    "Below, we plot the train and validation loss and AUC values as a function of the epoch. We use the matplotlib library, and the history object to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "429cc2ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(history.history['loss'], label='loss')\n",
    "plt.plot(history.history['val_loss'], label='val_loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Error [Price]')\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b58e220f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['auc'], label='auc')\n",
    "plt.plot(history.history['val_auc'], label='val_auc')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Error [Price]')\n",
    "plt.legend()\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de67ebc",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af5c445d",
   "metadata": {},
   "source": [
    "The gradient boosting classifier provided the best AUC score on the validation set. It is a common machine learning practice to train multiple models on the same train/validation data set and provide a model that works best. To simulate a production environment, we have held the test set aside until now.\n",
    "\n",
    "In the next cell, we are going to evaluate the performance of the gradient boosting classifier on the test set. This is simple as calling plot_roc_curve with the test set instead of the validation one.\n",
    "\n",
    "As a bonus, in the last cell, we are showing a feature importance plot, which plots the importance of each feature in regard to the predictive performance of the model (the higher the value the more important the feature is for determining the value of the target variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ced090d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fba053a2eb0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(gb, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3284b454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Calculate feature importances\n",
    "importances = gb.feature_importances_\n",
    "# Sort feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Rearrange feature names so they match the sorted feature importances\n",
    "names = [df_train[X_cols].columns[i] for i in indices]\n",
    "\n",
    "_ = plt.figure(figsize=(9, 7))\n",
    "plt.bar(names, importances[indices])\n",
    "_ = plt.title(\"Feature importance\")\n",
    "_ = plt.xticks(rotation=20, fontsize = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9010c24",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}