{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "C297HhYulXcb",
    "outputId": "d6e2a9df-586e-4192-b8ec-1e7b7025c0c3"
   },
   "outputs": [],
   "source": [
    "#importing basic packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "id": "fVPglpaf4REa",
    "outputId": "eef4a4ca-e12d-4cd3-e011-20376fc752a2"
   },
   "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>Domain</th>\n",
       "      <th>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>DNS_Record</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
       "      <th>Web_Forwards</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>graphicriver.net</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ecnavi.jp</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hubpages.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>extratorrent.cc</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>icicibank.com</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Domain  Have_IP  Have_At  URL_Length  URL_Depth  Redirection  \\\n",
       "0  graphicriver.net        0        0           1          1            0   \n",
       "1         ecnavi.jp        0        0           1          1            1   \n",
       "2      hubpages.com        0        0           1          1            0   \n",
       "3   extratorrent.cc        0        0           1          3            0   \n",
       "4     icicibank.com        0        0           1          3            0   \n",
       "\n",
       "   https_Domain  TinyURL  Prefix/Suffix  DNS_Record  Web_Traffic  Domain_Age  \\\n",
       "0             0        0              0           0            1           1   \n",
       "1             0        0              0           0            1           1   \n",
       "2             0        0              0           0            1           0   \n",
       "3             0        0              0           0            1           0   \n",
       "4             0        0              0           0            1           0   \n",
       "\n",
       "   Domain_End  iFrame  Mouse_Over  Right_Click  Web_Forwards  Label  \n",
       "0           1       0           0            1             0      0  \n",
       "1           1       0           0            1             0      0  \n",
       "2           1       0           0            1             0      0  \n",
       "3           1       0           0            1             0      0  \n",
       "4           1       0           0            1             0      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "data0 = pd.read_csv(\"5.urldata.csv\")\n",
    "data0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 879
    },
    "colab_type": "code",
    "id": "N9K0yAdAM70w",
    "outputId": "05687b93-945e-4fee-c3da-baae065ad528"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data0.hist(bins = 50,figsize = (15,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tdpRw0Bcn_K1"
   },
   "outputs": [],
   "source": [
    "#Dropping the Domain column\n",
    "data = data0.drop(['Domain'], axis = 1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "colab_type": "code",
    "id": "4LZnaoU_qBsz",
    "outputId": "df212692-ea66-4d67-a4aa-00a256010f69"
   },
   "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>Have_IP</th>\n",
       "      <th>Have_At</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>URL_Depth</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>https_Domain</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>Prefix/Suffix</th>\n",
       "      <th>DNS_Record</th>\n",
       "      <th>Web_Traffic</th>\n",
       "      <th>Domain_Age</th>\n",
       "      <th>Domain_End</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>Mouse_Over</th>\n",
       "      <th>Right_Click</th>\n",
       "      <th>Web_Forwards</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Have_IP  Have_At  URL_Length  URL_Depth  Redirection  https_Domain  \\\n",
       "0        0        0           1          3            0             0   \n",
       "1        0        0           0          0            0             0   \n",
       "2        0        0           0          2            0             0   \n",
       "3        0        0           0          0            0             0   \n",
       "4        0        0           0          4            0             0   \n",
       "\n",
       "   TinyURL  Prefix/Suffix  DNS_Record  Web_Traffic  Domain_Age  Domain_End  \\\n",
       "0        0              0           0            1           0           1   \n",
       "1        0              1           0            0           0           1   \n",
       "2        0              0           0            1           0           0   \n",
       "3        1              0           0            1           0           1   \n",
       "4        0              0           1            1           1           1   \n",
       "\n",
       "   iFrame  Mouse_Over  Right_Click  Web_Forwards  Label  \n",
       "0       0           0            1             0      0  \n",
       "1       0           0            1             0      1  \n",
       "2       0           0            1             0      1  \n",
       "3       0           0            1             0      1  \n",
       "4       0           0            1             0      1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data = data.sample(frac=1).reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "FzEU-wcLN8K7",
    "outputId": "534f9839-31e6-4b19-b469-c16db57fd5a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 16), (10000,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sepratating & assigning features and target columns to X & y\n",
    "y = data['Label']\n",
    "X = data.drop('Label',axis=1).values\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1, ..., 0, 1, 0],\n",
       "       [0, 0, 0, ..., 0, 1, 0],\n",
       "       [0, 0, 0, ..., 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 1, ..., 0, 1, 0],\n",
       "       [0, 0, 1, ..., 0, 1, 0],\n",
       "       [0, 0, 1, ..., 0, 1, 0]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "84xKobSqAV3U",
    "outputId": "20c0a9f7-d20e-4176-f815-238727c44336"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8000, 16), (2000, 16))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets: 80-20 split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size = 0.2, random_state = 12)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D5Tg_ei0-xPU"
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DPBHdBikSXHv"
   },
   "outputs": [],
   "source": [
    "\n",
    "ML_Model = []\n",
    "acc_train = []\n",
    "acc_test = []\n",
    "\n",
    "def storeResults(model, a,b):\n",
    "  ML_Model.append(model)\n",
    "  acc_train.append(round(a, 3))\n",
    "  acc_test.append(round(b, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 117
    },
    "colab_type": "code",
    "id": "1kzsjtudy-0w",
    "outputId": "80b84eba-eeb1-48d1-d95a-412b7cfb4c45"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(max_depth = 5)\n",
    "# fit the model \n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cpPk7O-MrTZi"
   },
   "outputs": [],
   "source": [
    "y_test_tree = tree.predict(X_test)\n",
    "y_train_tree = tree.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kLn-_qOuS_9Y"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "X4wDTnFZrz3q",
    "outputId": "a8bf5873-8185-4f18-e0f0-87717975e5a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: Accuracy on training Data: 0.816\n",
      "Decision Tree: Accuracy on test Data: 0.802\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
    "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
    "\n",
    "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
    "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "LITrJdVGWwTl",
    "outputId": "363e0abd-28df-4703-b784-5f5af37cab30"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'columns'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[28], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m n_features \u001b[38;5;241m=\u001b[39m X_train\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mbarh(\u001b[38;5;28mrange\u001b[39m(n_features), tree\u001b[38;5;241m.\u001b[39mfeature_importances_, align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m plt\u001b[38;5;241m.\u001b[39myticks(np\u001b[38;5;241m.\u001b[39marange(n_features), \u001b[43mX_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m)\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature importance\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'columns'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XpC9PAn5RTfY"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5XKvXxr9RSxl"
   },
   "outputs": [],
   "source": [
    "\n",
    "storeResults('Decision Tree', acc_train_tree, acc_test_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "2fmB9rPSsR6y",
    "outputId": "27ddebf4-bee1-4eec-eb4e-995d4cdc08b2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest = RandomForestClassifier(max_depth=5)\n",
    "\n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J1Qck-wrsabB"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_forest = forest.predict(X_test)\n",
    "y_train_forest = forest.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i8TybBPHT1ao"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "Oguf-37tsboO",
    "outputId": "34386ec6-a7f0-4185-b3c0-a40de3239fb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: Accuracy on training Data: 0.820\n",
      "Random forest: Accuracy on test Data: 0.804\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_forest = accuracy_score(y_train,y_train_forest)\n",
    "acc_test_forest = accuracy_score(y_test,y_test_forest)\n",
    "\n",
    "print(\"Random forest: Accuracy on training Data: {:.3f}\".format(acc_train_forest))\n",
    "print(\"Random forest: Accuracy on test Data: {:.3f}\".format(acc_test_forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "m9GZGxvZ9jnB",
    "outputId": "465186a8-d622-4427-c148-9dff349b40eb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XX6t48eI37BsZGanBgwfb9lNTUxUQEHBLdQIAAAD3qgL/nScPDw8FBwerZs2amjt3rr755hvNmTPnlsZ0dna227dYLDIMI9fjVa5cWSkpKbbvTeWGm5ubqX7NmjXTb7/9pk2bNt20r9Vqlbe3t90GAAAAFFYFPjz9k4ODg4YNG6YRI0bowoUL1xwPCQnRt99+a9e2b9++HF/HxcVFmZmZpvu3b99eLi4ueuutt7I9/u/FKrLj5eWlwMDAmy5d/vjjj+uTTz5Rr169tGTJEtM1AgAAAIVdoQpPktShQwc5Ojrq/fffv+bYiy++qB9//FFDhw7V0aNHtWzZMsXExEhSjl6rCwwM1A8//KCEhAT973//U0ZGxg37BwQEaMqUKZo2bZp69uyp7du365dfftGuXbv04osvauzYsaauO3r0aE2ePFnTp0/XsWPHtH//fr377rvX9HviiSe0cOFC9ejRQytWrDB9XwAAAEBhVujCk5OTk/r166e33npLf//9t92xoKAgrVixQqtWrVKNGjU0Y8YMDR8+XNKVV9jMev755xUSEqK6deuqRIkS2a549299+/bVF198od9++01PPPGEqlSpol69esnb2zvbFf+y061bN02dOlUffPCBqlWrplatWunYsWPZ9m3fvr3mz5+vZ599VqtWrTJ9bwAAAEBhZTFu5cs6hcD48eM1c+ZMnTx5Mr9LyXepqany8fFRSkoK338CAABAgZCTv3EL/Gp7OfXBBx+oXr16KlasmHbt2qW33377hj80CwAAAKBwKHSv7d3MsWPH1KZNG4WGhmrs2LF65ZVXNHr06Fsac8KECfL09Mx2a9GiRd4UDgAAAOC24rW9O+D06dM6ffp0tsfc3Nxu+EO1dxNe2wMAAEBBw2t7dxlfX1/5+vrmdxkAAAAAbgGv7QEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCTl236hN+V0CAAAAcMcRngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAm3LPhqXv37rJYLOrdu/c1x1566SVZLBZ17979zheWS4cPH1bHjh1VokQJWa1WVa5cWSNHjtT58+fzuzQAAAAAuofDkyQFBARoyZIlunDhgq3t4sWL+uSTT1SuXLl8rCxnvv76a9WvX1+XLl3SunXrdPToUY0fP14xMTFq1qyZLl26dNuubRiGLl++fNvGBwAAAAqKezo81alTRwEBAVq1apWtbdWqVSpXrpxq165ta0tPT1f//v1VsmRJubq66uGHH9a+fftsx2NiYlSkSBG7sT/77DNZLBbb/oEDBxQeHi4vLy95e3vr/vvv17fffms7vnPnTj3yyCNyc3NTQECA+vfvr7///vum92AYhnr27KmqVatq1apVeuCBB1S+fHl16NBBn3/+ufbs2aMpU6ZIkp555hl16tTJ7vyMjAwVL15cCxYskCRlZWUpOjpaQUFBcnNzU82aNbVixQpb/9jYWFksFm3YsEH333+/rFardu7cedM6AQAAgMLung5PkvTcc89p3rx5tv25c+eqR48edn1ee+01rVy5UvPnz9f+/fsVHBysiIgInT592vR1OnfurLJly2rfvn367rvv9Prrr8vZ2VmSlJiYqObNm6tdu3b64YcftHTpUu3cuVP9+vW76bhxcXE6cuSIBg8eLAcH+8dRs2ZNNW3aVIsXL7bV8PnnnystLc3WZ9OmTTp//ryeeOIJSVJ0dLQWLFigmTNn6vDhwxo0aJC6dOmi7du32439+uuva+LEiYqPj1eNGjWyrS09PV2pqal2GwAAAFBY3fPhqUuXLtq5c6d++eUX/fLLL9q1a5e6dOliO/73339rxowZevvtt9WiRQuFhobqo48+kpubm+bMmWP6OidOnFDTpk1VpUoVVapUSR06dFDNmjUlXQksnTt31sCBA1WpUiU1bNhQ06dP14IFC3Tx4sUbjnv06FFJUtWqVbM9XrVqVVufiIgIeXh46NNPP7Ud/+STT/T444/Ly8tL6enpmjBhgubOnauIiAhVqFBB3bt3V5cuXTRr1iy7cceMGaNmzZqpYsWK8vX1zfba0dHR8vHxsW0BAQHmPiwAAACgALrnw1OJEiXUsmVLxcTEaN68eWrZsqWKFy9uO56YmKiMjAw99NBDtjZnZ2c98MADio+PN32dwYMHq1evXmratKkmTpyoxMRE27EDBw4oJiZGnp6eti0iIkJZWVk6fvy4qfENw7hpHycnJ3Xs2FGLFi2SdCUYrl69Wp07d5Yk/fTTTzp//ryaNWtmV8uCBQvs6pWkunXr3vR6kZGRSklJsW0nT540dS8AAABAQeSU3wXkheeee872itz777+f4/MdHByuCS8ZGRl2+6NHj9YzzzyjdevWacOGDRo1apSWLFmiJ554QmlpaXrxxRfVv3//a8a+2cIVlStXliTFx8fbfU/rqvj4eFsf6cqre40bN9apU6e0efNmubm5qXnz5pJke51v3bp1KlOmjN04VqvVbt/Dw+OGdV0959/nAQAAAIXVPT/zJEnNmzfXpUuXlJGRoYiICLtjFStWlIuLi3bt2mVry8jI0L59+xQaGirpyuzVuXPn7BZ4iIuLu+Y6lStX1qBBg/TFF1/oySeftH3Xqk6dOjpy5IiCg4Ov2VxcXG5Ye61atVSlShVNmTJFWVlZdscOHDigLVu26Omnn7a1NWzYUAEBAVq6dKkWLVqkDh062L57FRoaKqvVqhMnTlxTB6/cAQAAALemQMw8OTo62l7Bc3R0tDvm4eGhPn366NVXX5Wvr6/KlSunt956S+fPn1fPnj0lSfXr15e7u7uGDRum/v3765tvvlFMTIxtjAsXLujVV19V+/btFRQUpF9//VX79u1Tu3btJElDhw7Vgw8+qH79+qlXr17y8PDQkSNHtHnzZr333ns3rN1isWjOnDlq1qyZ2rVrp8jISPn5+embb77RK6+8ogYNGmjgwIF25zzzzDOaOXOmjh49qm3bttnavby8NGTIEA0aNEhZWVl6+OGHlZKSol27dsnb21vdunXL7UcMAAAAFHoFYuZJkry9veXt7Z3tsYkTJ6pdu3Z69tlnVadOHf3000/atGmTihYtKkny9fXVxx9/rPXr16t69epavHixRo8ebTvf0dFRf/31l7p27arKlSurY8eOatGihaKioiRJNWrU0Pbt23X06FE98sgjql27tkaOHKnSpUubqr1hw4b6+uuv5ejoqBYtWig4OFiRkZHq1q2bNm/efM2rc507d9aRI0dUpkwZu+9ySdLYsWP1xhtvKDo6WlWrVlXz5s21bt06BQUFmf0oAQAAAGTDYphZqQCQlJqaemXVvYHLdGJKh/wuBwAAALhlV//GTUlJue5kzFUFZuYJAAAAAG4nwtNttmPHDrtlw/+9AQAAALg3FIgFI+5mdevWzXblPgAAAAD3FsLTbebm5qbg4OD8LgMAAADALeK1PQAAAAAwgfCEHDsUFXHzTgAAAEABQ3gCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB33lCjt03apMcrO45OidpYsvbVA0AAABwZzDzBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPOWxsLAwDRw4ML/LAAAAAJDH+J2nPLZq1So5OztLuhKktm/ffk2fjIwMOTnx0QMAAAD3Emae8pivr6+8vLxs+88//7ySk5PttuyC06VLl+5kmQAAAAByiPCUx/792p67u7v8/PzsNkkKDAzU2LFj1bVrV3l7e+uFF16QJA0dOlSVK1eWu7u7KlSooDfeeEMZGRm28UaPHq1atWpp7ty5KleunDw9PdW3b19lZmbqrbfekp+fn0qWLKnx48fb1XX27Fn16tVLJUqUkLe3t5o0aaIDBw7c/g8EAAAAKCB4dywfTZo0SSNHjtSoUaNsbV5eXoqJiVHp0qV18OBBPf/88/Ly8tJrr71m65OYmKgNGzZo48aNSkxMVPv27fXzzz+rcuXK2r59u3bv3q3nnntOTZs2Vf369SVJHTp0kJubmzZs2CAfHx/NmjVLjz76qI4ePSpfX987fu8AAADAvYaZp9vsgw8+kKenp2175ZVXbMeaNGmiV155RRUrVlTFihUlSSNGjFDDhg0VGBio1q1ba8iQIVq2bJndmFlZWZo7d65CQ0PVunVrhYeHKyEhQVOnTlVISIh69OihkJAQbdu2TZK0c+dO7d27V8uXL1fdunVVqVIlTZo0SUWKFNGKFSuuW3t6erpSU1PtNgAAAKCwYubpNuvcubOGDx9u2y9SpIjt33Xr1r2m/9KlSzV9+nQlJiYqLS1Nly9flre3t12fwMBAu+9VlSpVSo6OjnJwcLBrO3XqlCTpwIEDSktLU7FixezGuXDhghITE69be3R0tKKioszdKAAAAFDAEZ5uMx8fHwUHB2d7zMPDw25/z5496ty5s6KiohQRESEfHx8tWbJEkydPtut3dTW/qywWS7ZtWVlZkqS0tDT5+/srNjb2mhr+Geb+LTIyUoMHD7btp6amKiAg4Lr9AQAAgIKM8HQX2b17t8qXL283U/XLL7/c8rh16tTRH3/8IScnJwUGBpo+z2q1ymq13vL1AQAAgIKA7zzdRSpVqqQTJ05oyZIlSkxM1PTp0/Xpp5/e8rhNmzZVgwYN1LZtW33xxRdKSkrS7t27NXz4cH377bd5UDkAAABQ8BGe7iKPP/64Bg0apH79+qlWrVravXu33njjjVse12KxaP369WrUqJF69OihypUr66mnntIvv/yiUqVK5UHlAAAAQMFnMQzDyO8icG9ITU2Vj4+PAgYuk4PVPUfnJk1seZuqAgAAAHLv6t+4KSkp1yzU9m/MPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATnPK7ANx7DkVF3HQZRwAAAKCgYeYJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEVttDjt03apMcrO437JM0seUdqgYAAAC4M5h5AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMJTHouJiVGRIkXyu4ybio2NlcVi0dmzZ/O7FAAAAOCecM+Fp+7du8tischiscjZ2VmlSpVSs2bNNHfuXGVlZeV3eerUqZOOHj2aZ+P9837/uTVv3jzPrgEAAADg5pzyu4DcaN68uebNm6fMzEz9+eef2rhxowYMGKAVK1ZozZo1cnLKv9tyc3OTm5tbno559X7/yWq15uk1AAAAANzYPTfzJF0JDn5+fipTpozq1KmjYcOGafXq1dqwYYNiYmIkSSdOnFCbNm3k6ekpb29vdezYUX/++adtjNGjR6tWrVqaO3euypUrJ09PT/Xt21eZmZl666235Ofnp5IlS2r8+PF2137nnXdUvXp1eXh4KCAgQH379lVaWprt+L9f27t6nYULFyowMFA+Pj566qmndO7cuRzf7z+3okWL2o5bLBbNnj1bTzzxhNzd3VWpUiWtWbPGboz169ercuXKcnNzU3h4uJKSkkxfHwAAAMA9Gp6y06RJE9WsWVOrVq1SVlaW2rRpo9OnT2v79u3avHmzfv75Z3Xq1MnunMTERG3YsEEbN27U4sWLNWfOHLVs2VK//vqrtm/frjfffFMjRozQN998YzvHwcFB06dP1+HDhzV//nx9+eWXeu21125YW2Jioj777DOtXbtWa9eu1fbt2zVx4sQ8vf+oqCh17NhRP/zwgx577DF17txZp0+fliSdPHlSTz75pFq3bq24uDj16tVLr7/++k3HTE9PV2pqqt0GAAAAFFYFJjxJUpUqVZSUlKStW7fq4MGD+uSTT3T//ferfv36WrBggbZv3659+/bZ+mdlZWnu3LkKDQ1V69atFR4eroSEBE2dOlUhISHq0aOHQkJCtG3bNts5AwcOVHh4uAIDA9WkSRONGzdOy5Ytu2FdWVlZiomJ0X333adHHnlEzz77rLZu3Wr6vtauXStPT0+7bcKECXZ9unfvrqefflrBwcGaMGGC0tLStHfvXknSjBkzVLFiRU2ePFkhISHq3LmzunfvftPrRkdHy8fHx7YFBASYrhkAAAAoaO7J7zxdj2EYslgsio+PV0BAgN0f+6GhoSpSpIji4+NVr149SVJgYKC8vLxsfUqVKiVHR0c5ODjYtZ06dcq2v2XLFkVHR+vHH39UamqqLl++rIsXL+r8+fNyd3fPtq5/X8ff399uzJsJDw/XjBkz7Np8fX3t9mvUqGH7t4eHh7y9vW3XiI+PV/369e36N2jQ4KbXjYyM1ODBg237qampBCgAAAAUWgUqPMXHxysoKMh0f2dnZ7v9qyv4/bvt6ip+SUlJatWqlfr06aPx48fL19dXO3fuVM+ePXXp0qXrhqcbjWmGh4eHgoODc3wvt7r6oNVqZWEKAAAA4P8rMK/tffnllzp48KDatWunqlWr6uTJkzp58qTt+JEjR3T27FmFhobm+hrfffedsrKyNHnyZD344IOqXLmyfv/997wo/7aqWrWq7RW+q77++ut8qgYAAAC4N92TM0/p6en6448/7JYqj46OVqtWrdS1a1c5ODioevXq6ty5s6ZOnarLly+rb9++aty4serWrZvr6wYHBysjI0PvvvuuWrdurV27dmnmzJl5eGfZu3q//+Tk5KTixYubOr93796aPHmyXn31VfXq1UvfffedbVVCAAAAAObckzNPGzdulL+/vwIDA9W8eXNt27ZN06dP1+rVq+Xo6CiLxaLVq1eraNGiatSokZo2baoKFSpo6dKlt3TdmjVr6p133tGbb76p++67T4sWLVJ0dHQe3dX1Xb3ff24PP/yw6fPLlSunlStX6rPPPlPNmjU1c+bMaxacAAAAAHBjFsMwjPwuAveG1NTUK6vuDVwmB2v23++6KmliyztUFQAAAJB7V//GTUlJkbe39w373pMzTwAAAABwpxGe8tGJEyeu+f2mf24nTpzI7xIBAAAA/H/35IIRBUXp0qUVFxd3w+MAAAAA7g6Ep3zk5OR0099vAgAAAHB34LU9AAAAADCBmSfk2KGoiJuuRAIAAAAUNMw8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYwGp7yLH7Rm2Sg9U922NJE1ve4WoAAACAO4OZJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA85bGYmBgVKVIkv8sAAAAAkMfuufDUvXt3WSwWWSwWOTs7q1SpUmrWrJnmzp2rrKys/C5PnTp10tGjR/N83D179sjR0VEtW/IjtAAAAEB+uOfCkyQ1b95cycnJSkpK0oYNGxQeHq4BAwaoVatWunz5cr7W5ubmppIlS+b5uHPmzNHLL7+sr776Sr///nuejw8AAADgxu7J8GS1WuXn56cyZcqoTp06GjZsmFavXq0NGzYoJiZGknTixAm1adNGnp6e8vb2VseOHfXnn3/axhg9erRq1aqluXPnqly5cvL09FTfvn2VmZmpt956S35+fipZsqTGjx9vd+133nlH1atXl4eHhwICAtS3b1+lpaXZjv/7tb2r11m4cKECAwPl4+Ojp556SufOnTN9v2lpaVq6dKn69Omjli1b2u7xn9asWaNKlSrJ1dVV4eHhmj9/viwWi86ePWvrs3PnTj3yyCNyc3NTQECA+vfvr7///tt0HQAAAEBhdk+Gp+w0adJENWvW1KpVq5SVlaU2bdro9OnT2r59uzZv3qyff/5ZnTp1sjsnMTFRGzZs0MaNG7V48WLNmTNHLVu21K+//qrt27frzTff1IgRI/TNN9/YznFwcND06dN1+PBhzZ8/X19++aVee+21G9aWmJiozz77TGvXrtXatWu1fft2TZw40fS9LVu2TFWqVFFISIi6dOmiuXPnyjAM2/Hjx4+rffv2atu2rQ4cOKAXX3xRw4cPv6aG5s2bq127dvrhhx+0dOlS7dy5U/369bvuddPT05Wammq3AQAAAIWVU34XkJeqVKmiH374QVu3btXBgwd1/PhxBQQESJIWLFigatWqad++fapXr54kKSsrS3PnzpWXl5dCQ0MVHh6uhIQErV+/Xg4ODgoJCdGbb76pbdu2qX79+pKkgQMH2q4XGBiocePGqXfv3vrggw+uW1dWVpZiYmLk5eUlSXr22We1devWa2a1rmfOnDnq0qWLpCuvLKakpGj79u0KCwuTJM2aNUshISF6++23JUkhISE6dOiQ3fjR0dHq3Lmzrf5KlSpp+vTpaty4sWbMmCFXV9drrhsdHa2oqChTNQIAAAAFXYGZeZIkwzBksVgUHx+vgIAAW3CSpNDQUBUpUkTx8fG2tsDAQFugkaRSpUopNDRUDg4Odm2nTp2y7W/ZskWPPvqoypQpIy8vLz377LP666+/dP78+evW9e/r+Pv72415IwkJCdq7d6+efvppSZKTk5M6deqkOXPm2PW5GgiveuCBB+z2Dxw4oJiYGHl6etq2iIgIZWVl6fjx49leOzIyUikpKbbt5MmTpmoGAAAACqICNfMUHx+voKAg0/2dnZ3t9q+u4Pfvtqur+CUlJalVq1bq06ePxo8fL19fX+3cuVM9e/bUpUuX5O7ubvo6ZlcGnDNnji5fvqzSpUvb2gzDkNVq1XvvvScfHx9T46SlpenFF19U//79rzlWrly5bM+xWq2yWq2mxgcAAAAKugITnr788ksdPHhQgwYNUtmyZXXy5EmdPHnSNvt05MgRnT17VqGhobm+xnfffaesrCxNnjzZNju1bNmyPKk/O5cvX9aCBQs0efJk/ec//7E71rZtWy1evFi9e/dWSEiI1q9fb3d83759dvt16tTRkSNHFBwcfNvqBQAAAAqye/K1vfT0dP3xxx/67bfftH//fk2YMEFt2rRRq1at1LVrVzVt2lTVq1dX586dtX//fu3du1ddu3ZV48aNVbdu3VxfNzg4WBkZGXr33Xf1888/a+HChZo5c2Ye3pm9tWvX6syZM+rZs6fuu+8+u61du3a2V/defPFF/fjjjxo6dKiOHj2qZcuW2Vbks1gskqShQ4dq9+7d6tevn+Li4nTs2DGtXr36hgtGAAAAAPg/92R42rhxo/z9/RUYGKjmzZtr27Ztmj59ulavXi1HR0dZLBatXr1aRYsWVaNGjdS0aVNVqFBBS5cuvaXr1qxZU++8847efPNN3XfffVq0aJGio6Pz6K6uNWfOHDVt2jTbV/PatWunb7/9Vj/88IOCgoK0YsUKrVq1SjVq1NCMGTNsq+1dfe2uRo0a2r59u44ePapHHnlEtWvX1siRI+1eBwQAAABwfRbjn2teo8AYP368Zs6cmaeLPKSmpsrHx0cBA5fJwZr997uSJrbMs+sBAAAAt9vVv3FTUlLk7e19w74F5jtPhd0HH3ygevXqqVixYtq1a5fefvttXskDAAAA8hDhKR+dOHHihgtYHDly5Lor4f3bsWPHNG7cOJ0+fVrlypXTK6+8osjIyLwqFQAAACj0eG0vH12+fFlJSUnXPR4YGCgnp7sn3/LaHgAAAAoaXtu7Rzg5ObF0OAAAAHCPuCdX2wMAAACAO42ZJ+TYoaiIm05pAgAAAAUNM08AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACaw2h5y7L5Rm677I7m3gh/YBQAAwN2MmScAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPN1EbGysLBaLzp49m9+l2Pnwww8VEBAgBwcHTZ06Ndu20aNHq1atWvlaJwAAAFBQFKjwNHPmTHl5eeny5cu2trS0NDk7OyssLMyu79VQlJiYeNvq6d69uywWy3W3wMDAXI2bmpqqfv36aejQofrtt9/0wgsvZNs2ZMgQbd26NW9vCgAAACikClR4Cg8PV1pamr799ltb244dO+Tn56dvvvlGFy9etLVv27ZN5cqVU8WKFW9bPdOmTVNycrJtk6R58+bZ9vft22fX/9KlS6bGPXHihDIyMtSyZUv5+/vL3d092zZPT08VK1Ysz+8LAAAAKIwKVHgKCQmRv7+/YmNjbW2xsbFq06aNgoKC9PXXX9u1h4eHKysrS9HR0QoKCpKbm5tq1qypFStWXDP2rl27VKNGDbm6uurBBx/UoUOHblqPj4+P/Pz8bJskFSlSxLZfr149jR07Vl27dpW3t7deeOEFSdLQoUNVuXJlubu7q0KFCnrjjTeUkZEhSYqJiVH16tUlSRUqVJDFYsm2LSkpKdvX9ubOnatq1arJarXK399f/fr1M/8BAwAAAIVYgQpP0pXZp23bttn2t23bprCwMDVu3NjWfuHCBX3zzTcKDw9XdHS0FixYoJkzZ+rw4cMaNGiQunTpou3bt9uN++qrr2ry5Mnat2+fSpQoodatW9sCza2YNGmSatasqe+//15vvPGGJMnLy0sxMTE6cuSIpk2bpo8++khTpkyRJHXq1ElbtmyRJO3du1fJycnq0KHDNW0BAQHXXGvGjBl66aWX9MILL+jgwYNas2aNgoODr1tbenq6UlNT7TYAAACgsHLK7wLyWnh4uAYOHKjLly/rwoUL+v7779W4cWNlZGRo5syZkqQ9e/YoPT1dYWFhCg0N1ZYtW9SgQQNJV2Zudu7cqVmzZqlx48a2cUeNGqVmzZpJkubPn6+yZcvq008/VceOHW+p3iZNmuiVV16xaxsxYoTt34GBgRoyZIiWLFmi1157TW5ubrZX8UqUKGGb0cqu7d/GjRunV155RQMGDLC11atX77q1RUdHKyoqKnc3BgAAABQwBS48hYWF6e+//9a+fft05swZVa5cWSVKlFDjxo3Vo0cPXbx4UbGxsapQoYLS0tJ0/vx5Wyi66tKlS6pdu7Zd29VwJUm+vr4KCQlRfHz8Lddbt27da9qWLl2q6dOnKzExUWlpabp8+bK8vb1v6TqnTp3S77//rkcffdT0OZGRkRo8eLBtPzU1NdsZLQAAAKAwKHDhKTg4WGXLltW2bdt05swZ2+xR6dKlFRAQoN27d2vbtm1q0qSJ0tLSJEnr1q1TmTJl7MaxWq13pF4PDw+7/T179qhz586KiopSRESEfHx8tGTJEk2ePPmWruPm5pbjc6xW6x37HAAAAIC7XYELT9KVV/diY2N15swZvfrqq7b2Ro0aacOGDdq7d6/69Omj0NBQWa1WnThxwu4Vvex8/fXXKleunCTpzJkzOnr0qKpWrZrnte/evVvly5fX8OHDbW2//PLLLY/r5eWlwMBAbd26VeHh4bc8HgAAAFDYFNjw9NJLLykjI8MuFDVu3Fj9+vXTpUuXFB4eLi8vLw0ZMkSDBg1SVlaWHn74YaWkpGjXrl3y9vZWt27dbOeOGTNGxYoVU6lSpTR8+HAVL15cbdu2zfPaK1WqpBMnTmjJkiWqV6+e1q1bp08//TRPxh49erR69+6tkiVLqkWLFjp37px27dqll19+OU/GBwAAAAqyAhueLly4oCpVqqhUqVK29saNG+vcuXO2Jc0laezYsSpRooSio6P1888/q0iRIqpTp46GDRtmN+bEiRM1YMAAHTt2TLVq1dLnn38uFxeXPK/98ccf16BBg9SvXz+lp6erZcuWeuONNzR69OhbHrtbt266ePGipkyZoiFDhqh48eJq3779rRcNAAAAFAIWwzCM/C4C94bU1FT5+PgoYOAyOVjd83z8pIkt83xMAAAA4Eau/o2bkpJy00XaCtzvPAEAAADA7UB4ukUtWrSQp6dnttuECRPyuzwAAAAAeaRAfufpTpo9e7YuXLiQ7TFfX987XA0AAACA24XwdIv+/ftQAAAAAAomXtsDAAAAABOYeUKOHYqKuOlKJAAAAEBBw8wTAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJrLaHHLtv1CY5WN0lSUkTW+ZzNQAAAMCdwcwTAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJuQ5PCxcu1EMPPaTSpUvrl19+kSRNnTpVq1evzrPiAAAAAOBukavwNGPGDA0ePFiPPfaYzp49q8zMTElSkSJFNHXq1LysDwAAAADuCrkKT++++64++ugjDR8+XI6Ojrb2unXr6uDBg3lWHG6vsLAwDRw4ML/LAAAAAO4JuQpPx48fV+3ata9pt1qt+vvvv2+5KLO6d+8ui8Uii8UiZ2dnlSpVSs2aNdPcuXOVlZVl6xcYGCiLxaKvv/7a7vyBAwcqLCzMtn/+/HlFRkaqYsWKcnV1VYkSJdS4cWPTryKGhYXZ6nF1dVXlypUVHR0twzDy5H4BAAAA5J9chaegoCDFxcVd075x40ZVrVr1VmvKkebNmys5OVlJSUnasGGDwsPDNWDAALVq1UqXL1+29XN1ddXQoUNvOFbv3r21atUqvfvuu/rxxx+1ceNGtW/fXn/99Zfpep5//nklJycrISFBkZGRGjlypGbOnJnr+7tVhmHYfQ4AAAAAcidX4Wnw4MF66aWXtHTpUhmGob1792r8+PGKjIzUa6+9ltc13pDVapWfn5/KlCmjOnXqaNiwYVq9erU2bNigmJgYW78XXnhBX3/9tdavX3/dsdasWaNhw4bpscceU2BgoO6//369/PLLeu6550zX4+7uLj8/P5UvX149evRQjRo1tHnzZtvx9PR0DRkyRGXKlJGHh4fq16+v2NhYuzF27dqlsLAwubu7q2jRooqIiNCZM2ds5/fv318lS5aUq6urHn74Ye3bt892bmxsrCwWizZs2KD7779fVqtVO3fu1N9//62uXbvK09NT/v7+mjx5sul7AgAAAJDL8NSrVy+9+eabGjFihM6fP69nnnlGM2bM0LRp0/TUU0/ldY051qRJE9WsWVOrVq2ytQUFBal3796KjIy0e6Xvn/z8/LR+/XqdO3fulmswDEM7duzQjz/+KBcXF1t7v379tGfPHi1ZskQ//PCDOnTooObNm+vYsWOSpLi4OD366KMKDQ3Vnj17tHPnTrVu3dq2KMdrr72mlStXav78+dq/f7+Cg4MVERGh06dP213/9ddf18SJExUfH68aNWro1Vdf1fbt27V69Wp98cUXio2N1f79+294D+np6UpNTbXbAAAAgELLyKGMjAxj/vz5xh9//GEYhmH8/fffxp9//pnTYfJEt27djDZt2mR7rFOnTkbVqlUNwzCM8uXLG1OmTDFOnTpleHl5GQsWLDAMwzAGDBhgNG7c2HbO9u3bjbJlyxrOzs5G3bp1jYEDBxo7d+40XU/jxo0NZ2dnw8PDw3B2djYkGa6ursauXbsMwzCMX375xXB0dDR+++03u/MeffRRIzIy0jAMw3j66aeNhx56KNvx09LSDGdnZ2PRokW2tkuXLhmlS5c23nrrLcMwDGPbtm2GJOOzzz6z9Tl37pzh4uJiLFu2zNb2119/GW5ubsaAAQOuez+jRo0yJF2zBQxcZpQfutYoP3St6c8GAAAAuBulpKQYkoyUlJSb9s3xzJOTk5N69+6tixcvSrrymlrJkiXzLMzlFcMwZLFY7NpKlCihIUOGaOTIkbp06dI15zRq1Eg///yztm7dqvbt2+vw4cN65JFHNHbsWNPX7dy5s+Li4rRr1y61aNFCw4cPV8OGDSVJBw8eVGZmpipXrixPT0/btn37diUmJkr6v5mn7CQmJiojI0MPPfSQrc3Z2VkPPPCA4uPj7frWrVvX7rxLly6pfv36tjZfX1+FhITc8F4iIyOVkpJi206ePGn6cwAAAAAKGqfcnPTAAw/o+++/V/ny5fO6njwTHx+voKCga9oHDx6sDz74QB988EG25zk7O+uRRx7RI488oqFDh2rcuHEaM2aMhg4davf63fX4+PgoODhYkrRs2TIFBwfrwQcfVNOmTZWWliZHR0d99913dku8S5Knp6ckyc3NLae3mi0PD49bHsNqtcpqteZBNQAAAMC9L1ffeerbt69eeeUVvffee9qzZ49++OEHuy2/ffnllzp48KDatWt3zTFPT0+98cYbGj9+vKnvNoWGhury5cu2mbac8PT01IABAzRkyBAZhqHatWsrMzNTp06dUnBwsN3m5+cnSapRo4a2bt2a7XgVK1aUi4uLdu3aZWvLyMjQvn37FBoaet06KlasKGdnZ33zzTe2tjNnzujo0aM5vicAAACgsMrVzNPVRSH69+9va7NYLLZX5a4ubnAnpKen648//lBmZqb+/PNPbdy4UdHR0WrVqpW6du2a7TkvvPCCpkyZok8++cTuVbawsDA9/fTTqlu3rooVK6YjR45o2LBhCg8Pl7e3d67qe/HFFzV27FitXLlS7du3V+fOndW1a1dNnjxZtWvX1n//+19t3bpVNWrUUMuWLRUZGanq1aurb9++6t27t1xcXLRt2zZ16NBBxYsXV58+ffTqq6/K19dX5cqV01tvvaXz58+rZ8+e163B09NTPXv21KuvvqpixYqpZMmSGj58uBwccpWdAQAAgEIpV+Hp+PHjeV1Hrm3cuFH+/v5ycnJS0aJFVbNmTU2fPl3dunW7bjhwdnbW2LFj9cwzz9i1R0REaP78+Ro2bJjOnz+v0qVLq1WrVho5cmSu6/P19VXXrl01evRoPfnkk5o3b57GjRunV155Rb/99puKFy+uBx98UK1atZIkVa5cWV988YWGDRumBx54QG5ubqpfv76efvppSdLEiROVlZWlZ599VufOnVPdunW1adMmFS1a9IZ1vP3220pLS1Pr1q3l5eWlV155RSkpKbm+LwAAAKCwsRiGYeR3Ebg3pKamysfHRwEDl8nB6i5JSprYMp+rAgAAAHLv6t+4KSkpN33bLFczTwsWLLjh8eu9LgcAAAAA96pchacBAwbY7WdkZOj8+fNycXGRu7t7gQxPO3bsUIsWLa57PC0t7Q5WAwAAAOBOy1V4OnPmzDVtx44dsy1mUBDVrVtXcXFx+V0GAAAAgHySq/CUnUqVKmnixInq0qWLfvzxx7wa9q7h5uZm+/0mAAAAAIVPnq5V7eTkpN9//z0vhwQAAACAu0KuZp7WrFljt28YhpKTk/Xee+/poYceypPCcPc6FBWR69+9AgAAAO5VuQpPbdu2tdu3WCwqUaKEmjRposmTJ+dFXQAAAABwV8lVeMrKysrrOgAAAADgrpar7zyNGTNG58+fv6b9woULGjNmzC0XBQAAAAB3G4thGEZOT3J0dFRycrJKlixp1/7XX3+pZMmSyszMzLMCcffIya8vAwAAAPeCnPyNm6uZJ8MwZLFYrmk/cOCAfH19czMkAAAAANzVcvSdp6JFi8pischisahy5cp2ASozM1NpaWnq3bt3nheJu8t9ozbJweqe32UA+IekiS3zuwQAAAq8HIWnqVOnyjAMPffcc4qKipKPj4/tmIuLiwIDA9WgQYM8LxIAAAAA8luOwlO3bt0kSUFBQWrYsKGcnZ1vS1EAAAAAcLfJ1VLljRs3tv374sWLunTpkt1xFhMAAAAAUNDkasGI8+fPq1+/fipZsqQ8PDxUtGhRuw0AAAAACppchadXX31VX375pWbMmCGr1arZs2crKipKpUuX1oIFC/K6RgAAAADId7l6be/zzz/XggULFBYWph49euiRRx5RcHCwypcvr0WLFqlz5855XScAAAAA5KtczTydPn1aFSpUkHTl+02nT5+WJD388MP66quv8q46AAAAALhL5Co8VahQQcePH5ckValSRcuWLZN0ZUaqSJEieVZcfho9erRKlSoli8Wizz77TN27d1fbtm3zuyxJ0meffabg4GA5Ojpq4MCB2bbFxMQUmGcBAAAA3A1yFZ569OihAwcOSJJef/11vf/++3J1ddWgQYP06quv5mmBN9O9e3fbD/e6uLgoODhYY8aM0eXLl3M9Znx8vKKiojRr1iwlJyerRYsWmjZtmmJiYnI81vz58/Xwww9Lko4fP65nnnlGpUuXlqurq8qWLas2bdroxx9/zNGYL774otq3b6+TJ09q7Nix2bZ16tRJR48ezXG9AAAAALKXq+88DRo0yPbvpk2b6scff9R3332n4OBg1ahRI8+KM6t58+aaN2+e0tPTtX79er300ktydnZWZGSkXb9Lly7JxcXlpuMlJiZKktq0aSOLxSJJslqtuapt9erVevzxx5WRkaFmzZopJCREq1atkr+/v3799Vdt2LBBZ8+eNT1eWlqaTp06pYiICJUuXfq6bZLk5uaWq5oBAAAAXCtXM0//dPHiRZUvX15PPvlkvgQn6Uqw8fPzU/ny5dWnTx81bdpUa9assb1qN378eJUuXVohISGSpJMnT6pjx44qUqSIfH191aZNGyUlJUm68rpe69atJUkODg628PTP1/b++9//ys/PTxMmTLDVsHv3brm4uGjr1q22tosXL+qLL77Q448/rsOHDysxMVEffPCBHnzwQZUvX14PPfSQxo0bpwcffFCSFBsbK4vFYhem4uLiZLFYlJSUpNjYWHl5eUmSmjRpIovFct22f762ZxiGmjZtqoiICBmGIenK99bKli2rkSNH5uGTAAAAAAquXIWnzMxMjR07VmXKlJGnp6d+/vlnSdIbb7yhOXPm5GmBueHm5mb74d6tW7cqISFBmzdv1tq1a5WRkaGIiAh5eXlpx44d2rVrlzw9PdW8eXNdunRJQ4YM0bx58yRJycnJSk5Ovmb8EiVKaO7cuRo9erS+/fZbnTt3Ts8++6z69eunRx991NZv69atKlOmjKpUqaISJUrIwcFBK1asUGZmZq7uq2HDhkpISJAkrVy5UsnJyddt+yeLxaL58+dr3759mj59uiSpd+/eKlOmzA3DU3p6ulJTU+02AAAAoLDKVXgaP368YmJi9NZbb9m9Bnffffdp9uzZeVZcThmGoS1btmjTpk1q0qSJJMnDw0OzZ89WtWrVVK1aNS1dulRZWVmaPXu2qlevrqpVq2revHk6ceKEYmNj5enpaZux8fPzk5+fX7bXeuyxx/T888+rc+fO6t27tzw8PBQdHW3X5+ore5JUpkwZTZ8+XSNHjlTRokXVpEkTjR071hY8zXBxcVHJkiUlSb6+vvLz87tu27+VKVNGs2bN0uuvv67IyEitX79eH3/8sZycrv/mZnR0tHx8fGxbQECA6VoBAACAgiZX4WnBggX68MMP1blzZzk6Otraa9asmePFD/LC2rVr5enpKVdXV7Vo0UKdOnXS6NGjJUnVq1e3CxMHDhzQTz/9JC8vL3l6esrT01O+vr66ePGi7btOZk2aNEmXL1/W8uXLtWjRIrvvRRmGoc8//9wWniTppZde0h9//KFFixapQYMGWr58uapVq6bNmzff2gdgUocOHfTEE09o4sSJmjRpkipVqnTD/pGRkUpJSbFtJ0+evCN1AgAAAHejXC0Y8dtvvyk4OPia9qysLGVkZNxyUTkVHh6uGTNmyMXFRaVLl7abTfHw8LDrm5aWpvvvv1+LFi26ZpwSJUrk6LqJiYn6/ffflZWVpaSkJFWvXt12bO/evbp8+fI1r9B5eXmpdevWat26tcaNG6eIiAiNGzdOzZo1k4PDlSx79XtJkvL08zx//ry+++47OTo66tixYzftb7Vac71QBgAAAFDQ5Co8hYaGaseOHSpfvrxd+4oVK1S7du08KSwnPDw8sg1z2alTp46WLl2qkiVLytvbO9fXvHTpkrp06aJOnTopJCREvXr10sGDB22v0K1evVotW7a0m5n7N4vFoipVqmj37t2S/i+8JScnq2jRopKuLBiRV1555RU5ODhow4YNeuyxx9SyZUvb640AAAAAbixXr+2NHDlS/fr105tvvqmsrCytWrVKzz//vMaPH3/Xr97WuXNnFS9eXG3atNGOHTt0/PhxxcbGqn///vr1119NjzN8+HClpKRo+vTpGjp0qCpXrqznnnvOdnzNmjV2r+zFxcWpTZs2WrFihY4cOaKffvpJc+bM0dy5c9WmTRtJUnBwsAICAjR69GgdO3ZM69at0+TJk/PkvtetW6e5c+dq0aJFatasmV599VV169ZNZ86cyZPxAQAAgIIuR+Hp559/lmEYatOmjT7//HNt2bJFHh4eGjlypOLj4/X555+rWbNmt6vWPOHu7q6vvvpK5cqV05NPPqmqVauqZ8+eunjxoumZqNjYWE2dOlULFy6Ut7e3HBwctHDhQu3YsUMzZsxQYmKifvrpJ0VERNjOKVu2rAIDAxUVFaX69eurTp06mjZtmqKiojR8+HBJkrOzsxYvXqwff/xRNWrU0Jtvvqlx48bd8j3/97//Vc+ePTV69GjVqVNHkhQVFaVSpUqpd+/etzw+AAAAUBhYjH9+weYmHB0dlZycbHs1rVOnTpo+fbpKlSp12wq8F73zzjvasmWL1q9fn9+l5KnU1NQrq+4NXCYHq3t+lwPgH5ImtszvEgAAuCdd/Rs3JSXlppMpOZp5+nfO2rBhg/7++++cV1jAlS1bVpGRkfldBgAAAIA8lKsFI67KwaRVodKxY8f8LgEAAABAHsvRzJPFYpHFYrmmDQAAAAAKuhzNPBmGoe7du9t+++fixYvq3bv3Nb+ltGrVqryrEAAAAADuAjkKT926dbPb79KlS54WAwAAAAB3qxyttofCLScrkQAAAAD3gtu22h4AAAAAFFaEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJiQo6XKAUm6b9QmOVjdsz2WNLHlHa4GAAAAuDOYeQIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCUx7q3r272rZtm99lAAAAALgNCE8mWSyWG26jR4/WtGnTFBMTk6fXDQsL08CBA69pj4mJUZEiRWz7o0ePttXi6OiogIAAvfDCCzp9+rTdeYGBgZo6dWqe1ggAAAAUBk75XcC9Ijk52fbvpUuXauTIkUpISLC1eXp6ytPTMz9Ks6lWrZq2bNmizMxMxcfH67nnnlNKSoqWLl2ar3UBAAAABQEzTyb5+fnZNh8fH1ksFrs2T0/Pa17bCwsLU//+/fXaa6/J19dXfn5+Gj16tO34c889p1atWtldJyMjQyVLltScOXNyXKOTk5P8/PxUpkwZNW3aVB06dNDmzZtze8sAAAAA/oHwdJvNnz9fHh4e+uabb/TWW29pzJgxtkDTq1cvbdy40W5Wa+3atTp//rw6dep0S9dNSkrSpk2b5OLikusx0tPTlZqaarcBAAAAhRXh6TarUaOGRo0apUqVKqlr166qW7eutm7dKklq2LChQkJCtHDhQlv/efPmqUOHDrl6BfDgwYPy9PSUm5ubgoKCdPjwYQ0dOjTXtUdHR8vHx8e2BQQE5HosAAAA4F5HeLrNatSoYbfv7++vU6dO2fZ79eqlefPmSZL+/PNPbdiwQc8991yurhUSEqK4uDjt27dPQ4cOVUREhF5++eVc1x4ZGamUlBTbdvLkyVyPBQAAANzrCE+3mbOzs92+xWJRVlaWbb9r1676+eeftWfPHn388ccKCgrSI488Yjvu7e2tlJSUa8Y9e/asfHx87NpcXFwUHBys++67TxMnTpSjo6OioqJyXbvVapW3t7fdBgAAABRWhKd8VqxYMbVt21bz5s1TTEyMevToYXc8JCRE+/fvv+a8/fv3q3Llyjcce8SIEZo0aZJ+//33PK0ZAAAAKIxYqvwu0KtXL7Vq1UqZmZnq1q2b3bE+ffrovffeU//+/dWrVy9ZrVatW7dOixcv1ueff37DcRs0aKAaNWpowoQJeu+992ztv/32m+Li4uz6li9fXkWLFs2zewIAAAAKGmae7gJNmzaVv7+/IiIiVLp0abtjFSpU0FdffaUff/xRTZs2Vf369bVs2TItX75czZs3v+nYgwYN0uzZs+2+rzRp0iTVrl3bblu3bl2e3xcAAABQkFgMwzDyu4jCLi0tTWXKlNG8efP05JNP5nc515Wamnpl1b2By+Rgdc+2T9LElne4KgAAACD3rv6Nm5KSctPv+PPaXj7KysrS//73P02ePFlFihTR448/nt8lAQAAALgOwlM+OnHihIKCglS2bFnFxMTIyYnHAQAAANyt+Gs9HwUGBoq3JgEAAIB7AwtGAAAAAIAJhCcAAAAAMIHX9pBjh6IibroSCQAAAFDQMPMEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgwl0VnsLCwjRw4MD8LqNA6N69u9q2bZvfZQAAAAAFxl0Vnm7GYrHos88+s2sbPXq0atWqdUfrCAwMlMVikcVikZubmwIDA9WxY0d9+eWXd7SOG5k2bZpiYmLyuwwAAACgwLinwtPdZMyYMUpOTlZCQoIWLFigIkWKqGnTpho/fnx+lyZJ8vHxUZEiRfK7DAAAAKDAuOvCU1ZWll577TX5+vrKz89Po0ePlnRltkeSnnjiCVksFgUGBiomJkZRUVE6cOCAbSbo6myLxWLRjBkz1KJFC7m5ualChQpasWKF7TqXLl1Sv3795O/vL1dXV5UvX17R0dGm6/Ty8pKfn5/KlSunRo0a6cMPP9Qbb7yhkSNHKiEhwdZv+/bteuCBB2S1WuXv76/XX39dly9fth0PCwvTyy+/rIEDB6po0aIqVaqUPvroI/3999/q0aOHvLy8FBwcrA0bNtjOyczMVM+ePRUUFCQ3NzeFhIRo2rRpdvX9+7W9sLAw9e/fP9vPFgAAAMDN3XXhaf78+fLw8NA333yjt956S2PGjNHmzZu1b98+SdK8efOUnJysffv2qVOnTnrllVdUrVo1JScnKzk5WZ06dbKN9cYbb6hdu3Y6cOCAOnfurKeeekrx8fGSpOnTp2vNmjVatmyZEhIStGjRIltAy60BAwbIMAytXr1akvTbb7/pscceU7169XTgwAHNmDFDc+bM0bhx46655+LFi2vv3r16+eWX1adPH3Xo0EENGzbU/v379Z///EfPPvuszp8/L+lKwCxbtqyWL1+uI0eOaOTIkRo2bJiWLVuWq8/2etLT05Wammq3AQAAAIWWcRdp3Lix8fDDD9u11atXzxg6dKhhGIYhyfj000/tjo8aNcqoWbPmNWNJMnr37m3XVr9+faNPnz6GYRjGyy+/bDRp0sTIysrKcZ3ly5c3pkyZku2xUqVK2a4xbNgwIyQkxO4a77//vuHp6WlkZmYahnHtPV++fNnw8PAwnn32WVtbcnKyIcnYs2fPdWt66aWXjHbt2tn2u3XrZrRp08a2f7PPNjujRo0yJF2zpaSkXPccAAAA4F6SkpJi+m/cu27mqUaNGnb7/v7+OnXqVK7GatCgwTX7V2eeunfvrri4OIWEhKh///764osvclfwvxiGIYvFIkmKj49XgwYNbPuS9NBDDyktLU2//vqrre2f9+zo6KhixYqpevXqtrZSpUpJkt3n8P777+v+++9XiRIl5OnpqQ8//FAnTpy4YW05/WwjIyOVkpJi206ePHnD8QEAAICC7K4LT87Oznb7FotFWVlZeX6dOnXq6Pjx4xo7dqwuXLigjh07qn379rc05l9//aX//ve/CgoKytF52d3zP9uuhq+rn8OSJUs0ZMgQ9ezZU1988YXi4uLUo0cPXbp0KcfXudFna7Va5e3tbbcBAAAAhdVdF55uxNnZWZmZmXZtLi4u17Rd9fXXX1+zX7VqVdu+t7e3OnXqpI8++khLly7VypUrdfr06VzXN23aNDk4ONgWaqhatar27NkjwzBsfXbt2iUvLy+VLVs219fZtWuXGjZsqL59+6p27doKDg5WYmJirscDAAAAcHNO+V1ATgQGBmrr1q166KGHZLVaVbRoUQUGBur48eOKi4tT2bJl5eXlJavVKklavny56tatq4cffliLFi3S3r17NWfOHEnSO++8I39/f9WuXVsODg5avny5/Pz8TC/vfe7cOf3xxx/KyMjQ8ePH9fHHH2v27NmKjo5WcHCwJKlv376aOnWqXn75ZfXr108JCQkaNWqUBg8eLAeH3OfWSpUqacGCBdq0aZOCgoK0cOFC7du3L8czXgAAAADMu6dmniZPnqzNmzcrICBAtWvXliS1a9dOzZs3V3h4uEqUKKHFixfb+kdFRWnJkiWqUaOGFixYoMWLFys0NFTSlaXG33rrLdWtW1f16tVTUlKS1q9fbzrUjBw5Uv7+/goODtazzz6rlJQUbd26VUOHDrX1KVOmjNavX6+9e/eqZs2a6t27t3r27KkRI0bc0ufw4osv6sknn1SnTp1Uv359/fXXX+rbt+8tjQkAAADgxizGP98pK0AsFos+/fRTu986wq1JTU2Vj4+PUlJS+P4TAAAACoSc/I17T808AQAAAEB+ITz9y6JFi+Tp6ZntVq1atfwuDwAAAEA+uacWjMiJ3L6N+Pjjj6t+/frZHvv3Ut8AAAAACo8CG55yy8vLS15eXvldBgAAAIC7DK/tAQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhyaTY2FhZLBadPXtWkhQTE6MiRYrkSy35eW0AAACgsCqQ4al79+6yWCyyWCxydnZWUFCQXnvtNV28eDHPrtGpUycdPXo0z8a7nsDAQE2dOjVfrg0AAADg/zjldwG3S/PmzTVv3jxlZGTou+++U7du3WSxWPTmm2/myfhubm5yc3O77vFLly7JxcUlT66V02sDAAAAyHsFcuZJkqxWq/z8/BQQEKC2bduqadOm2rx5syQpKytL0dHRCgoKkpubm2rWrKkVK1bYnb9+/XpVrlxZbm5uCg8PV1JSkt3xf786N3r0aNWqVUuzZ89WUFCQXF1dJUlnz55Vr169VKJECXl7e6tJkyY6cOCA3Viff/656tWrJ1dXVxUvXlxPPPGEJCksLEy//PKLBg0aZJtJy+7akjRjxgxVrFhRLi4uCgkJ0cKFC+2OWywWzZ49W0888YTc3d1VqVIlrVmzJlefLQAAAFAYFdjw9E+HDh3S7t27bTNB0dHRWrBggWbOnKnDhw9r0KBB6tKli7Zv3y5JOnnypJ588km1bt1acXFx6tWrl15//fWbXuenn37SypUrtWrVKsXFxUmSOnTooFOnTmnDhg367rvvVKdOHT366KM6ffq0JGndunV64okn9Nhjj+n777/X1q1b9cADD0iSVq1apbJly2rMmDFKTk5WcnJyttf99NNPNWDAAL3yyis6dOiQXnzxRfXo0UPbtm2z6xcVFaWOHTvqhx9+0GOPPabOnTvb6shOenq6UlNT7TYAAACg0DIKoG7duhmOjo6Gh4eHYbVaDUmGg4ODsWLFCuPixYuGu7u7sXv3brtzevbsaTz99NOGYRhGZGSkERoaand86NChhiTjzJkzhmEYxrx58wwfHx/b8VGjRhnOzs7GqVOnbG07duwwvL29jYsXL9qNVbFiRWPWrFmGYRhGgwYNjM6dO1/3XsqXL29MmTLFru3f127YsKHx/PPP2/Xp0KGD8dhjj9n2JRkjRoyw7aelpRmSjA0bNlz32qNGjTIkXbOlpKRc9xwAAADgXpKSkmL6b9wCO/MUHh6uuLg4ffPNN+rWrZt69Oihdu3a6aefftL58+fVrFkzeXp62rYFCxYoMTFRkhQfH6/69evbjdegQYObXrN8+fIqUaKEbf/AgQNKS0tTsWLF7K51/Phx27Xi4uL06KOP3tK9xsfH66GHHrJre+ihhxQfH2/XVqNGDdu/PTw85O3trVOnTl133MjISKWkpNi2kydP3lKdAAAAwL2swC4Y4eHhoeDgYEnS3LlzVbNmTc2ZM0f33XefpCuvy5UpU8buHKvVesvX/Ke0tDT5+/srNjb2mr5Xv7N0Jxd+cHZ2ttu3WCzKysq6bn+r1XrLnwkAAABQUBTY8PRPDg4OGjZsmAYPHqyjR4/KarXqxIkTaty4cbb9q1ates1iCl9//XWOr1unTh398ccfcnJyUmBgYLZ9atSooa1bt6pHjx7ZHndxcVFmZuYNr1O1alXt2rVL3bp1s7Xt2rVLoaGhOa4ZAAAAQPYK7Gt7/9ahQwc5Ojpq1qxZGjJkiAYNGqT58+crMTFR+/fv17vvvqv58+dLknr37q1jx47p1VdfVUJCgj755BPFxMTk+JpNmzZVgwYN1LZtW33xxRdKSkrS7t27NXz4cH377beSpFGjRmnx4sUaNWqU4uPjdfDgQbvl1AMDA/XVV1/pt99+0//+979sr/Pqq68qJiZGM2bM0LFjx/TOO+9o1apVGjJkSM4/KAAAAADZKjThycnJSf369dNbb72lyMhIvfHGG4qOjlbVqlXVvHlzrVu3TkFBQZKkcuXKaeXKlfrss89Us2ZNzZw5UxMmTMjxNS0Wi9avX69GjRqpR48eqly5sp566in98ssvKlWqlKQry5EvX75ca9asUa1atdSkSRPt3bvXNsaYMWOUlJSkihUr2n2f6p/atm2radOmadKkSapWrZpmzZqlefPmKSwsLOcfFAAAAIBsWQzDMPK7CNwbUlNT5ePjo5SUFHl7e+d3OQAAAMAty8nfuIVm5gkAAAAAbgXhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJTvldAO49943aJAere36XAdwRSRNb5ncJAADgLsHMEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeConu3burbdu2+V0GAAAAcM8q0OEpLCxMAwcOvKY9JiZGRYoUkSSNHj1aFotFFotFjo6OCggI0AsvvKDTp0/bnRMYGKipU6fmuIakpCTb+BaLRV5eXqpWrZpeeuklHTt2LBd3Ze56cXFxeT42AAAAUJgV6PBkVrVq1ZScnKwTJ05o3rx52rhxo/r06ZOn19iyZYuSk5N14MABTZgwQfHx8apZs6a2bt2ap9cBAAAAcHsQniQ5OTnJz89PZcqUUdOmTdWhQwdt3rw5T69RrFgx+fn5qUKFCmrTpo22bNmi+vXrq2fPnsrMzLT1W716terUqSNXV1dVqFBBUVFRunz5su24xWLRjBkz1KJFC7m5ualChQpasWKF7XhQUJAkqXbt2rJYLAoLC7OrY9KkSfL391exYsX00ksvKSMjI0/vEwAAACioCE//kpSUpE2bNsnFxeW2XsfBwUEDBgzQL7/8ou+++06StGPHDnXt2lUDBgzQkSNHNGvWLMXExGj8+PF2577xxhtq166dDhw4oM6dO+upp55SfHy8JGnv3r2S/m+ma9WqVbbztm3bpsTERG3btk3z589XTEyMYmJirltjenq6UlNT7TYAAACgsCI8STp48KA8PT3l5uamoKAgHT58WEOHDr3t161SpYqkK4FNkqKiovT666+rW7duqlChgpo1a6axY8dq1qxZdud16NBBvXr1UuXKlTV27FjVrVtX7777riSpRIkSkv5vpsvX19d2XtGiRfXee++pSpUqatWqlVq2bHnD1wajo6Pl4+Nj2wICAvLy9gEAAIB7ilN+F3A3CAkJ0Zo1a3Tx4kV9/PHHiouL08svv3zbr2sYhqQrr+JJ0oEDB7Rr1y67mabMzExdvHhR58+fl7u7uySpQYMGduM0aNDA1AIR1apVk6Ojo23f399fBw8evG7/yMhIDR482LafmppKgAIAAEChVaDDk7e3t1JSUq5pP3v2rHx8fGz7Li4uCg4OliRNnDhRLVu2VFRUlMaOHXtb67v6qt3V7ymlpaUpKipKTz755DV9XV1db/l6zs7OdvsWi0VZWVnX7W+1WmW1Wm/5ugAAAEBBUKBf2wsJCdH+/fuvad+/f78qV6583fNGjBihSZMm6ffff79ttWVlZWn69OkKCgpS7dq1JUl16tRRQkKCgoODr9kcHP7vUX399dd2Y3399deqWrWqJNm+q/XPRSgAAAAA3LoCPfPUp08fvffee+rfv7969eolq9WqdevWafHixfr888+ve16DBg1Uo0YNTZgwQe+9956t/bfffrvm9bjy5curaNGiN63lr7/+0h9//KHz58/r0KFDmjp1qvbu3at169bZXqUbOXKkWrVqpXLlyql9+/ZycHDQgQMHdOjQIY0bN8421vLly1W3bl09/PDDWrRokfbu3as5c+ZIkkqWLCk3Nzdt3LhRZcuWlaurq90sGwAAAIDcKdAzTxUqVNBXX32lH3/8UU2bNlX9+vW1bNkyLV++XM2bN7/huYMGDdLs2bN18uRJW9ukSZNUu3Ztu23dunWmamnatKn8/f1VvXp1vf7666patap++OEHhYeH2/pERERo7dq1+uKLL1SvXj09+OCDmjJlisqXL283VlRUlJYsWaIaNWpowYIFWrx4sUJDQyVdWXZ9+vTpmjVrlkqXLq02bdqY/bgAAAAA3IDFuLpqAe4JFotFn376qdq2bXvHr52amnpl1b2By+Rgdb/j1wfyQ9LElvldAgAAuI2u/o2bkpIib2/vG/Yt0DNPAAAAAJBXCE+3qHfv3vL09Mx26927d36XBwAAACCPFOgFI+6EMWPGaMiQIdkeu9m0X27wliUAAACQPwhPt6hkyZIqWbJkfpcBAAAA4DbjtT0AAAAAMIGZJ+TYoaiI2/JKIgAAAHA3Y+YJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEVttDjt03apMcrO75XQYAAAAKgKSJLfO7BNOYeQIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCUyGQlJQki8WiuLi4/C4FAAAAuGcViPAUFhamgQMHXtMeExOjIkWKSJJGjx4ti8Uii8UiR0dHBQQE6IUXXtDp06ftzgkMDNTUqVNzXMPdElC6d++utm3b5msNAAAAQEHklN8F3EnVqlXTli1blJmZqfj4eD333HNKSUnR0qVL87s0AAAAAHe5AjHzZJaTk5P8/PxUpkwZNW3aVB06dNDmzZvvyLWzsrIUHR2toKAgubm5qWbNmlqxYoXteGxsrCwWi7Zu3aq6devK3d1dDRs2VEJCgt0448aNU8mSJeXl5aVevXrp9ddfV61atSRdmV2bP3++Vq9ebZtli42NtZ37888/Kzw8XO7u7qpZs6b27NlzJ24dAAAAKBAKVXj6p6SkJG3atEkuLi535HrR0dFasGCBZs6cqcOHD2vQoEHq0qWLtm/fbtdv+PDhmjx5sr799ls5OTnpueeesx1btGiRxo8frzfffFPfffedypUrpxkzZtiODxkyRB07dlTz5s2VnJys5ORkNWzY0G7sIUOGKC4uTpUrV9bTTz+ty5cvX7fm9PR0paam2m0AAABAYVWoXts7ePCgPD09lZmZqYsXL0qS3nnnndt+3fT0dE2YMEFbtmxRgwYNJEkVKlTQzp07NWvWLDVu3NjWd/z48bb9119/XS1bttTFixfl6uqqd999Vz179lSPHj0kSSNHjtQXX3yhtLQ0SZKnp6fc3NyUnp4uPz+/a+oYMmSIWrZsKUmKiopStWrV9NNPP6lKlSrZ1h0dHa2oqKi8+yAAAACAe1ihmnkKCQlRXFyc9u3bp6FDhyoiIkIvv/zybb/uTz/9pPPnz6tZs2by9PS0bQsWLFBiYqJd3xo1atj+7e/vL0k6deqUJCkhIUEPPPCAXf9/79/IjcbOTmRkpFJSUmzbyZMnTV8LAAAAKGgKxMyTt7e3UlJSrmk/e/asfHx8bPsuLi4KDg6WJE2cOFEtW7ZUVFSUxo4de1vruzoztG7dOpUpU8bumNVqtdt3dna2/dtisUi68n2pvJDTsa1W6zX1AQAAAIVVgZh5CgkJ0f79+69p379/vypXrnzd80aMGKFJkybp999/v53lKTQ0VFarVSdOnFBwcLDdFhAQYHqckJAQ7du3z67t3/suLi7KzMzMk7oBAAAA/J8CMfPUp08fvffee+rfv7969eolq9WqdevWafHixfr888+ve16DBg1Uo0YNTZgwQe+9956t/bfffrvm95rKly+vokWL3rSWf6+OJ11ZIn3IkCEaNGiQsrKy9PDDDyslJUW7du2St7e3unXrZuo+X375ZT3//POqW7euGjZsqKVLl+qHH35QhQoVbH0CAwO1adMmJSQkqFixYnYzbwAAAAByr0CEpwoVKuirr77S8OHD1bRpU126dElVqlTR8uXL1bx58xueO2jQIHXv3l1Dhw61zQJNmjRJkyZNsuu3cOFCdenS5aa1PPXUU9e0nTx5UmPHjlWJEiUUHR2tn3/+WUWKFFGdOnU0bNgw0/fZuXNn/fzzzxoyZIguXryojh07qnv37tq7d6+tz/PPP6/Y2FjVrVtXaWlp2rZtmwIDA01fAwAAAED2LIZhGPldBHKvWbNm8vPz08KFC2/7tVJTU+Xj46OAgcvkYHW/7dcDAABAwZc0sWW+Xv/q37gpKSny9va+Yd8CMfNUWJw/f14zZ85URESEHB0dtXjxYm3ZsuWO/dAvAAAAUJgViAUj7oTevXvbLTP+z6137953pAaLxaL169erUaNGuv/++/X5559r5cqVatq06R25PgAAAFCY8dqeSadOnVJqamq2x7y9vVWyZMk7XNGdx2t7AAAAyGu8tlcAlSxZslAEJAAAAADZ47U9AAAAADCBmSfk2KGoiJtOaQIAAAAFDTNPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmsNoecuy+UZvuiR/Jze8fXAMAAEDBwswTAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCk0ndu3dX27Ztr2mPjY2VxWLR2bNn73hNNxIdHS1HR0e9/fbb1xwLCwvTwIED73xRAAAAwD2M8FRAzZ07V6+99prmzp2b36UAAAAABQLhKQ/99ddfevrpp1WmTBm5u7urevXqWrx4se34hx9+qNKlSysrK8vuvDZt2ui5556z7a9evVp16tSRq6urKlSooKioKF2+fNl0Hdu3b9eFCxc0ZswYpaamavfu3bZj3bt31/bt2zVt2jRZLBZZLBYlJSXl/qYBAACAQoLwlIcuXryo+++/X+vWrdOhQ4f0wgsv6Nlnn9XevXslSR06dNBff/2lbdu22c45ffq0Nm7cqM6dO0uSduzYoa5du2rAgAE6cuSIZs2apZiYGI0fP950HXPmzNHTTz8tZ2dnPf3005ozZ47t2LRp09SgQQM9//zzSk5OVnJysgICArIdJz09XampqXYbAAAAUFgRnnJg7dq18vT0tNtatGhhO16mTBkNGTJEtWrVUoUKFfTyyy+refPmWrZsmSSpaNGiatGihT755BPbOStWrFDx4sUVHh4uSYqKitLrr7+ubt26qUKFCmrWrJnGjh2rWbNmmaoxNTVVK1asUJcuXSRJXbp00bJly5SWliZJ8vHxkYuLi9zd3eXn5yc/Pz85OjpmO1Z0dLR8fHxs2/VCFgAAAFAYEJ5yIDw8XHFxcXbb7NmzbcczMzM1duxYVa9eXb6+vvL09NSmTZt04sQJW5/OnTtr5cqVSk9PlyQtWrRITz31lBwcrjyKAwcOaMyYMXYB7eos0fnz529a4+LFi1WxYkXVrFlTklSrVi2VL19eS5cuzfH9RkZGKiUlxbadPHkyx2MAAAAABYVTfhdwL/Hw8FBwcLBd26+//mr799tvv61p06Zp6tSpql69ujw8PDRw4EBdunTJ1qd169YyDEPr1q1TvXr1tGPHDk2ZMsV2PC0tTVFRUXryySevub6rq+tNa5wzZ44OHz4sJ6f/e7RZWVmaO3euevbsmaP7tVqtslqtOToHAAAAKKgIT3lo165datOmje2VuaysLB09elShoaG2Pq6urnryySe1aNEi/fTTTwoJCVGdOnVsx+vUqaOEhIRrQpoZBw8e1LfffqvY2Fj5+vra2k+fPq2wsDD9+OOPqlKlilxcXJSZmXkLdwoAAAAUPoSnPFSpUiWtWLFCu3fvVtGiRfXOO+/ozz//tAtP0pVX91q1aqXDhw/bgtZVI0eOVKtWrVSuXDm1b99eDg4OOnDggA4dOqRx48bd8Ppz5szRAw88oEaNGl1zrF69epozZ47efvttBQYG6ptvvlFSUpI8PT3l6+tre20QAAAAQPb4izkPjRgxQnXq1FFERITCwsLk5+eX7Q/rNmnSRL6+vkpISNAzzzxjdywiIkJr167VF198oXr16unBBx/UlClTVL58+Rte+9KlS/r444/Vrl27bI+3a9dOCxYsUEZGhoYMGSJHR0eFhoaqRIkSdt/JAgAAAJA9i2EYRn4XgXtDamrqlVX3Bi6Tg9U9v8u5qaSJLfO7BAAAANzlrv6Nm5KSIm9v7xv2ZeYJAAAAAEwgPN1DFi1adM3vTF3dqlWrlt/lAQAAAAUaC0bcQx5//HHVr18/22POzs53uBoAAACgcCE83UO8vLzk5eWV32UAAAAAhRKv7QEAAACACcw8IccORUXcdCUSAAAAoKBh5gkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwARW20OO3Tdqkxys7tkeS5rY8g5XAwAAANwZzDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA85VD37t3Vtm3ba9pjY2NlsVh09uzZO15TdgIDAzV16lS7fYvFIovFIg8PD9WpU0fLly/PvwIBAACAewzhqRAZM2aMkpOT9f3336tevXrq1KmTdu/end9lAQAAAPcEwtNt8Ndff+npp59WmTJl5O7ururVq2vx4sW24x9++KFKly6trKwsu/PatGmj5557zra/evVq1alTR66urqpQoYKioqJ0+fLlXNfl5eUlPz8/Va5cWe+//77c3Nz0+eef53o8AAAAoDAhPN0GFy9e1P33369169bp0KFDeuGFF/Tss89q7969kqQOHTror7/+0rZt22znnD59Whs3blTnzp0lSTt27FDXrl01YMAAHTlyRLNmzVJMTIzGjx+fJzU6OTnJ2dlZly5dum6f9PR0paam2m0AAABAYUV4yoW1a9fK09PTbmvRooXteJkyZTRkyBDVqlVLFSpU0Msvv6zmzZtr2bJlkqSiRYuqRYsW+uSTT2znrFixQsWLF1d4eLgkKSoqSq+//rq6deumChUqqFmzZho7dqxmzZp1y/VfunRJ0dHRSklJUZMmTa7bLzo6Wj4+PrYtICDglq8NAAAA3KsIT7kQHh6uuLg4u2327Nm245mZmRo7dqyqV68uX19feXp6atOmTTpx4oStT+fOnbVy5Uqlp6dLkhYtWqSnnnpKDg5XHsmBAwc0ZswYu4D2/PPPKzk5WefPn89V3UOHDpWnp6fc3d315ptvauLEiWrZsuV1+0dGRiolJcW2nTx5MlfXBQAAAAoCp/wu4F7k4eGh4OBgu7Zff/3V9u+3335b06ZN09SpU1W9enV5eHho4MCBdq/ItW7dWoZhaN26dapXr5527NihKVOm2I6npaUpKipKTz755DXXd3V1zVXdr776qrp37y5PT0+VKlVKFovlhv2tVqusVmuurgUAAAAUNISn22DXrl1q06aNunTpIknKysrS0aNHFRoaauvj6uqqJ598UosWLdJPP/2kkJAQ1alTx3a8Tp06SkhIuCak3YrixYvn6XgAAABAYUJ4ug0qVaqkFStWaPfu3SpatKjeeecd/fnnn3bhSbry6l6rVq10+PBhW9C6auTIkWrVqpXKlSun9u3by8HBQQcOHNChQ4c0bty4O3k7AAAAAMR3nm6LESNGqE6dOoqIiFBYWJj8/Pyy/WHdJk2ayNfXVwkJCXrmmWfsjkVERGjt2rX64osvVK9ePT344IOaMmWKypcvf4fuAgAAAMA/WQzDMPK7CNwbUlNTr6y6N3CZHKzu2fZJmnj9BSgAAACAu83Vv3FTUlLk7e19w77MPAEAAACACYSne9CiRYuu+Z2pq1u1atXyuzwAAACgQGLBiHvQ448/rvr162d7zNnZ+Q5XAwAAABQOhKd7kJeXl7y8vPK7DAAAAKBQ4bU9AAAAADCBmSfk2KGoiJuuRAIAAAAUNMw8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGCCU34XgHuHYRiSpNTU1HyuBAAAAMgbV/+2vfq37o0QnmDaX3/9JUkKCAjI50oAAACAvHXu3Dn5+PjcsA/hCab5+vpKkk6cOHHT/2Dh3pWamqqAgACdPHlS3t7e+V0ObhOec+HAcy48eNaFA8/59jAMQ+fOnVPp0qVv2pfwBNMcHK58Rc7Hx4f/whYC3t7ePOdCgOdcOPCcCw+edeHAc857ZicGWDACAAAAAEwgPAEAAACACYQnmGa1WjVq1ChZrdb8LgW3Ec+5cOA5Fw4858KDZ1048Jzzn8UwsyYfAAAAABRyzDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMIT7Lz//vsKDAyUq6ur6tevr717996w//Lly1WlShW5urqqevXqWr9+/R2qFLciJ8/58OHDateunQIDA2WxWDR16tQ7VyhuSU6e80cffaRHHnlERYsWVdGiRdW0adOb/vcfd4ecPOdVq1apbt26KlKkiDw8PFSrVi0tXLjwDlaL3Mrp/z5ftWTJElksFrVt2/b2Fog8k5NnHRMTI4vFYre5urrewWoLH8ITbJYuXarBgwdr1KhR2r9/v2rWrKmIiAidOnUq2/67d+/W008/rZ49e+r7779X27Zt1bZtWx06dOgOV46cyOlzPn/+vCpUqKCJEyfKz8/vDleL3Mrpc46NjdXTTz+tbdu2ac+ePQoICNB//vMf/fbbb3e4cuRETp+zr6+vhg8frj179uiHH35Qjx491KNHD23atOkOV46cyOlzviopKUlDhgzRI488cocqxa3KzbP29vZWcnKybfvll1/uYMWFkAH8fw888IDx0ksv2fYzMzON0qVLG9HR0dn279ixo9GyZUu7tvr16xsvvvjiba0Ttyanz/mfypcvb0yZMuU2Voe8civP2TAM4/Lly4aXl5cxf/7821Ui8sCtPmfDMIzatWsbI0aMuB3lIY/k5jlfvnzZaNiwoTF79myjW7duRps2be5ApbhVOX3W8+bNM3x8fO5QdTAMw2DmCZKkS5cu6bvvvlPTpk1tbQ4ODmratKn27NmT7Tl79uyx6y9JERER1+2P/Jeb54x7T1485/PnzysjI0O+vr63q0zcolt9zoZhaOvWrUpISFCjRo1uZ6m4Bbl9zmPGjFHJkiXVs2fPO1Em8kBun3VaWprKly+vgIAAtWnTRocPH74T5RZahCdIkv73v/8pMzNTpUqVsmsvVaqU/vjjj2zP+eOPP3LUH/kvN88Z9568eM5Dhw5V6dKlr/k/SHD3yO1zTklJkaenp1xcXNSyZUu9++67atas2e0uF7mUm+e8c+dOzZkzRx999NGdKBF5JDfPOiQkRHPnztXq1av18ccfKysrSw0bNtSvv/56J0oulJzyuwAAwN1l4sSJWrJkiWJjY/nicQHk5eWluLg4paWlaevWrRo8eLAqVKigsLCw/C4NeeDcuXN69tln9dFHH6l48eL5XQ5uswYNGqhBgwa2/YYNG6pq1aqaNWuWxo4dm4+VFVyEJ0iSihcvLkdHR/3555927X/++ed1Fwnw8/PLUX/kv9w8Z9x7buU5T5o0SRMnTtSWLVtUo0aN21kmblFun7ODg4OCg4MlSbVq1VJ8fLyio6MJT3epnD7nxMREJSUlqXXr1ra2rKwsSZKTk5MSEhJUsWLF21s0ciUv/jfa2dlZtWvX1k8//XQ7SoR4bQ//n4uLi+6//35t3brV1paVlaWtW7fa/T8a/9SgQQO7/pK0efPm6/ZH/svNc8a9J7fP+a233tLYsWO1ceNG1a1b906UiluQV/99zsrKUnp6+u0oEXkgp8+5SpUqOnjwoOLi4mzb448/rvDwcMXFxSkgIOBOlo8cyIv/TmdmZurgwYPy9/e/XWUiv1eswN1jyZIlhtVqNWJiYowjR44YL7zwglGkSBHjjz/+MAzDMJ599lnj9ddft/XftWuX4eTkZEyaNMmIj483Ro0aZTg7OxsHDx7Mr1uACTl9zunp6cb3339vfP/994a/v78xZMgQ4/vvvzeOHTuWX7cAE3L6nCdOnGi4uLgYK1asMJKTk23buXPn8usWYEJOn/OECROML774wkhMTDSOHDliTJo0yXBycjI++uij/LoFmJDT5/xvrLZ378jps46KijI2bdpkJCYmGt99953x1FNPGa6ursbhw4fz6xYKPF7bg02nTp303//+VyNHjtQff/yhWrVqaePGjbYvLp44cUIODv83WdmwYUN98sknGjFihIYNG6ZKlSrps88+03333ZdftwATcvqcf//9d9WuXdu2P2nSJE2aNEmNGzdWbGzsnS4fJuX0Oc+YMUOXLl1S+/bt7cYZNWqURo8efSdLRw7k9Dn//fff6tu3r3799Ve5ubmpSpUq+vjjj9WpU6f8ugWYkNPnjHtXTp/1mTNn9Pzzz+uPP/5Q0aJFdf/992v37t0KDQ3Nr1so8CyGYRj5XQQAAAAA3O34vykAAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAwB0UFhamgQMH5ncZAIBcIDwBAO4a3bt3l8ViuWb76aef8mT8mJgYFSlSJE/Gyq1Vq1Zp7Nix+VrDjcTGxspisejs2bP5XQoA3HWc8rsAAAD+qXnz5po3b55dW4kSJfKpmuvLyMiQs7Nzjs/z9fW9DdXkjYyMjPwuAQDuasw8AQDuKlarVX5+fnabo6OjJGn16tWqU6eOXF1dVaFCBUVFReny5cu2c9955x1Vr15dHh4eCggIUN++fZWWlibpyoxKjx49lJKSYpvRGj16tCTJYrHos88+s6ujSJEiiomJkSQlJSXJYrFo6dKlaty4sVxdXbVo0SJJ0uzZs1W1alW5urqqSpUq+uCDD254f/9+bS8wMFDjxo1T165d5enpqfLly2vNmjX673//qzZt2sjT01M1atTQt99+azvn6gzaZ599pkqVKsnV1VURERE6efKk3bVmzJihihUrysXFRSEhIVq4cKHdcYvFohkzZujxxx+Xh4eHnn/+eYWHh0uSihYtKovFou7du0uSNm7cqIcfflhFihRRsWLF1KpVKyUmJtrGuvoZrVq1SuHh4XJ3d1fNmjW1Z88eu2vu2rVLYWFhcnd3V9GiRRUREaEzZ85IkrKyshQdHa2goCC5ubmpZs2aWrFixQ0/TwC4owwAAO4S3bp1M9q0aZPtsa+++srw9vY2YmJijMTEROOLL74wAgMDjdGjR9v6TJkyxfjyyy+N48ePG1u3bjVCQkKMPn36GIZhGOnp6cbUqVMNb29vIzk52UhOTjbOnTtnGIZhSDI+/fRTu+v5+PgY8+bNMwzDMI4fP25IMgIDA42VK1caP//8s/H7778bH3/8seHv729rW7lypeHr62vExMRc9x4bN25sDBgwwLZfvnx5w9fX15g5c6Zx9OhRo0+fPoa3t7fRvHlzY9myZUZCQoLRtm1bo2rVqkZWVpZhGIYxb948w9nZ2ahbt66xe/du49tvvzUeeOABo2HDhrZxV61aZTg7Oxvvv/++kZCQYEyePNlwdHQ0vvzyS1sfSUbJkiWNuXPnGomJiUZSUpKxcuVKQ5KRkJBgJCcnG2fPnjUMwzBWrFhhrFy50jh27Jjx/fffG61btzaqV69uZGZm2n1GVapUMdauXWskJCQY7du3N8qXL29kZGQYhmEY33//vWG1Wo0+ffoYcXFxxqFDh4x3333X+O9//2sYhmGMGzfOqFKlirFx40YjMTHRmDdvnmG1Wo3Y2Njrfp4AcCcRngAAd41u3boZjo6OhoeHh21r3769YRiG8eijjxoTJkyw679w4ULD39//uuMtX77cKFasmG1/3rx5ho+PzzX9zIanqVOn2vWpWLGi8cknn9i1jR071mjQoMF1a8ouPHXp0sW2n5ycbEj6f+3cTUiUWxzH8e+IaI6RvYmplAUSaqNipPmSWFEkohCRhbZoMRkWFhqGtlAYSqQXCyvoRUE3QbUIIt2I4sapBIWkYhIrYjZCL2hhYqZz7iKce+dqMnJv3eL+PquZc87853/O7jfPPI+pqanxjj169MgAZnh42LsPwDx+/Ni7xuVyGcD09vYaY4zJzMw0JSUlPt9dWFho8vLyfPZdXl7us6a7u9sAZmRk5Lt7MMaYd+/eGcA8ffrUGPPnGTU3N3vXPH/+3ADG5XIZY4wpKioyWVlZc9abmJgwVqvVPHz40GfcbreboqKieXsREflZdM+TiIj8UrZt28a1a9e870NDQwEYGBjA6XRSV1fnnZuenmZiYoLx8XGsViudnZ3U19fz4sULPn36xNTUlM/8P7Vp0ybv68+fP/Pq1SvsdjslJSXe8ampKcLCwhZUNykpyfs6IiICgMTExFljb9++ZdWqVQAEBgaSmprqXRMXF8fSpUtxuVykpaXhcrk4fPiwz/dkZWXR2Nj43T3NZ2hoiNraWnp7e3n//j0ejwcAt9uNzWabcy+RkZHevuPi4njy5AmFhYVz1n/58iXj4+Ps3LnTZ3xycpKUlBS/ehQR+dEUnkRE5JcSGhpKbGzsrPGxsTEcDgd79uyZNbdo0SLevHlDfn4+R44coa6ujuXLl9PT04PdbmdycnLe8GSxWDDG+IzN9fCEmSA30w9AU1MTmzdv9lk3c4+Wv/764AmLxfLdsZnA8m/6657mU1BQQExMDE1NTURFReHxeLDZbExOTvqsm6/vkJCQ79afOc/29naio6N95oKDg/3qUUTkR1N4EhGR38LGjRsZHBycM1gB9Pf34/F4aGhoICDg2/OQ7t6967MmKCiI6enpWZ8NDw9neHjY+35oaIjx8fF5+4mIiCAqKorXr19z4MCBhW7nH5uamqKvr4+0tDQABgcHGR0dJT4+HoD4+HicTicHDx70fsbpdJKQkDBv3aCgIACfc/rw4QODg4M0NTWRnZ0NQE9Pz4J7TkpKoqurC4fDMWsuISGB4OBg3G43OTk5C64tIvIzKDyJiMhvoba2lvz8fNasWcPevXsJCAhgYGCAZ8+ecebMGWJjY/n69StXrlyhoKAAp9PJ9evXfWqsXbuWsbExurq6SE5Oxmq1YrVa2b59O1evXiUjI4Pp6Wmqqqr8egy5w+Hg+PHjhIWFkZuby5cvX+jr62NkZIQTJ078qKMAvl3hOXbsGJcvXyYwMJCysjLS09O9YerkyZPs27ePlJQUduzYwYMHD7h37x6dnZ3z1o2JicFisdDW1kZeXh4hISEsW7aMFStWcPPmTSIjI3G73VRXVy+451OnTpGYmMjRo0cpLS0lKCiI7u5uCgsLWblyJZWVlVRUVODxeNiyZQsfP37E6XSyZMkSnxAoIvJf0aPKRUTkt7Br1y7a2tro6OggNTWV9PR0Ll26RExMDADJyclcvHiRs2fPYrPZuHXrFvX19T41MjMzKS0tZf/+/YSHh3Pu3DkAGhoaWL16NdnZ2RQXF1NZWenXPVKHDh2iubmZlpYWEhMTycnJobW1lXXr1v37B/A3VquVqqoqiouLycrKYvHixdy5c8c7v3v3bhobG7lw4QIbNmzgxo0btLS0sHXr1nnrRkdH43A4qK6uJiIigrKyMgICArh9+zb9/f3YbDYqKio4f/78gntev349HR0dDAwMkJaWRkZGBvfv3ycw8NtvuadPn6ampob6+nri4+PJzc2lvb39p5yniIg/LObvf/IWERGRX1prayvl5eWMjo7+162IiPyv6MqTiIiIiIiIHxSeRERERERE/KC/7YmIiIiIiPhBV55ERERERET8oPAkIiIiIiLiB4UnERERERERPyg8iYiIiIiI+EHhSURERERExA8KTyIiIiIiIn5QeBIREREREfGDwpOIiIiIiIgfFJ5ERERERET88AcBr4vs+C83KQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), forest.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t6U_BEF8W-FS"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YNf4EXHUW-FU"
   },
   "outputs": [],
   "source": [
    "\n",
    "storeResults('Random Forest', acc_train_forest, acc_test_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "JSFAbsgnAxqv",
    "outputId": "2828ce2e-95ec-4dfd-e7dd-5d3da152ea09"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier(alpha=0.001, hidden_layer_sizes=([100,100,100]))\n",
    "\n",
    "# fit the model \n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gyuSg6w_A4pN"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_mlp = mlp.predict(X_test)\n",
    "y_train_mlp = mlp.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UlDx0rDXatCl"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "z2ndgKQbA64_",
    "outputId": "40ddef62-9dd4-4d55-b5ba-9932ba07a0b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilayer Perceptrons: Accuracy on training Data: 0.866\n",
      "Multilayer Perceptrons: Accuracy on test Data: 0.856\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_mlp = accuracy_score(y_train,y_train_mlp)\n",
    "acc_test_mlp = accuracy_score(y_test,y_test_mlp)\n",
    "\n",
    "print(\"Multilayer Perceptrons: Accuracy on training Data: {:.3f}\".format(acc_train_mlp))\n",
    "print(\"Multilayer Perceptrons: Accuracy on test Data: {:.3f}\".format(acc_test_mlp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zjBgfI64Xubd"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N0fsq4yEXubk"
   },
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Multilayer Perceptrons', acc_train_mlp, acc_test_mlp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "oIIQGzxgAREc",
    "outputId": "fc27da07-7071-4fbf-9d05-05e514ad9b3e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier(learning_rate=0.4,max_depth=7)\n",
    "\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_fx9xbzfAUO-"
   },
   "outputs": [],
   "source": [
    "y_test_xgb = xgb.predict(X_test)\n",
    "y_train_xgb = xgb.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pwoDNqDIaxB9"
   },
   "source": [
    "**Performance Evaluation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "x1NNeI-NaxCA",
    "outputId": "d021057e-e9bc-487d-b584-9fb2492305de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost: Accuracy on training Data: 0.868\n",
      "XGBoost : Accuracy on test Data: 0.857\n"
     ]
    }
   ],
   "source": [
    "acc_train_xgb = accuracy_score(y_train,y_train_xgb)\n",
    "acc_test_xgb = accuracy_score(y_test,y_test_xgb)\n",
    "\n",
    "print(\"XGBoost: Accuracy on training Data: {:.3f}\".format(acc_train_xgb))\n",
    "print(\"XGBoost : Accuracy on test Data: {:.3f}\".format(acc_test_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_g2HQNotaxCQ"
   },
   "source": [
    "**Storing the results:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sFNo8jskaxCS"
   },
   "outputs": [],
   "source": [
    "storeResults('XGBoost', acc_train_xgb, acc_test_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3vsRppPv3rs"
   },
   "source": [
    "## **8. Comparision of Models**\n",
    "To compare the models performance, a dataframe is created. The columns of this dataframe are the lists created to store the results of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "colab_type": "code",
    "id": "RkOSzcfsv8Xl",
    "outputId": "82b2e437-b210-4b83-c3a0-dc9c5f65f9e0"
   },
   "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>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AutoEncoder</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "0           Decision Tree           0.812          0.820\n",
       "1           Random Forest           0.819          0.824\n",
       "2  Multilayer Perceptrons           0.865          0.858\n",
       "3  Multilayer Perceptrons           0.865          0.858\n",
       "4                 XGBoost           0.867          0.858\n",
       "5             AutoEncoder           0.002          0.001\n",
       "6                     SVM           0.800          0.806"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe\n",
    "results = pd.DataFrame({ 'ML Model': ML_Model,    \n",
    "    'Train Accuracy': acc_train,\n",
    "    'Test Accuracy': acc_test})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "colab_type": "code",
    "id": "eKheGBiHwDfK",
    "outputId": "8ff038a3-9eea-472a-e1e7-ac6be45c9882"
   },
   "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>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.865</td>\n",
       "      <td>0.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AutoEncoder</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "4                 XGBoost           0.867          0.858\n",
       "2  Multilayer Perceptrons           0.865          0.858\n",
       "3  Multilayer Perceptrons           0.865          0.858\n",
       "1           Random Forest           0.819          0.824\n",
       "0           Decision Tree           0.812          0.820\n",
       "6                     SVM           0.800          0.806\n",
       "5             AutoEncoder           0.002          0.001"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting the datafram on accuracy\n",
    "results.sort_values(by=['Test Accuracy', 'Train Accuracy'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5t9806vn601b"
   },
   "source": [
    "For the above comparision, it is clear that the XGBoost Classifier works well with this dataset.\n",
    "\n",
    "So, saving the model for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aCIIkZ7V3AFN"
   },
   "outputs": [],
   "source": [
    "# save XGBoost model to file\n",
    "import pickle\n",
    "pickle.dump(xgb, open(\"XGBoostClassifier1.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PbrNHP0o3QrD"
   },
   "source": [
    "**Testing the saved model:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "-ZEm_PS33QD-",
    "outputId": "a4195d7f-94ef-4bc7-a165-35ed2ed5493f"
   },
   "outputs": [
    {
     "data": {
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       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.4, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model from file\n",
    "loaded_model = pickle.load(open(\"XGBoostClassifier.pickle.dat\", \"rb\"))\n",
    "loaded_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3vy2egEdwkqZ"
   },
   "source": [
    "## **9. References**\n",
    "* https://blog.keras.io/building-autoencoders-in-keras.html\n",
    "* https://en.wikipedia.org/wiki/Autoencoder\n",
    "* https://mc.ai/a-beginners-guide-to-build-stacked-autoencoder-and-tying-weights-with-it/\n",
    "* https://github.com/shreyagopal/t81_558_deep_learning/blob/master/t81_558_class_14_03_anomaly.ipynb\n",
    "* https://machinelearningmastery.com/save-gradient-boosting-models-xgboost-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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