Part4_Websites_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5lnWf8VWaZsy",
   "metadata": {
    "id": "5lnWf8VWaZsy"
   },
   "source": [
    "# Part IV Modelling and Prediction of Phishing websites"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rg-vOPugb7ch",
   "metadata": {
    "id": "rg-vOPugb7ch"
   },
   "source": [
    "## IV.1 Data preparation:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8iiYDiGYafH8",
   "metadata": {
    "id": "8iiYDiGYafH8"
   },
   "source": [
    "**Load all the data after cleaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2a4a33c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "e2a4a33c",
    "outputId": "e6a0a71f-4b48-4a7d-9290-0196cfd02f69"
   },
   "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>NumberRedirects</th>\n",
       "      <th>checkWHOIS_exists</th>\n",
       "      <th>isinAlexa</th>\n",
       "      <th>NumbLinkToDomain</th>\n",
       "      <th>indexbyGoogle</th>\n",
       "      <th>Domain</th>\n",
       "      <th>Have_@</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>Domain_Length</th>\n",
       "      <th>occurence_tirets</th>\n",
       "      <th>Redirection</th>\n",
       "      <th>Number_%</th>\n",
       "      <th>TinyURL</th>\n",
       "      <th>iFrame</th>\n",
       "      <th>MouseOver</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>thehistoryofenglish.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>creativelab.org.ua</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>otelika.com</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>seeweg.de</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>au-paycccy.aseaoaameoy.uqfj.top</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NumberRedirects  checkWHOIS_exists  isinAlexa  NumbLinkToDomain  \\\n",
       "0                0                141          0                 0   \n",
       "1                0                195          0                 0   \n",
       "2                1                 66          0                 0   \n",
       "3                0                  0          0                 0   \n",
       "4                0                 11          0                 0   \n",
       "\n",
       "   indexbyGoogle                           Domain  Have_@  URL_Length  \\\n",
       "0              1          thehistoryofenglish.com     0.0        35.0   \n",
       "1              1               creativelab.org.ua     0.0        30.0   \n",
       "2              1                      otelika.com     0.0        23.0   \n",
       "3              1                        seeweg.de     0.0        21.0   \n",
       "4              1  au-paycccy.aseaoaameoy.uqfj.top     0.0        45.0   \n",
       "\n",
       "   Domain_Length  occurence_tirets  Redirection  Number_%  TinyURL  iFrame  \\\n",
       "0           23.0               1.0          6.0       1.0      0.0       1   \n",
       "1           18.0               1.0          6.0       1.0      0.0       1   \n",
       "2           11.0               1.0          6.0       1.0      0.0       0   \n",
       "3            9.0               1.0          6.0       1.0      0.0       0   \n",
       "4           31.0               2.0          5.0       1.0      0.0       1   \n",
       "\n",
       "   MouseOver  label  \n",
       "0          1    0.0  \n",
       "1          0    0.0  \n",
       "2          1    0.0  \n",
       "3          1    0.0  \n",
       "4          0    1.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "df = pd.read_csv('database_websites.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "O3CdrXVJZ2PQ",
   "metadata": {
    "id": "O3CdrXVJZ2PQ"
   },
   "source": [
    "**Number of links, Number of labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "N2piMxtGWrQm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N2piMxtGWrQm",
    "outputId": "cd572cae-fb52-487c-e798-902801cbd002"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 16)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Vc7h-YHXZ-xt",
   "metadata": {
    "id": "Vc7h-YHXZ-xt"
   },
   "source": [
    "**Replace all Empty values with 0** ( just to make sure,  normally this cell doesn't have to be executed if the data was generated from the previous code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b147c349",
   "metadata": {
    "id": "b147c349"
   },
   "outputs": [],
   "source": [
    "columns=df.columns\n",
    "df = df.fillna(value=0.0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8OjaBB4aOed",
   "metadata": {
    "id": "d8OjaBB4aOed"
   },
   "source": [
    "**Drop all the string labels and the labels that will not be useful for classificaton**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0361e2fb",
   "metadata": {
    "id": "0361e2fb"
   },
   "outputs": [],
   "source": [
    "dfsa = df.drop(['Domain'], axis = 1).copy() \n",
    "dfsa = dfsa.drop(['Redirection'], axis = 1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CDk_Fxh4akuD",
   "metadata": {
    "id": "CDk_Fxh4akuD"
   },
   "source": [
    "**Divide the database in X and y values where:**\n",
    "- **y** is the label of a url link representing whether it is a phishing or legitimate website, which is a value that will be predicted for new websites.\n",
    "- **X** which contains all the data of the features for all URLs without the labels to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5ab7b9d",
   "metadata": {
    "id": "f5ab7b9d"
   },
   "outputs": [],
   "source": [
    "y = dfsa['label']  #target variable\n",
    "X = dfsa.drop('label',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d_5-41Vvbo4z",
   "metadata": {
    "id": "d_5-41Vvbo4z"
   },
   "source": [
    "**Divide the database into X_train, X_test, y_train and y_test for prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbf13820",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fbf13820",
    "outputId": "29d2ba71-a72d-49a5-9761-6068dc290a9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4800, 13), (1200, 13))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 12)  #test-size 20%\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-SYvllc4elOR",
   "metadata": {
    "id": "-SYvllc4elOR"
   },
   "source": [
    "**Function: Report best scores for hyperparameters selection** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "BMEloEamIJJK",
   "metadata": {
    "id": "BMEloEamIJJK"
   },
   "outputs": [],
   "source": [
    "def report_best_scores(results, n_top=3):\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "                  results['mean_test_score'][candidate],\n",
    "                  results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eBjMBQMwb_2R",
   "metadata": {
    "id": "eBjMBQMwb_2R"
   },
   "source": [
    "## IV.2 Classification Models:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1178f915",
   "metadata": {
    "id": "1178f915"
   },
   "source": [
    "### **IV.2.1 Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f275df6",
   "metadata": {
    "id": "3f275df6"
   },
   "outputs": [],
   "source": [
    "# Decision Tree model \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# instantiate the model \n",
    "tree = DecisionTreeClassifier(max_depth = 5)\n",
    "# fit the model \n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "y_test_tree = tree.predict(X_test)\n",
    "y_train_tree = tree.predict(X_train)\n",
    "y_probas_tree = tree.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0726d1b0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0726d1b0",
    "outputId": "79a65025-d676-4960-ccb3-bbb5b395af23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: Accuracy on training Data: 0.915\n",
      "Decision Tree: Accuracy on test Data: 0.908\n"
     ]
    }
   ],
   "source": [
    "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:\", round(acc_train_tree,3))\n",
    "print(\"Decision Tree: Accuracy on test Data:\" ,round(acc_test_tree,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PW8pMOmvAvYy",
   "metadata": {
    "id": "PW8pMOmvAvYy"
   },
   "source": [
    "**IV.2.1.2 Feature importance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f3b6b9a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "9f3b6b9a",
    "outputId": "d2fed33c-8aca-4d51-83fc-ba9bb9efeccb"
   },
   "outputs": [
    {
     "data": {
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Z3QNfxmir9jOIe3ngZev1R62/s28/P6iqvcYZZ2l7Xcb95yxj1LGicSbi9y24joz7qar6h+VtUFXXt1nRZwP/kuScqnrnJNQiSZJmsdX9Hr4LgIOSbJhkI+BgerNsL0iyOUDfJd1vA69ubWsneSjwP8DDk2yeZD3gOePs5zpgbpK92vbrJtlhBbWdAxw98sBHq2Oi49wObLKC/Ywc2yFJHj6yrySPauvuaZeSaU/83lVV/wf4ALDbAGNLkqSOW60DX1VdAZwCXAZcCpxcVRfSuy/u/CRXAf/aur8eeFqSxfRC4Q5VdQ/wzrbtGcC14+znD8AhwHvbmAuBvVdQ3snAT4FFbZuXrMQ4pwAnjDy0MV6n9uTu24BzkiwCzgVGHgY5sdXwWWAn4LIkC+nd5/jPKzgGSZK0BkiV9+2vqYaGhmp4eHimy5AkSZMkyYKqGhrdvlrP8EmSJGnVre4PbXRCko8A+4xq/veq+uRM1CNJktYsBr5pUFWvmekaJEnSmstLupIkSR1n4JMkSeo4A58kSVLHGfgkSZI6zsAnSZLUcQY+SZKkjjPwSZIkdZyfw7cGW/zzJcx7y5kzXcZq5ebjD5jpEiRJmnTO8EmSJHWcgU+SJKnjDHySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HGzKvAlWZZkYZIfJLkqyRuTTOkxJDk6yctWYrt9k5wxFTW18TdN8jfTtT9JkjR7zbYPXr67quYDJHk48DlgDvBPU7XDqjphqsZeRZsCfwN8dIbrkCRJq7lZNcPXr6p+BRwFvDY96yf5ZJLFSa5M8jSAJIcn+VqSbyS5Kclr28zglUkuSbJZ6/fKJJe3mcMvJ9mwtR+X5Ni2fF6S9ya5LMn1SZ480bqT7J/k4iRXJPlSko1b+81J3tHaFyfZrrXPTXJua/9Ykp8k2QI4HtimzXi+vw2/cZLTklyb5LNJsoqnWZIkdcCsDXwAVXUjvWN4OPCa1rYT8GLgU0nWb113BF4C7AG8G7irqnYFLgZGLtd+paqeUFW7AD8Ejhxnt+tU1R7AG5jgzGILam8D9quq3YBh4I19XW5t7f8FHNva/gn4Tmv/KrB1a38L8OOqml9Vb2ptu7a6tgceA+wzRg1HJRlOMrzsriUTKV+SJM1SszrwNSOzWE8CPgNQVdcCPwEe19Z9t6pur6pbgCXAN1r7YmBeW94xyfeSLAYOA3YYZ39faa8L+rYd1J70wtiFSRYCLwcetYKxnwR8oR3XWcBvljP+ZVX1s6q6D1g4Vn1VdWJVDVXV0Nobzplg+ZIkaTaabffwPUCSxwDLgF9xf/Aby9K+5fv63t/H/efgFOCgqroqyeHAvisYaxkTP38Bzq2qF09g7Ilclu0/zpWpT5IkddCsneFLMhc4AfjPqirgAnozcyR5HL1Ln9dNYMhNgF8mWXdknClwCbBPkscCJNmw1bo83wde0PrvDzystd9Or2ZJkqTlmm0zQBu0S6HrAvfSu4T7r23dR4ET2iXZe4HDq2rpBJ5b+EfgUnqXghczOWHqGUl+1vf+UOBw4PNJ1mttbwOuX84Y72j9XwicD/wSuL0d24VJrga+BZw5CfVKkqQOSm9yTKurFgyXVdW9SfYC/mvko2lW1XpbbltbvvxDkzFUZ9x8/AEzXYIkSSstyYKqGhrdPttm+NZEWwOntg+Y/gPwyhmuR5IkzTIGvlWU5JnAe0c131RVB0/G+FV1A72PW5EkSVopBr5VVFVnA2fPdB2SJEnjmbVP6UqSJGkwA8/wJdkA2LqqJvJRJ1qN7bTVHIZ9SEGSpM4baIYvyXPpfXPDWe39/CSnT2FdkiRJmiSDXtI9jt730P4WoKoWMvGvFZMkSdIMGDTw3VtVS6a0EkmSJE2JQe/huzrJS4C1k2wLHANcNHVlSZIkabIMOsP3OmAHYCnwOWAJ8IYpqkmSJEmTaIVfrZZkbeDsqtpvekrSdPGr1SRJml5T/RWe43212gpn+KpqGXBXkjlTUpkkSZKm1KD38P0eWJzkXODOkcaqOmZKqpIkSdKkGTTwndl+JEmSNMsMFPiq6lNTXYgkSZKmxkCBL8lNwIOe7qiqx0x6RZIkSZpUg17S7X/aY33gUGCzyS9HkiRJk22gz+Grqtv6fn5eVR8Cnj61pUmSJGkyDBT4kuzW9zOU5GhgkymubUolmZfk6lFtxyU5NskpSW5KsjDJVUme0dfnvCQP+nybMcbfN8kZU1F7G3/TJH8zXfuTJEmz16CXdD/Yt3wvcBPwgskvZ7Xypqo6LcnTgBOBbWe6oFE2Bf4G+OgM1yFJklZzgwa+I6vqxv6GJI+egnpWRxcDW03WYEn2B94BrAf8GDiiqu5IcjPwKeC5wLrAoVV1bZK59L7ObnPgcuBZwO7A8cA2SRYC59L72JyNk5wG7AgsAF5aK/oqFUmS1HmDfpfuaQO2ddGzgK9NxkBJtgDeBuxXVbsBw8Ab+7rc2tr/Czi2tf0T8J3W/lVg69b+FuDHVTW/qt7U2nal9x3H2wOPAfYZo4ajkgwnGV5215LJOCxJkrSaW+4MX5LtgB2AOUme17fqofSe1p3Nxpv5Gml/f5L3AQ8H9pykfe5JL4xdmATgIfRmEEd8pb0uAEbO95OAgwGq6qwkv1nO+JdV1c8A2szfPOD7/R2q6kR6l6hZb8ttnf2TJGkNsKJLun8GPIfe/WLP7Wu/HXjlFNU0XW4DHjaqbTN69ycCvIleADuG3qXW3SdhnwHOraoXj7N+aXtdxv3/bTKB8Zf2LfePIUmS1mDLvaRbVV+vqiOA51TVEX0/x1TVRdNU45SoqjuAX448gZtkM3qXb7/f1+c+4N+BtZI8cxJ2ewmwT5LHtn1umORxK9jm+7QHZNr9fyMh9XZm+ZPSkiRpegw6A3RlktfQu7z7x0u5VfWKKalq+rwM+EiSkaeQ31FVP26XWwGoqkryz8DfA2e35jOT3NOWL66qQ8cZ/xlJftb3/lDgcODzSdZrbW8Drl9Oje9o/V8InA/8Eri9qpYmubB9tMy38LuOJUnSODLIQ5xJvgRcC7wEeCdwGPDDqnr91JanFgyXVdW9SfYC/quq5k/G2OttuW1t+fIPTcZQkiRpADcff8CUjp9kQVU96POCB53he2xVHZrkwKr6VJLPcf9sl6bW1sCpSdYC/sDsv3dSkiRNs0ED38jly98m2RH4f/SeAF3jtXv73juq+aaqOngyxq+qG+h93IokSdJKGTTwnZjkYcA/AqcDGwNvn7KqZpGqOhtnOyVJ0mpsoMBXVSe3xfPpfaCvJEmSZomBAl+SRwDvAR5ZVX+RZHtgr6r6+JRWpym101ZzGJ7im0clSdLMG/Sr1U6hd9nyke399fS+wkuSJEmruUED3xZVdSpwH0BV3UvvmxwkSZK0mhs08N2ZZHPa98wm2RNYMmVVSZIkadIM+pTuG+k9nbtNkguBucAhU1aVJEmSJs1yA1+Sravqp1V1RZKnAn8GBLiuqu5Z3raSJElaPazoku7X+pa/WFU/qKqrDXuSJEmzx4oCX/qW/fw9SZKkWWhFga/GWZYkSdIssaKHNnZJ8jt6M30btGXa+6qqh05pdZIkSVplyw18VbX2dBUiSZKkqTHo5/BJkiRpljLwSZIkdZyBT5IkqeMMfJMgyR2j3h+e5D+ncf9PT/KNJIuTXJzkDUm8/1KSJAEGvlkvyauBvwf+oap2AvYDNgS+kCTL3ViSJK0RDHxTLMlzk1ya5Mok/53kEUnWSnJzkk37+v2orZub5MtJLm8/+yxn7G2BFwDPqaqrAarqzqp6D3Atft+xJElixZ/Dp8FskGRh3/vNgNPb8veBPauqkvw18PdV9XdJvg4cDHwyyROBm6vqf5J8Dvi3qvp+kq2Bs4HHj7PfI4D3APcl+QiwJ/AN4GHAccApwJf6N0hyFHAUwNZbb71qRy1JkmYFZ/gmx91VNX/kB3h737o/Ac5Oshh4E7BDa/8i8MK2/KL2HnqXZP+zBcjTgYcm2WSc/e4CXAI8F7inqnYHfgfMqarfAA/arqpOrKqhqhqaO3fuyh2tJEmaVQx8U+/DwH+2++teBazf2i8GHptkLnAQ8JXWvhawV1+A3Kqqbh9n7ADLgO2As1rbtwCSrAcsneyDkSRJs4+Bb+rNAX7ell8+0lhVBXwV+Ffgh1V1W1t1DvDakX5J5i9n7MXAXsB1wP6t7Zn0guCbgdNWvXxJkjTbGfim3nHAl5J8D7h11LovAi/l/su5AMcAQ0kWJbkGOHo5Y3+K3j18Z9K7j3ABsCm9y8Z3AJ+YjAOQJEmzW3oTTZqtkhxLb5bvb6vqp0k2AJ4HXFBV/3d52w4NDdXw8PB0lClJkqZBkgVVNTS63ad0Z7mq+kCSZwMnJXk4sAT4PPdfRpYkSWs4A98skOQI4PWjmi+sqtcAVNU3gW9Oe2GSJGlWMPDNAlX1SeCTM12HJEmanXxoQ5IkqeMMfJIkSR1n4JMkSeo4A58kSVLHGfgkSZI6zsAnSZLUcQY+SZKkjjPwSZIkdZyBT5IkqeMMfJIkSR1n4JMkSeo4A58kSVLHGfgkSZI6zsAnSZLUcQY+SZKkjpvVgS/JRRPsv2+SM1ZyX6ckOWSC2zwryWVJrk2yMMkXk2y9Mvtfzj5W+pgkSdKaYZ2ZLmBVVNXeM13DeJLsCHwY+Muq+mFr+0tgHvDTGSxNkiStYWb7DN8d7XXfJOclOa3Npn02Sdq6Z7W27wPP69t2oySfSHJ5kiuTHNja/yPJ29vyM5NckGTkPO2X5HtJrk/ynNbne0nm9417YZKdgTcD7xkJewBVdXpVXdD6zU9ySZJFSb6a5GEraH9Ca7s4yfuTXD3G+RjzmCRJ0pptVge+UXYF3gBsDzwG2CfJ+sBJwHOBJwP/q6//W4HvVNUTgKcB70+yEfAW4IVJngb8B3BEVd3XtpkHPBU4ADihjX8ycDhAkscB61XVImAH4Irl1Ptp4M1VtTOwGPinFbR/Eji6qvYClo0z5njH9EdJjkoynGT4lltuWU55kiSpK7oU+C6rqp+1cLaQXjjbDripqm6oqgL+T1///YG3JFkInAesD2xdVXcBrwTOBf6zqn7ct82pVXVfVd0A3NjG/xLwnCTrAq8AThldWJLN2z181yc5NskcYNOqOr91+RTwlOW0bwpsUlUj9yx+bpxzMOYx9XeoqhOraqiqhubOnTvOMJIkqUtm9T18oyztW17G/cdW4/QP8Pyqum6MdTsBtwGPHNU+eqyqqruSnAscCLwAGGrrfgDsBlxVVbcB85McC2w8yMGMUeug/cY7JkmStIbq0gzfWK4FHp1km/b+xX3rzgZe13ev367t9VHA39G7RPwXSZ7Yt82hSdZq4z0GGAlWJ9O7/Ht5Vf26tb0PeGuSx/dtvyFAVS0BfpPkya39r4Dzl9P+G+D2JHu29heNc7xjHpMkSVqzdWmG70Gq6vdJjgLOTHIr8H1gx7b6XcCHgEUtIN2c5LnAx4Fjq+oXSY4ETknyhLbNdcD5wCPo3U/3+7afBUl+R+8+u5F9L07yeuDTSTahN2P4U+6/J+/l9O4D3JDe5eEjVtB+JHBSkjvpXa5dMsYhP+iYgOdM7KxJkqSuSe/WNq2KJI+kF8K263vAY7L3sXFVjTyV/BZgy6p6/aqMOTQ0VMPDw5NSnyRJmnlJFlTV0Oj2rl/SnXJJXgZcCrx1qsJec0B78ONqek8c//MU7kuSJHVIpy/pToeq+jS9j1KZ6v18EfjiVO9HkiR1jzN8kiRJHWfgkyRJ6jgDnyRJUscZ+CRJkjrOwCdJktRxBj5JkqSOM/BJkiR1nIFPkiSp4wx8a7DFP1/CvLecyby3nDnTpUiSpClk4JMkSeo4A58kSVLHGfgkSZI6zsAnSZLUcQY+SZKkjjPwSZIkdZyBT5IkqeOmLPAlqSQf7Ht/bJLjJmnsU5IcMkb7vknOGKP95CTbT3TMJF9NsjDJj5IsacsLk+w9xvbHJfl5W39Dkq+saJ+TIclFU70PSZI0u03lDN9S4HlJtpjCfQykqv66qq5Zie0Orqr5wF8D36uq+e1nvJD1b239tsAXge8kmbvylQ9U44PCpyRJUr+pDHz3AicCfzt6xejZtCR3tNd9k5yf5NQk1yc5PslhSS5LsjjJNn3D7Jfke63fc5ZXSJLzkgyN7CvJu5NcleSSJI8Yo/+7Wo0POj9JHpXk20kWtdetx9pnVX0ROAd4SdvuGUmubMfxiSTrtfabk7wnycVJhpPsluTsJD9OcnTrs3Hb1xVt+wPHOXfnJTktybVJPpskyzsvkiRpzTDV9/B9BDgsyZwJbLML8HpgJ+CvgMdV1R7AycDr+vrNA54KHACckGT9AcffCLikqnYBLgBe2b8yyfuAhwNHVNV9Y2z/n8Cnq2pn4LPAfyxnX1cA27XaTgFeWFU7AesAr+7r93+rai/ge63fIcCewDvb+t8DB1fVbsDTgA+OE+Z2Bd4AbA88BthndIckR7VgObzsriXLKV2SJHXFlAa+qvod8GngmAlsdnlV/bKqlgI/pjdLBrCYXsgbcWpV3VdVNwA3AtsNOP4fgJH7/BaMGvMfgU2r6lVVVeNsvxfwubb8GeBJy9nXSCj7M+Cmqrq+vf8U8JS+fqe318XApVV1e1XdAvw+yaZtnPckWQT8N7AV8KCZSeCyqvpZC6oLRx0bAFV1YlUNVdXQ2htOJIdLkqTZajqe0v0QcCS9mbUR947su81UPaRv3dK+5fv63t9Hb2ZsxOhANl5AG+2evjC3bNSYlwO7J9lswLFWtN9dgR9yf/AbT/8xjj7+dYDDgLnA7u2ewv8BxprR7N929LFJkqQ11JQHvqr6NXAqvdA34mZg97Z8ILDuSgx9aJK12n19jwGuW5U6m7OA44Ezk2wyTp+LgBe15cOA74/VKcnzgf2BzwPXAvOSPLat/ivg/AnUNQf4VVXdk+RpwKMmsK0kSVrDTdcM0AeB1/a9Pwn4epLLgG8Dd67EmNfRC02PAI6uqt+329qekeRnff0OncigVfWlFvZOT/Lsqrp7VJdjgE8keRNwC3BE37q/TfJSerOZVwNPb5dmSXIE8KUk69CbSTxhAmV9FvhGkmF6l2qvncgxSZKkNVvGv1VNXbfeltvWli//EAA3H3/AzBYjSZJWWZIFVTU0ut1v2pAkSeo4A58kSVLHGfgkSZI6zsAnSZLUcX5O2xpsp63mMOzDGpIkdZ4zfJIkSR1n4JMkSeo4A58kSVLHGfgkSZI6zsAnSZLUcQY+SZKkjjPwSZIkdZyBT5IkqeMMfJIkSR1n4JMkSeo4A58kSVLHGfgkSZI6zsAnSZLUcQa+CUpy0QrWfzPJpgOMs06SW5P8y6j285IMrWKZkiRJf2Tgm6Cq2nsF659dVb8dYKj9geuAFyTJZNQmSZI0FgPfBCW5o71umeSCJAuTXJ3kya395iRbJJmX5IdJTkrygyTnJNmgb6gXA/8O/BTYc5x97Z/k4iRXJPlSko2TPCrJDW0fayX5XpL9W/+vJVnQ9nfU1J4JSZI0Wxj4Vt5LgLOraj6wC7BwjD7bAh+pqh2A3wLPB2jB7xnAGcDn6YW/B0iyBfA2YL+q2g0YBt5YVT8B3gucAPwdcE1VndM2e0VV7Q4MAcck2XyMcY9KMpxk+JZbblnJQ5ckSbOJgW/lXQ4ckeQ4YKequn2MPjdV1cK2vACY15afA3y3qu4CvgwcnGTtUdvuCWwPXJhkIfBy4FEAVXUysAlwNHBs3zbHJLkKuAT4U3qB8wGq6sSqGqqqoblz507ogCVJ0uxk4FtJVXUB8BTg58BnkrxsjG5L+5aXAeu05RcD+yW5mV4Q3Bx42qhtA5xbVfPbz/ZVdSRAkg2BP2n9Nm5t+wL7AXtV1S7AlcD6q3KMkiSpGwx8KynJo4BfVdVJwMeB3Qbc7qHAk4Ctq2peVc0DXsODL+teAuyT5LFtuw2TPK6tey/wWeDtwEmtbQ7wm6q6K8l2jHNfoCRJWvOss+IuGse+wJuS3APcAYw1wzeW5wHfqar+2b+vA+9Lst5IQ1XdkuRw4PN97W9LsiXwBGCfqlqW5PlJjgA+BxydZBG9p38vWYVjkyRJHZKqmukaNEOGhoZqeHh4psuQJEmTJMmCqnrQ5/l6SVeSJKnjDHySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HEGPkmSpI4z8EmSJHWcgU+SJKnjDHySJEkd53fprsEW/3wJ895y5kyXodXAzccfMNMlSJKmkDN8kiRJHWfgkyRJ6jgDnyRJUscZ+CRJkjrOwCdJktRxBj5JkqSOM/BJkiR13LQHviSnJDlkKsZJcmCSr/W9/4ckP+p7/9wkp7flm5Ns0bdu3yRn9L0/KMmiJNcmWZzkoLH2neQ5Sa5MclWSa5K8alWPrW8/70yy33LWH5Rk+8nanyRJ6qauffDyRcCJfe/3An6X5OFV9Stgb+DCFQ2SZBfgA8CfV9VNSR4NnJvkxqpa1Ndv3ba/ParqZ0nWA+ZN1sFU1dtX0OUg4AzgmsnapyRJ6p4pn+FL8rI2U3ZVks+05qckuSjJjf2zdEnelOTy1v8dKxijfx/vSnIKcBuwJMlj26qtgC/TC3q014sGKPtY4D1VdRNAe/0X4E2j+m1CLzTf1votrarrlnMu5ib5cjvGy5Ps09q/nuRlbflVST7blvtnEo9vM4iLknwgyd7AXwLvT7IwyTZJjunr84UBjlOSJK0BpnSGL8kOwFuBfarq1iSbAf8KbAk8CdgOOB04Lcn+wLbAHkCA05M8hV6YGj1G/z7eB8wBjqiqSnIRsHeStYEbgEuAZ7bLtTsDl/dt/t0ky9ryxsC1bXkHejN8/YaB1/Q3VNWv2yXinyT5Nr3Zts9X1X3jnJJ/B/6tqr6fZGvgbODxwFHAhUluAv4O2HPUMW4GHAxs145x06r6bdv3GVV1Wuv3FuDRVbU0yaZjFZDkqLY/1n7o3HHKlCRJXTLVl3SfDpxWVbfCHwMSwNdaKLomySNa3/3bz5Xt/cb0AuAuo8foG/8fgUur6qi+tgvpzeStDVwMXAa8HdgVuK6qft/X92kj4ybZl97MHvQCZ406lrHaqKq/TrITsF/b/s+Bw8c5H/sB27dzAPDQJJtU1f8keTvwXeDgUccI8Dvg98DJSc6kFyzHsgj4bLuP8WtjdaiqE2mXvdfbctsHHY8kSeqeqb6kO2ZIApaO6jPy+i9VNb/9PLaqPr6cMaA3W7f7qFm/i+gFvr2Bi6vqdmB9YF8GuH+v+QEwNKptN8a5V66qFlfVv9ELe89fzrhrAXv1HeNWrT6AnejNZj5yjPHvpTfz+WV69+2dNc74BwAfAXYHFiTp2j2akiRpJUx14Ps28IIkm8MfL02O52zgFUk2bn23SvLwFYxxFnA8cGaSTVrbNfRC05O5f7ZwIXA0g92/B73Luf+QZF7b5zzgfwMf7O+UZOM2MzhiPvCT5Yx7DvDavu3nt9c9gL+gNwt5bHtI5AH7AeZU1TeBN7T9ANxO7z5CkqwF/GlVfRf4e2BTerOkkiRpDTelM0BV9YMk7wbOb/fKXbmcvuckeTxwcbvkeQfw0nHGOLxvuy+1sHd6kmdX1d1JLqUXkO5p3S6md9/aQIGvqhYmeTPwjfYk7j3A31fVwlFdA/x9ko8BdwN3Mv7lXIBjgI8kWUTv3F+Q5PXASfTuQfxFkr8DPpHk6X3bbQJ8Pcn6bZ9/29q/AJyU5BjgRcDHk8xpff6tqn47yPFKkqRuS5W3ca2p1tty29ry5R+a6TK0Grj5+ANmugRJ0iRIsqCqRt+W5jdtSJIkdZ039U+BJG8FDh3V/KWqevdM1CNJktZsBr4p0IKd4U6SJK0WvKQrSZLUcc7wrcF22moOw96sL0lS5znDJ0mS1HEGPkmSpI4z8EmSJHWcgU+SJKnjDHySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HEGPkmSpI4z8EmSJHWcgU+SJKnjDHySJEkdt1oFviSV5IN9749NctwkjX1KkkNWcYxlSRYmuTrJN5JsOsHt72ivj0xy2qrU0jfmG5JsOBljSZKkblqtAh+wFHheki1mupB+SdZui3dX1fyq2hH4NfCalRmvqn5RVQ8Kn0nWWYnh3gAY+CRJ0rhWt8B3L3Ai8LejV4yeoeubLds3yflJTk1yfZLjkxyW5LIki5Ns0zfMfkm+1/o9p22/dpL3J7k8yaIkr+ob97tJPgcsHqPWi4GtWt9tkpyVZEEbf7vW/ugkF7ex39VX+7wkV7flw5N8Kck3gHOSbJTkE22bK5Mc2FfnB9oxLUryuiTHAI8EvttqXbudp6tbvwedR0mStOZZmRmlqfYRYFGS901gm12Ax9ObdbsROLmq9kjyeuB19GbBAOYBTwW2oReSHgu8DFhSVU9Ish5wYZJzWv89gB2r6qb+nbUZv2cAH29NJwJHV9UNSZ4IfBR4OvDvwH9V1aeTLG82cC9g56r6dZL3AN+pqle0S8aXJfnvVuejgV2r6t4km7X+bwSeVlW3Jtkd2KrNQDLWJeckRwFHAWy99dbLPamSJKkbVrcZPqrqd8CngWMmsNnlVfXLqloK/BgYCWyL6YW8EadW1X1VdQO9YLgdsD/wsiQLgUuBzYFtW//LRoW9DVq/24DNgHOTbAzsDXyprfsYsGXrvw/w+bb8meXUf25V/bot7w+8pY11HrA+sDWwH3BCVd0L0Ne/343AY5J8OMmzgN+N7lBVJ1bVUFUNzZ07dzklSZKkrlgdZ/gAPgRcAXyyr+1eWkBNEuAhfeuW9i3f1/f+Ph54jDVqPwUEeF1Vnd2/Ism+wJ2j+t9dVfOTzAHOoHcP3ynAb6tq/jjHMnqfY+nfT4DnV9V1o+rJisaqqt8k2QV4ZqvtBcArBti/JEnqsNVuhg/+OHt1KnBkX/PNwO5t+UBg3ZUY+tAka7X7+h4DXAecDbw6yboASR6XZKMV1LeE3gzkscDdwE1JDm3bp4UugAuBF7Xlwwas8WzgdS3gkWTX1n4OcPTIgx1JNmvttwObtLYtgLWq6svAPwK7DbhPSZLUYatl4Gs+CPQ/rXsS8NQklwFP5MGzb4O4Djgf+Ba9e+5+D5wMXANc0R6k+BgDzHxW1ZXAVfQC3WHAkUmuAn5AL5ACvB54TZLLgTkD1vguemF2Uatn5GGPk4GftvargJe09hOBbyX5Lr2HSM5rl4NPAf5hwH1KkqQOS9UgVxzVRUNDQzU8PDzTZUiSpEmSZEFVDY1uX51n+CRJkjQJDHySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HEGPkmSpI4z8EmSJHWcgU+SJKnjDHySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HEGPkmSpI4z8EmSJHWcgU+SJKnjDHySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HGpqpmuQTMkye3AdTNdx2pqC+DWmS5iNeR5GZvnZXyem7F5XsbmeRnfoOfmUVU1d3TjOpNfj2aR66pqaKaLWB0lGfbcPJjnZWyel/F5bsbmeRmb52V8q3puvKQrSZLUcQY+SZKkjjPwrdlOnOkCVmOem7F5XsbmeRmf52ZsnpexeV7Gt0rnxoc2JEmSOs4ZPkmSpI4z8EmSJHWcgW8NkORZSa5L8qMkbxljfZL8R1u/KMluM1HndBvgvGyX5OIkS5McOxM1zpQBzs1h7XdlUZKLkuwyE3VOtwHOy4HtnCxMMpzkSTNR53Rb0Xnp6/eEJMuSHDKd9c2kAX5n9k2ypP3OLEzy9pmoc7oN8jvTzs3CJD9Icv501zgTBvh9eVPf78rV7c/TZgMNXlX+dPgHWBv4MfAY4CHAVcD2o/o8G/gWEGBP4NKZrns1OS8PB54AvBs4dqZrXs3Ozd7Aw9ryX/g788c+G3P/vdE7A9fOdN2rw3np6/cd4JvAITNd9+pyboB9gTNmutbV8LxsClwDbN3eP3ym614dzsuo/s8FvjPo+M7wdd8ewI+q6saq+gPwBeDAUX0OBD5dPZcAmybZcroLnWYrPC9V9auquhy4ZyYKnEGDnJuLquo37e0lwJ9Mc40zYZDzcke1v4mBjYA14am4Qf6OAXgd8GXgV9NZ3Awb9NysaQY5Ly8BvlJVP4Xe38fTXONMmOjvy4uBzw86uIGv+7YC/m/f+5+1ton26Zo18ZgHNdFzcyS9GeKuG+i8JDk4ybXAmcArpqm2mbTC85JkK+Bg4IRprGt1MOifpb2SXJXkW0l2mJ7SZtQg5+VxwMOSnJdkQZKXTVt1M2fgv3uTbAg8i97/RA3Er1brvozRNnrWYZA+XbMmHvOgBj43SZ5GL/CtCfeqDXRequqrwFeTPAV4F7DfVBc2wwY5Lx8C3lxVy5KxunfWIOfmCnrffXpHkmcDXwO2nerCZtgg52UdYHfgGcAGwMVJLqmq66e6uBk0kX+XngtcWFW/HnRwA1/3/Qz40773fwL8YiX6dM2aeMyDGujcJNkZOBn4i6q6bZpqm0kT+p2pqguSbJNki6rq8pfBD3JehoAvtLC3BfDsJPdW1dempcKZs8JzU1W/61v+ZpKP+jvzxz63VtWdwJ1JLgB2Aboc+Cbyd8yLmMDlXPCS7prgcmDbJI9O8hB6vySnj+pzOvCy9rTunsCSqvrldBc6zQY5L2uqFZ6bJFsDXwH+quP/x91vkPPy2LRU0552fwjQ9TC8wvNSVY+uqnlVNQ84DfibNSDswWC/M/+r73dmD3r/Lq/xvzPA14EnJ1mnXb58IvDDaa5zug3071KSOcBT6Z2jgTnD13FVdW+S1wJn03sC6BNV9YMkR7f1J9B7au7ZwI+Au4AjZqre6TLIeUnyv4Bh4KHAfUneQO+Jqd+NN24XDPg783Zgc+Cj7d+qe6tqaKZqng4Dnpfn0/ufp3uAu4EX9j3E0UkDnpc10oDn5hDg1Unupfc78yJ/Z+qEqvphkrOARcB9wMlVdfXMVT31JvBn6WDgnDb7OTC/Wk2SJKnjvKQrSZLUcQY+SZKkjjPwSZIkdZyBT5IkqeMMfJIkSR1n4JOkASRZlmRh38+8lRjjoCTbT0F5JHlkktOmYuzl7HN++3YISas5P4dPkgZzd1XNX8UxDgLOAK4ZdIMk61TVvSvqV1W/oPeZbtMiyTrAfHrfovHN6dqvpJXjDJ8kraQkuyc5v325+9lJtmztr0xyeZKrknw5yYZJ9gb+Enh/myHcpn0x/FDbZoskN7flw5N8Kck3gHOSbJTkE23MK5McOEYt85Jc3bf915J8I8lNSV6b5I1t20uSbNb6nZfkQ0kuSnJ1+6YHkmzWtl/U+u/c2o9LcmKSc4BPA+8EXtiO54VJ9mhjXdle/6yvnq8kOSvJDUne11f3s5Jc0c7Vt1vbCo9X0sQ4wydJg9kgycK2fBPwAuDDwIFVdUuSFwLvBl4BfKWqTgJI8s/AkVX14SSnA2dU1Wlt3fL2txewc1X9Osl7gO9U1SuSbApcluS/V/BJ+zsCuwLr0/sWnTdX1a5J/g14GfCh1m+jqto7yVOAT7Tt3gFcWVUHJXk6vXA3v/XfHXhSVd2d5HBgqKpe247nocBT2jcG7Ae8h963j9C23xVYClyX5MPA74GT2jY3jQRR4K0rcbySlsPAJ0mDecAl3SQ70gtH57bgtjYw8h3UO7agtymwMb2vSpqoc6vq1215f+Avkxzb3q8PbM3yv1v0u1V1O3B7kiXAN1r7YmDnvn6fB6iqC5I8tAWsJ9GCWlV9J8nm6X1/J8DpVXX3OPucA3wqybZAAev2rft2VS0BSHIN8CjgYcAFVXVT29eqHK+k5TDwSdLKCfCDqtprjHWnAAdV1VVtFmzfcca4l/tvrVl/1Lr+2awAz6+q6yZQ39K+5fv63t/HA//uH/39mtX2N9pIv+XNsr2LXtA8uD3Uct449SxrNWSM/cPKHa+k5fAePklaOdcBc5PsBZBk3SQ7tHWbAL9Msi5wWN82t7d1I26md4kUlv/AxdnA69KmEpPsuurl/9EL25hPApa0WbgLaHUn2Re4tap+N8a2o49nDvDztnz4APu+GHhqkke3fY1c0p3K45XWSAY+SVoJVfUHeiHtvUmuAhYCe7fV/whcCpwLXNu32ReAN7UHEbYBPgC8OslFwBbL2d276F0eXdQezHjXJB7Kb9r+TwCObG3HAUNJFgHHAy8fZ9vvAtuPPLQBvA/4lyQX0rvEvVxVdQtwFPCVdg6/2FZN5fFKa6RUjTWbLknquiTnAcdW1fBM1yJpajnDJ0mS1HHO8EmSJHWcM3ySJEkdZ+CTJEnqOAOfJElSxxn4JEmSOs7AJ0mS1HH/H+30XOWXnAsnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yRSVPk6m1419",
   "metadata": {
    "id": "yRSVPk6m1419"
   },
   "source": [
    "**IV.2.1.3 Parameters tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "UNJcN4oJ0qvm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "UNJcN4oJ0qvm",
    "outputId": "80841ded-b910-41f8-a857-5d227918d97e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28783741888>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_valid_array = []\n",
    "deapth_list=[]\n",
    "for deapth in range(1,31):\n",
    "  tree = DecisionTreeClassifier(max_depth = deapth)\n",
    "  tree.fit(X_train, y_train)\n",
    "  y_test_tree = tree.predict(X_test)\n",
    "  acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
    "  acc_valid_array.append(round(acc_test_tree,3))\n",
    "  deapth_list.append(deapth)\n",
    "plt.plot(deapth_list,acc_valid_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "Pyo-siL3H1qm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pyo-siL3H1qm",
    "outputId": "4a2aeefd-c83d-42f7-89fa-47c9f5ff35c9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\souhm\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:296: UserWarning: The total space of parameters 40 is smaller than n_iter=100. Running 40 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 40 candidates, totalling 120 fits\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.932 (std: 0.009)\n",
      "Parameters: {'min_samples_leaf': 2, 'max_depth': 7, 'criterion': 'gini'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import uniform, randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "tree = DecisionTreeClassifier()\n",
    "tree_param = {\n",
    "    'criterion': ['entropy', 'gini'],\n",
    "    'max_depth': range(3,8),\n",
    "    'min_samples_leaf': range(1,5)\n",
    "    }\n",
    "\n",
    "\n",
    "search = RandomizedSearchCV(tree, param_distributions=tree_param, random_state=42, n_iter=100, cv=3, verbose=1, n_jobs=1, return_train_score=True)\n",
    "search.fit(X, y)\n",
    "report_best_scores(search.cv_results_, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yzP69wm0KbrH",
   "metadata": {
    "id": "yzP69wm0KbrH"
   },
   "source": [
    "Best hyperparameters according to the randomized search : \n",
    "* min_samples_leaf = 1\n",
    "* max_depth = 7\n",
    "* criterion='gini'\n",
    "* **Validation score = 0.919**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc57e085",
   "metadata": {
    "id": "fc57e085"
   },
   "source": [
    "### **IV.2.2 Random Forest**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wLZTi0bSK_M_",
   "metadata": {
    "id": "wLZTi0bSK_M_"
   },
   "source": [
    "**IV.2.2.1 Model training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20ab1f58",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "20ab1f58",
    "outputId": "499f7dea-a562-43ef-911d-43f6a5297931"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# instantiate the model\n",
    "forest = RandomForestClassifier(max_depth=5)\n",
    "\n",
    "# fit the model \n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b1509a4b",
   "metadata": {
    "id": "b1509a4b"
   },
   "outputs": [],
   "source": [
    "y_test_forest = forest.predict(X_test)\n",
    "y_train_forest = forest.predict(X_train)\n",
    "y_probas_forest = forest.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4f925bae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4f925bae",
    "outputId": "0196e1d3-a3c2-45a5-e2bd-9e9396d8c5bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: Accuracy on training Data: 0.906\n",
      "Random forest: Accuracy on test Data: 0.902\n"
     ]
    }
   ],
   "source": [
    "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": "markdown",
   "id": "BuL82iWVLi0x",
   "metadata": {
    "id": "BuL82iWVLi0x"
   },
   "source": [
    "**IV.2.2.2 Feature importance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7cdbdb0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "id": "b7cdbdb0",
    "outputId": "39c7d842-989f-4416-841c-000b1751d346"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,7))\n",
    "n = X_train.shape[1]\n",
    "plt.barh(range(n), forest.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t-TGN2XELrbi",
   "metadata": {
    "id": "t-TGN2XELrbi"
   },
   "source": [
    "\n",
    "**IV.2.2.3 Parameters tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "kwVQXW8c3aBU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "kwVQXW8c3aBU",
    "outputId": "46992136-e5b5-4b67-deff-3d9ee9359994"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x287836dba48>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_valid_array = []\n",
    "deapth_list=[]\n",
    "for deapth in range(1,31):\n",
    "  forest = RandomForestClassifier(max_depth = deapth)\n",
    "  forest.fit(X_train, y_train)\n",
    "  y_test_forest = forest.predict(X_test)\n",
    "  y_train_forest = forest.predict(X_train)\n",
    "  acc_valid_array.append(round(accuracy_score(y_train,y_train_forest),3))\n",
    "  deapth_list.append(deapth)\n",
    "plt.plot(deapth_list,acc_valid_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "Ps_nZ8IZKSxO",
   "metadata": {
    "id": "Ps_nZ8IZKSxO"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\souhm\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:296: UserWarning: The total space of parameters 60 is smaller than n_iter=100. Running 60 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  UserWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 60 candidates, totalling 180 fits\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.964 (std: 0.003)\n",
      "Parameters: {'n_estimators': 500, 'max_features': 'sqrt', 'max_depth': 8, 'criterion': 'entropy'}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import uniform, randint\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "forest = RandomForestClassifier()\n",
    "\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "\n",
    "\n",
    "search = RandomizedSearchCV(forest, param_distributions=param_grid, random_state=42, n_iter=100, cv=3, verbose=1, n_jobs=1, return_train_score=True)\n",
    "\n",
    "search.fit(X, y) # it takes a lot of time\n",
    "\n",
    "report_best_scores(search.cv_results_, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rZivZs-UL6wa",
   "metadata": {
    "id": "rZivZs-UL6wa"
   },
   "source": [
    "Best hyperparameters according to the randomized search : \n",
    "* n_estimators = 500\n",
    "* max_features = 'sqrt'\n",
    "* max_depth = 8\n",
    "* criterion='entropy'\n",
    "* **validation score=0.921**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6NMjxaXz4qoW",
   "metadata": {
    "id": "6NMjxaXz4qoW"
   },
   "source": [
    " **Comparing Random forest vs Decision trees using ROC** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eJCzLnNodrfs",
   "metadata": {
    "id": "eJCzLnNodrfs"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GmMOd16qh/wKvAm1xqnFeA2ZVtoGqFgE3Ax/itCe8qqqrReR6EbneXe1eYLCIrAI+AW5znz7CK3MtFOXzv/3OoDJN42LCfkhjjDlcec01JKr6kt/0yyJyc6iNVPU94L2AedP8Pm8DzvAYQ83JccqanIZtOC6uMR1aWK8hY0z9VekTgYg0F5HmwAIRmSgiHUXkOBG5FZgfmRDDoCAbBdJ3ldCssT0NGGPqt1BPBCtwevqU9gD6P79lilO1U/fkZ5FXWEyuNKaouMKcesYYUy+EyjXUKVKBRFR+NiUNYijSGC4feFxtR2OMMbWqKiOU9cJJFRFXOk9VXwxHUGFXkEVJdBMohijLMmqMqec8FQQichdOLqAeOI2/o4AvgbpZEORnUxwbD/m1HYgxxtQ+r91Hf4vT3/8XVb0aSAYahi2qcMvPoiSmSW1HYYwxhwWvBUGemwOoyE0BsRMI9ULZ4asgh+KY+NqOwhhjDgte2wiWi0gz4BmcnkTZRCINRLjkZ1HSsEVtR2GMMYcFr7mGbnQ/ThORD4CmqroyfGGFWUEWxQn2RGCMMRB68Pq+lS1T1W9qPqQwU3Uai61qyBhjgNBPBP+pZJkCv6nBWCKjMBe0xBqLjTHGFeqFsmGRCiRi8rMB7InAGGNcnoeqPGLkZwFWEBhjTKn6VxAUOE8EVjVkjDGO+lcQ+J4IrCAwxhjwPkKZiMjlIjLJne4gIv3DG1qYlD4RxCbUciDGGHN48PpE8CQwCBjnTmcBT4QlonArbSyOtsFojDEGvL9ZPEBV+4rItwCqutcdh7juKcgCEWsjMMYYl9cngkIRicJ5dwARaQWUhC2qcMrPgth4kPrXPGKMMcF4vRo+BrwFtBaRf+CkoL4/bFGFU362UxAYY4wBvOcaekVEVuCkohbgXFVdG9bIwqUgGxrGM3vpFgDExqUxxtRzXgemeRSYo6p1s4HYX/4BiI2nKM8Zq7hzS3s6MMbUb16rhr4B7hSRDSLybxFJDWdQYZWfDQ0TEIS+HZoRG21tBcaY+s3TVVBVZ6rqaKA/sB74l4j8ENbIwqXA2giMMcZfVW+HTwC6AR2BdTUeTSS4TwTGGGMcXt8sLn0CmAysBvqp6piwRhYOxYVQdBAa2hOBMcaU8vpC2U/AIFXdFc5gws7NM2RVQ8YY86tQI5R1U9V1OOMTdxCRDv7L69wIZW6eIasaMsaYX4V6IvgzcB3BRyqreyOU5VtBYIwxgUKNUHad+3GUqh70XyYicWGLKlzKVA0V12ooxhhzuPDaa2ixx3lliMhIEfneff9gYgXrDBWRNBFZLSKfe4ynenxVQ9ZGYIwxpUK1ERwNtAMaiciJOOklAJoCleZxdpPUPQGMADKAZSIyT1XX+K3TDCfF9UhV3SIirat7Ip6UPhE0bArsD+uhjDGmrgjVRnAmMB5oDzzkNz8L+FuIbfsDG1R1I4CIzAbGAmv81rkUeFNVtwCo6k7PkVeH9RoyxphyQrURzARmisgFqvpGFffdDtjqN50BDAhYpwsQIyKfAQnAo6r6YuCOROQ6nEZrOnToELjYu4IciIqF6Lo5lIIxxoRDqKqhy1X1ZaCjiPw5cLmqPhRkM9/mQeZpkOP3w8lq2gj4SkSWqOr6gONMB6YDpKamBu7Du/wsax8wxpgAoaqGSofxqs7VMwM41m+6PbAtyDq7VDUHyBGRhUAyTj6jmleQZV1HjTEmQKiqoafd3/dUY9/LgCQR6QT8DFyC0ybgby4wVUSigVicqqOHq3Esb/KzwQatN8aYMrzmGnpARJqKSIyIfCIiu0Tk8sq2UdUi4GbgQ2At8KqqrhaR60XkenedtcAHwEqct5efVdX0QzmhSrmD0hhjjPmV11xDZ6jqrSJyHk51zoXAAuDlyjZS1feA9wLmTQuY/jfwb88RH4r8LEg8NvR6xhhTj3h9oSzG/T0amKWqe8IUT3jlWxuBMcYE8vpE8I6IrAPygBtFpBVwMMQ2h5eSEqf7qFUNGWNMGV5HKJsIDAJSVbUQyMF5OazuKMoDLbHGYmOMCeB18PoY4ArgNBEB+ByYVulGhxtfegkrCIwxxp/XqqGncNoJnnSnr3DnXRuOoMKiNAV1bJPK1zPGmHrGa0Fwkqom+01/KiLfhSOgsCmwJwJjjAnGa6+hYhE5vnRCRDpT1xL626A0xhgTlNcnggnAAhHZiJND6Djg6rBFFQ4lRc7vBl5P2Rhj6oeQV0W3q+h+nLTSrXEKgnWqmh/m2MImO7+I9Tuy6NuhWW2HYowxta7SqiERuRZYDTwOpAEdVfW7ulwIAKzbfgCAxEYxIdY0xpgjX6gngj8CPVU1020XeAWYF/aoIuTs5GNqOwRjjKl1oRqLC1Q1E8Adaaxh+EMyxhgTSaGeCNqLyGMVTavqLeEJyxhjTKSEKggmBEyvCFcgxhhjaoeXMYuNMcYcwUL1GpouIr0qWNZERK4RkcvCE1r4ZOzNq+0QjDHmsBGqauhJYJKI9AbSgUwgDkgCmgLP4/QkqlP+t+YXABLi7OUyY4wJVTWUBlwkIvFAKtAWZ0yCtar6ffjDq3mFJcrWPXkktYmnbWKj2g7HGGNqnadbYlXNBj4LbyiRse4XJ/lcQkN7GjDGGPCedO6IUVxcAsBlA4+r5UiMMebwUO8Kgs17cgFoILUciDHGHCaqVBCISJ0f1WXZT3sASGwUW8uRGGPM4cFTQSAig0VkDbDWnU4WkSdDbHZYiolqQPKxibRKsGwZxhgD3p8IHgbOBHYDqOp3wGnhCircGkZH1XYIxhhz2PBcNaSqWwNm1a0RyowxxgTltQ/lVhEZDKiIxAK34FYTGWOMqdu8PhFcD9wEtAMygBTgxjDFZIwxJoK8PhF0VdUyOYVE5GRgUc2HZIwxJpK8PhE87nGeMcaYOqbSJwIRGQQMBlqJyJ/9FjUFrOuNMcYcAUJVDcUC8e56CX7zDwC/DVdQxhhjIidU9tHPgc9FZIaqbq7qzkVkJPAoztPDs6o6pYL1TgKWABer6utVPY4xxpjq89pYnCsi/wZ64oxHAICq/qaiDUQkCngCGIHT02iZiMxT1TVB1vsX8GEVYzfGGFMDvDYWvwKsAzoB9wCbgGUhtukPbFDVjapaAMwGxgZZ7/fAG8BOj7EYY4ypQV4Lghaq+hxQqKqfq+o1wMAQ27QD/N9GznDn+YhIO+A8YFplOxKR60RkuYgsz8zM9BiyMcYYL7wWBIXu7+0icpaInAi0D7FNsETPGjD9CHCbqlaarkJVp6tqqqqmtmrVylPAxhhjvPHaRnCfiCQCf8F5f6Ap8McQ22QAx/pNtwe2BayTCswWEYCWwGgRKVLVtz3GZYwx5hB5HaryXffjfmAY+N4srswyIElEOgE/A5cAlwbst1PpZxGZAbwbzkJAga17czmuRbiOYIwxdU+oF8qigItw6vY/UNV0ETkb+BvQCDixom1VtUhEbsbpDRQFPK+qq0Xkend5pe0C4ZBb4NRAFZcE1lAZY0z9FeqJ4Dmc6p2lwGMishkYBEz0cueuqu8B7wXMC1oAqOp4D/EeElWnALj4pGNDrGmMMfVHqIIgFeijqiUiEgfsAk5Q1V/CH1r4NIyud0M1G2NMhUJdEQtUtQRAVQ8C6+t6IWCMMaasUE8E3URkpftZgOPdaQFUVfuENTpjjDFhF6og6B6RKIwxxtSaUEnnqpxozhhjTN1irabGGFPPWUFgjDH1nOeCQEQaiUjXcAZjjDEm8jwVBCIyBkgDPnCnU0RkXhjjMsYYEyFenwjuxhlfYB+AqqYBHcMRkDHGmMjyWhAUqer+sEZijDGmVnhNQ50uIpcCUSKSBNwCLA5fWMYYYyLF6xPB73HGK84H/ouTjvqPYYrJGGNMBHl9IuiqqncAd4QzGGOMMZHn9YngIRFZJyL3ikjPsEZkjDEmojwVBKo6DBgKZALTRWSViNwZzsCMMcZEhucXylT1F1V9DLge552CSeEKyhhjTOR4faGsu4jcLSLpwFScHkPtwxqZMcaYiPDaWPwCMAs4Q1W3hTEeY4wxEeapIFDVgeEOxBhjTO2otCAQkVdV9SIRWQWo/yLq4Ahle3MLIQ5EpLZDMcaYw0aoJ4I/uL/PDncgkVCiTll27FGNajkSY4w5fFTaWKyq292PN6rqZv8f4Mbwh1fz+nU4iugoG4bBGGNKeb0ijggyb1RNBmKMMaZ2hGojuAHnzr+ziKz0W5QALApnYMYYYyIjVBvBf4H3gX8CE/3mZ6nqnrBFFQbb9+dRUFRCsWrolY0xph4JVRCoqm4SkZsCF4hI87pUGOzKLiQO6NO+WW2HYowxhxUvTwRnAytwuo/697tUoHOY4gqb41vF13YIxhhzWKm0IFDVs93fnSITjjHGmEjzmmvoZBFp4n6+XEQeEpEO4Q3NGGNMJHjtPvoUkCsiycCtwGbgpVAbichIEfleRDaIyMQgyy8TkZXuz2J3/8YYYyKoKoPXKzAWeFRVH8XpQlohEYkCnsB536AHME5EegSs9hMwxE1VcS8wvSrBG2OMOXReC4IsEbkduAKY717kY0Js0x/YoKobVbUAmI1TkPio6mJV3etOLsFSWxtjTMR5LQguxhm4/hpV/QVoB/w7xDbtgK1+0xnuvIr8DuedhXJE5DoRWS4iyzMzMz2GbIwxxguvQ1X+ArwCJIrI2cBBVX0xxGbBUnwGfZtLRIbhFAS3VXD86aqaqqqprVq18hKyMcYYj7z2GroIWApcCFwEfC0ivw2xWQZwrN90e6DcoDYi0gd4Fhirqru9xGOMMabmeB2h7A7gJFXdCSAirYCPgdcr2WYZkCQinYCfgUuAS/1XcLugvglcoarrqxi7McaYGuC1IGhQWgi4dhM6hXWRiNwMfAhEAc+r6moRud5dPg2YBLQAnnQHiylS1dQqnoMxxphD4LUg+EBEPsQZtxicxuP3Qm2kqu8FrucWAKWfrwWu9RiDMcaYMPA6ZvEEETkfOAWnEXi6qr4V1siMMcZERKjxCJKAB4HjgVXAX1X150gEZowxJjJC9Rp6HngXuAAnA+njYY/IGGNMRIWqGkpQ1Wfcz9+LyDfhDsgYY0xkhSoI4kTkRH59OayR/7SqWsFgjDF1XKiCYDvwkN/0L37TCvwmHEEZY4yJnFAD0wyLVCDmyFNYWEhGRgYHDx6s7VCMqTfi4uJo3749MTGh8oL+yut7BMZUWUZGBgkJCXTs2BH3hUFjTBipKrt37yYjI4NOnbwPLOk1+6gxVXbw4EFatGhhhYAxESIitGjRospP4VYQmLCyQsCYyKrO/zmv2UfFHat4kjvdQUT6V/loxhhjDjtenwieBAYB49zpLJxhKI05rEVFRZGSkkKvXr0YM2YM+/btq5H9zpgxg5tvvrlG9tWxY0d69+5NSkoKKSkpLF68uEb2GygtLY333iubIuz9998nNTWV7t27061bN/76178CcPfdd/Pggw/W2LEHDx7s+zxhwgR69uzJhAkTmDZtGi++GGpok8p9++23XHtt2ZRlY8eOZdCgQWXmjR8/ntdfL5swOT4+3vd5/fr1jB49mhNOOIHu3btz0UUXsWPHjkOKbc+ePYwYMYKkpCRGjBjB3r17g6736KOP0qtXL3r27MkjjzxSZtnjjz9O165d6dmzJ7feeisAq1atYvz48YcUmz+vjcUDVLWviHwLoKp7RSS2xqIwJkwaNWpEWloaAFdddRVPPPEEd9xxR+0GFcSCBQto2bJllbYpKioiOtp7f4+0tDSWL1/O6NGjAUhPT+fmm29m/vz5dOvWjaKiIqZPD8+w4f6F29NPP01mZiYNGzas8n6CnfP999/PnXfe6Zvet28f33zzDfHx8fz000+eGk0PHjzIWWedxUMPPcSYMWMA52+SmZlJmzZtqhxnqSlTpjB8+HAmTpzIlClTmDJlCv/617/KrJOens4zzzzD0qVLiY2NZeTIkZx11lkkJSWxYMEC5s6dy8qVK2nYsCE7dzpJoHv37k1GRgZbtmyhQ4cO1Y6vlNd/RYXuOMUKvvEISg756KbeeGbhRjbuyq7RfXZuGc//O62z5/UHDRrEypUrAVi6dCl//OMfycvLo1GjRrzwwgt07dqVGTNmMG/ePHJzc/nxxx8577zzeOCBBwB44YUX+Oc//0nbtm3p0qWL70K2efNmrrnmGjIzM2nVqhUvvPACHTp0YPz48TRq1Ih169axefNmXnjhBWbOnMlXX33FgAEDmDFjRoWxVrbP5s2b8+2339K3b19uvPFGbrrpJjIzM2ncuDHPPPMM3bp147XXXuOee+4hKiqKxMREPv74YyZNmkReXh5ffvklt99+O/Pnz+eOO+6gW7duAERHR3PjjTeWi+WZZ55h+vTpFBQUcMIJJ/DSSy/RuHHjcsdYuHAhq1ev5uqrr6agoICSkhLeeOMNkpKSiI+PJzs7m3POOYecnBwGDBjA7bffztq1a4mPj+evf/0rP/74Y9BzCTzn//znP77YsrKyWLlyJcnJyb55b7zxBmPGjKFNmzbMnj2b22+/PeS/jf/+978MGjTIVwgADBt26L3n586dy2effQY4NyJDhw4tVxCsXbuWgQMH0rhxYwCGDBnCW2+9xa233spTTz3FxIkTff/WWrdu7dtuzJgxzJ492/eUcCi8Vg09BrwFtBaRfwBfAvcf8tGNiZDi4mI++eQTzjnnHAC6devGwoUL+fbbb5k8eTJ/+9vffOumpaUxZ84cVq1axZw5c9i6dSvbt2/nrrvuYtGiRXz00UesWbPGt/7NN9/MlVdeycqVK7nsssu45ZZbfMv27t3Lp59+ysMPP8yYMWP405/+xOrVq1m1apXvSQWci05KSgoDBgwIuc/169fz8ccf85///IfrrruOxx9/nBUrVvDggw/6LuSTJ0/mww8/5LvvvmPevHnExsYyefJkLr74YtLS0rj44otJT0+nX79+Ib+7888/n2XLlvHdd9/RvXt3nnvuuaDHAJg2bRp/+MMffE8f7du3L7OvefPm+Z7SLr744jLLKjqXwHP2t3z5cnr16lVm3qxZsxg3bhzjxo1j1qxZeOH1u8jKyvJV4QX++P+bKLVjxw7atm0LQNu2bX139P569erFwoUL2b17N7m5ubz33nts3brVd95ffPEFAwYMYMiQISxbtsy3XWpqKl988YWn8wvFaxrqV0RkBTAcJ73Euaq6tkYiMPVCVe7ca1JeXh4pKSls2rSJfv36MWLECAD279/PVVddxQ8//ICIUFhY6Ntm+PDhJCYmAtCjRw82b97Mrl27GDp0KKVjZl988cWsX+8MqvfVV1/x5ptvAnDFFVeUuUMbM2YMIkLv3r1p06YNvXv3BqBnz55s2rSJlJQUoHzVUGX7vPDCC4mKiiI7O5vFixdz4YUX+pbl5+cDcPLJJzN+/Hguuugizj///EP6DtPT07nzzjvZt28f2dnZnHnmmRUeY9CgQfzjH/8gIyOD888/n6SkJE/HqOxc/M850Pbt2/Efx3zHjh1s2LCBU045BREhOjqa9PR0evXqFbQ3TVV72CQkJJQpwGtC9+7due222xgxYgTx8fEkJyf7qr+KiorYu3cvS5YsYdmyZVx00UVs3LgREaF169Zs21Zu9N9q8dprqAOQC7wDzANy3HnGHNZK7z43b95MQUEBTzzh9HH4+9//zrBhw0hPT+edd94p0+/av+46KiqKoqIiwPtFw3+90n01aNCgzH4bNGjg229V99mkSRMASkpKaNasGWlpab6ftWud+7Np06Zx3333sXXrVlJSUti9u/xw4D179mTFihUhjz1+/HimTp3KqlWruOuuu3zfVbBjXHrppb67/jPPPJNPP/3U0/lVdi7+5xyoUaNGZf52c+bMYe/evXTq1ImOHTuyadMmZs+eDUCLFi3KNNbu2bPHV/h6/S6q+kTQpk0btm/fDjiFln/Vjr/f/e53fPPNNyxcuJDmzZv7CtD27dtz/vnnIyL079+fBg0asGvXLsBp12jUqFHImL3wWjU0Hycd9XzgE2Aj8H6NRGBMBCQmJvLYY4/x4IMPUlhYyP79+2nXrh1ApXX1pQYMGMBnn33G7t27KSws5LXXXvMtGzx4sO9i88orr3DKKacccrxe9tm0aVM6derki0VV+e677wD48ccfGTBgAJMnT6Zly5Zs3bqVhIQEsrKyfNtPmDCB+++/3/dkU1JSwkMPPVTuOFlZWbRt25bCwkJeeeUV3/xgx9i4cSOdO3fmlltu4ZxzzvG1yYRS2blUpnv37mzYsME3PWvWLD744AM2bdrEpk2bWLFihe97HDp0KHPmzKGgoABw/u6l7QCXXnopixcvZv78+b59ffDBB6xatarM8UqfCIL99OjRo1x855xzDjNnzgRg5syZjB07Nuh5lFYZbdmyhTfffJNx45wOmueee66vMF2/fj0FBQW+wmv9+vXlqsWqy1NBoKq9VbWP+zsJ6I/TTmBMnXHiiSeSnJzsa2C7/fbbOfnkkykuLg65bdu2bbn77rsZNGgQp59+On379vUte+yxx3jhhRfo06cPL730Eo8++ughx+p1n6+88grPPfccycnJ9OzZk7lz5wLORb5379706tWL0047jeTkZIYNG8aaNWtISUlhzpw59OnTh0ceeYRx48bRvXt3evXq5bt79XfvvfcyYMAARowY4WtYrugYc+bMoVevXqSkpLBu3TquvPJKz+dc0blUplu3buzfv5+srCw2bdrEli1bGDhwoG95p06daNq0KV9//TVnn302p556Kv369SMlJYVFixb5Gm4bNWrEu+++y+OPP05SUhI9evRgxowZFd7BezVx4kQ++ugjkpKS+Oijj5g4cSIA27Zt8/XeArjgggvo0aMHY8aM4YknnuCoo44C4JprrmHjxo306tWLSy65hJkzZ/qeDhcsWMBZZ511SPGVElWt3oYi36hq39Br1qzU1FRdvnx5lbdbtXAucZ9PpvFlL9Ouc/cwRGYCrV27lu7d7bs24fXwww+TkJBQ7l2CI1l+fj5Dhgzhyy+/DNqFONj/PRFZoaqpwfbnqbFYRP7sN9kA6Atkeo7aGGPC5IYbbihTVVcfbNmyhSlTplTpPZLKeN1Lgt/nIpy2gjdqJAJjjDkEcXFxXHHFFbUdRkQlJSV57pHlRciCwH2RLF5VJ9TYUY0xxhw2Km0sFpFoVS3GqQoyxhhzBAr1RLAUpxBIE5F5wGtATulCVX0zjLEZY4yJAK9tBM2B3ThjFCvO28UKWEFgjDF1XKj3CFq7PYbSgVXu79Xu7/Qwx2bMIStNQ92zZ0+Sk5N56KGHKCmpXr7ESZMm8fHHH1e4vLoplT/88EPf26nx8fF07dqVlJSUKvXBr8iDDz5It27d6NWrF8nJyb74hg4dSnW6YQezfPlyXy6k/Px8Tj/9dN+7Ctdee23QN26r4pFHHinzvRYVFdGyZctyyeQ6duzoe+sW4LPPPuPss8/2TVeUcvtQrFixgt69e3PCCSdwyy23EKw7fkFBAVdffTW9e/cmOTnZl4TO3znnnFPm5bCpU6fywgsvHHJ8nqlqhT/AdmAScFeQn0mVbRuun379+ml1rPz8bV0/ua9m/LimWtubqluzpva/6yZNmvg+79ixQ4cPH66TJk2qxYgqN2TIEF22bFm5+UVFRVXe11NPPaVnnHGG7t+/X1VV9+3bpzNmzKj0OIfqq6++0tNOO63a2weeZ2Fhofbu3VsLCwt98+bPn6+DBw/Wzp07a0lJiW/+cccdp5mZmb7pBQsW6FlnnaWqqqtWrdLOnTvr2rVrfft94oknqh1nqZNOOkkXL16sJSUlOnLkSH3vvffKrTN16lQdP368qjr/Bvv27avFxcW+5W+88YaOGzdOe/bs6ZuXk5OjKSkp1Y4r2P89YLlWcF0N9USwXVUnq+o9QX4mh7F8MkeaxY/DvFtq9mfx41UKoXXr1kyfPp2pU6eiqhQXFzNhwgROOukk+vTpw9NPP+1b94EHHvDdwZW+Deo/sMnEiRPp0aMHffr0CTqYS1paGgMHDqRPnz6cd955vhw3Q4cO5bbbbqN///506dKl0uyRHTt2ZPLkyZxyyim89tpr/O9//2PQoEH07duXCy+8kOxsJ633ihUrGDJkCP369ePMM8/0vR18//338+STT9K0aVPASbNx1VVXlTvODTfcQGpqKj179uSuu+7yzQ92jq+99prv6eK0004Dfr3z3rlzJ5dffjlpaWmkpKTw448/lnnyqCj+wPP09+mnn9K3b98y/eVnzZrFH/7wBzp06MCSJUsq/P78PfDAA55SblfF9u3bOXDgAIMGDUJEuPLKK3n77bfLrbdmzRqGDx8OOP8GmzVr5vtOsrOzeeihh8qMpwDQuHFjOnbsyNKlSw8pRq9CtRHYgLPmiNK5c2dKSkrYuXMnc+fOJTExkWXLlpGfn8/JJ5/MGWecwbp163j77bf5+uuvady4MXv27Cmzjz179vDWW2+xbt06RCToqGdXXnkljz/+OEOGDGHSpEncc889vpGnioqKWLp0Ke+99x733HNPpdVNcXFxfPnll+zatYvzzz+fjz/+mCZNmvCvf/2Lhx56iNtvv53f//73zJ07l1atWjFnzhzuuOMOHn30UbKysjj++ONDfif/+Mc/aN68OcXFxQwfPpyVK1fSvn37oOdYmnq6Xbt25c67devWPPvsszz44IO8++67ZZbt2rWL++67r1z8kyZNKnOegRYtWlQmPXReXh6ffPIJTz/9NPv27WPWrFnlRiILJj09nb/85S8h11uwYAF/+tOfys1v3LhxuZHjfv755zJpttu3b8/PP/9cbtvk5GTmzp3LJZdcwtatW1mxYgVbt26lf//+/P3vf+cvf/mLbywCf6Vppvv3D/+owKEKguFhj8DUD4N/X9sR+Khbj/u///2PlStX+u7y9+/fzw8//MDHH3/M1Vdf7fvP2bx58zLbN23alLi4OK699lrOOuusMvXQpfvZt28fQ4YMAZwBSfzTK5embO7Xrx+bNm2qNNbSnP1LlixhzZo1nHzyyYBT7zxo0CC+//570tPTfem1i4uLadu2LarqOVvqq6++yvTp0ykqKmL79u2sWbOGHj16BD3H6qa3rij+wPMMtH379jKpEt59912GDRtG48aNueCCC7j33nt5+OGHiYqKqpE008OGDfOcZrr031Go411zzTWsXbuW1NRUjjvuOAYPHkx0dDRpaWls2LCBhx9+OOi/g9atW7Nu3boqxV9dlRYEqrqnsuWhiMhI4FEgCnhWVacELBd3+WicNNfjVfWbQzmmMZXZuHEjUVFRtG7dGlXl8ccf9+XXL/XBBx9UegGJjo5m6dKlfPLJJ8yePZupU6d6TrcMv6am9k9xXZHS9MuqyogRI8oNtLJq1Sp69uzJV199FXTb0mygFfnpp5948MEHWbZsGUcddRTjx4/n4MGDFZ7jtGnT+Prrr5k/fz4pKSlVumgGiz/wPAMFppmeNWsWixYtomPHjgDs3r2bBQsWcPrpp/vSTJdm5wyWZtp/JLNgqvJE0L59ezIyMnzTGRkZHHPMMeW2jY6O5uGHH/ZNDx48mKSkJD7//HNWrFhBx44dKSoqYufOnQwdOtTXmFyTaaZD8ZqGusrcN5KfAEYBPYBxIhKYp3UUkOT+XAc8Fa54jMnMzOT666/n5ptvRkQ488wzeeqpp3yD0qxfv56cnBzOOOMMnn/+eXJzcwHKVQ1lZ2ezf/9+Ro8ezSOPPFLuYpiYmMhRRx3lq/9/6aWXfE8H1TVw4EAWLVrkS7mcm5vL+vXr6dq1K5mZmb6CoLCwkNWrVwNw++23c9NNN3HgwAEADhw4UG5M4gMHDtCkSRMSExPZsWMH77//fqXnGCz19KHEH4p/mukDBw7w5ZdfsmXLFl+a6SeeeMJXuAwdOpSXXnoJcJ6MXn75ZV+aaa8pt0ufCAJ/AgsBcDLSJiQksGTJElSVF198MWia6dzcXHJynNevPvroI6Kjo+nRowc33HAD27ZtY9OmTXz55Zd06dKlTI+imkwzHUrNZCwKrj+wQVU3AojIbGAs4N+XbCzwotuivUREmolIW1UtnwvXmGooHaGssLCQ6OhorrjiCv78ZyeH4rXXXsumTZvo27cvqkqrVq14++23GTlyJGlpaaSmphIbG8vo0aO5//5fR2bNyspi7NixHDx4EFUtc7dXaubMmVx//fXk5ubSuXPnQ+4K2KpVK2bMmMG4ceN8I3fdd999dOnShddff51bbrmF/fv3U1RUxB//+Ed69uzJDTfcQHZ2NieddBIxMTHExMSUqydPTk7mxBNPpGfPnnTu3NlXdVPROU6YMIEffvgBVWX48OEkJyfz+eefH1L8lRk1apQvj9Cbb77Jb37zmzID/IwdO5Zbb72V/Px8/v73v3PDDTeQnJyMqjJy5Eguv/xygDIpt3NzcxGRGknh/NRTTzF+/Hjy8vIYNWoUo0aNApwhOZcvX87kyZPZuXMnZ555Jg0aNKBdu3a+wiqURYsWlWm8D6uKuhMd6g/wW5zqoNLpK4CpAeu8C5ziN/0JkBpkX9cBy4HlHTp0qHJXKlXVH1ct0cXTbtTM7Vuqtb2pusOh+6ip+84991xdv359bYcRUd98841efvnl1d6+qt1Hw/lEEKySNbB1xcs6qOp0YDo44xFUJ5jOvQbQudeA6mxqjKlFU6ZMYfv27TWabfNwt2vXLu69996IHS+cBUEGcKzfdHsgcKRlL+sYY+qxrl270rVr19oOI6JKe4FFStgai4FlQJKIdBKRWOASnIHv/c0DrhTHQGC/WvvAEUWrOQKeMaZ6qvN/LmxPBKpaJCI3Ax/idB99XlVXi8j17vJpwHs4XUc34HQfvTpc8ZjIi4uLY/fu3bRo0aLK/bmNMVWnquzevZu4uLgqbVftMYtrS3XHLDaRV1hYSEZGRpl+4MaY8IqLi6N9+/bExMSUmX/IYxYbUx0xMTF06tSptsMwxoQQzjYCY4wxdYAVBMYYU89ZQWCMMfVcnWssFpFMYHM1N28J7Aq51pHFzrl+sHOuHw7lnI9T1VbBFtS5guBQiMjyilrNj1R2zvWDnXP9EK5ztqohY4yp56wgMMaYeq6+FQTTQ69yxLFzrh/snOuHsJxzvWojMMYYU159eyIwxhgTwAoCY4yp547IgkBERorI9yKyQUQmBlkuIvKYu3yliPStjThrkodzvsw915UislhEKh/Fuw4Idc5+650kIsUi8ttIxhcOXs5ZRIaKSJqIrBaR0ONIHuY8/NtOFJF3ROQ795zrdBZjEXleRHaKSHoFy2v++lXR0GV19Qcn5fWPQGcgFvgO6BGwzmjgfZwR0gYCX9d23BE458HAUe7nUfXhnP3W+xQn5flvazvuCPydm+GMC97BnW5d23FH4Jz/BvzL/dwK2APE1nbsh3DOpwF9gfQKltf49etIfCLoD2xQ1Y2qWgDMBsYGrDMWeFEdS4BmItI20oHWoJDnrKqLVXWvO7kEZzS4uszL3xng98AbwM5IBhcmXs75UuBNVd0CoKp1/by9nLMCCeIMehGPUxAURTbMmqOqC3HOoSI1fv06EguCdsBWv+kMd15V16lLqno+v8O5o6jLQp6ziLQDzgOmRTCucPLyd+4CHCUin4nIChG5MmLRhYeXc54KdMcZ5nYV8AdVLYlMeLWixq9fR+J4BMGGwgrsI+tlnbrE8/mIyDCcguCUsEYUfl7O+RHgNlUtPkJGSPNyztFAP2A40Aj4SkSWqOr6cAcXJl7O+UwgDfgNcDzwkYh8oaoHwhxbbanx69eRWBBkAMf6TbfHuVOo6jp1iafzEZE+wLPAKFXdHaHYwsXLOacCs91CoCUwWkSKVPXtiERY87z+296lqjlAjogsBJKBuloQeDnnq4Ep6lSgbxCRn4BuwNLIhBhxNX79OhKrhpYBSSLSSURigUuAeQHrzAOudFvfBwL7VXV7pAOtQSHPWUQ6AG8CV9Thu0N/Ic9ZVTupakdV7Qi8DtxYhwsB8PZvey5wqohEi0hjYACwNsJx1iQv57wF5wkIEWkDdAU2RjTKyKrx69cR90SgqkUicjPwIU6Pg+dVdbWIXO8un4bTg2Q0sAHIxbmjqLM8nvMkoAXwpHuHXKR1OHOjx3M+ong5Z1VdKyIfACuBEuBZVQ3aDbEu8Ph3vheYISKrcKpNblPVOpueWkRmAUOBliKSAdwFxED4rl+WYsIYY+q5I7FqyBhjTBVYQWCMMfWcFQTGGFPPWUFgjDH1nBUExhhTz1lBcARyM22m+f10rGTd7Bo43gwR+ck91jciMqga+3hWRHq4n/8WsGzxocbo7qf0e0l3s1U2C7F+ioiMrsZx2orIu+7noSKyX0S+FZG1InJXNfZ3TmnWTRE5t/R7cqcni8jpVd1nkGPMkBDZWd20FZ67HLvn/q6H9SrNtumuEzTjpojEishCETniusJHkhUER6Y8VU3x+9kUgWNOUNUUYCLwdFU3VtVrVXWNO/m3gGWDDz084NfvpRdOUq+bQqyfgtNfu6r+DDzjN/2Fqp6I86bz5SLSryo7U9V5qjrFnTwX6OG3bJKqflyNGA8nM4CRIdYZBSS5P9cBTwG4ieg+AS4OY3xHPCsI6gERiReRT9y79VUiUi5Lp3sXu9DvjvlUd/4ZIvKVu+1rIhIf4nALgRPcbf/s7itdRP7ozmsiIvPFyR2fLiIXu/M/E5FUEZkCNHLjeMVdlu3+nuN/h+7exV4gIlEi8m8RWebeLf6fh6/lK9xEXSLSX5wxGr51f3d132KdDFzsxnKxG/vz7nG+DfY9ui4APgic6aZ9WAEc7z5tLHHjfUtEjnJjuUVE1rjzZ7vzxovIVBEZDJwD/NuN6fjSO3kRGSUir/p9N0NF5B33c5X+hiIyyT3HdBGZLlImUdPl7neULiL93fW9fi9Beci2CZVn3HwbuKwqxzQBwpFP235q9wcoxknClQa8hfMGeVN3WUucNxJLXybMdn//BbjD/RwFJLjrLgSauPNvAyYFOd4M3Fz/wIXA1ziJz1YBTXBSA68GTsS5SD7jt22i+/szINU/Jr91SmM8D5jpfo7FycDYCOcO8U53fkNgOdApSJzZfuf3GjDSnW4KRLufTwfecD+PB6b6bX8/cLn7uRlO/p4mAcfoBKzwmx4KvOt+bgFsAnrivPk7xJ0/GXjE/bwNaFh6jMA4/L9r/2n3b7zF72/1FHB5Nf+Gzf3mvwSM8fsbPeN+Pg03X35F30vAuafivOVc0b/ZjlSQf99d/i5wit/0J37/XqKAzNr+f1eXf6xe7ciUp041DQAiEgPcLyKn4aQdaAe0AX7x22YZ8Ly77tuqmiYiQ3CqIRa5N4WxOHfSwfxbRO4EMnGymw4H3lLnLhgReRM4FedO+UER+RfOReKLKpzX+8BjItIQpyphoarmicgZQB+/Ou5EnCqEnwK2byQiaTgXnRXAR37rzxSRJJwsjjEVHP8M4BwR+as7HQd0oGwun7bud+DvVBH5Fue7n4KTNKyZqpaOHjYTp2ACp4B4RUTexrnT9USdVAwfAGNE5HXgLOBWoCp/w1LDRORWoDHQHKcQf8ddNss93kIRaSpOO0tF34t/fMuBa72eTxAVZtxUJ7tsgYgkqGrWIRyj3rKCoH64DGfkpn6qWigim3D+s/q4/7FPw7mAvCQi/wb2Ah+p6jgPx5igqq+XTkgFDZiqut6tIx8N/FNE/qeqk72chKoeFJHPcNIOX4x7UcK5SPxeVT8MsYs8VU0RkUScO8ybgMdwctUsUNXzxGlY/6yC7QW4QFW/r+wYBHy3OG0EZ/t24hy/Imfh3G2fA/xdRHpWsm6gOTjntAdYpqpZbrWO178hIhIHPIlzt71VRO6m7PkE5qRRKvhexEkAV1NCZdxsCByswePVK9ZGUD8kAjvdQmAYcFzgCiJynLvOM8BzOEPlLQFOFpHSOv/GItLF4zEXAue62zTBqdb5QkSOAXJV9WXgQfc4gQrdJ5NgZuMk2ToVJxEZ7u8bSrcRkS7uMYNS1f3ALcBf3W0SgZ/dxeP9Vs3CqSIr9SHw+9I6cxE5Mcju1+M8cVTIPf5ecdthgCuAz0WkAXCsqi7AuZtvhlOt5i8wJn+f4Xyf/w+nUICq/w1LL/q73LaEwJ5EpW06p+BkvdyPt+/lUFWYcVNEWuBUDRWG4bj1ghUE9cMrQKqILMd5OlgXZJ2hQJpbhXEB8KiqZuJcGGeJyEqci0o3LwdU1W9w6p2X4rQZPKuq3wK9gaVuFc0dwH1BNp8OrBS3sTjA/3DumD9Wp8cIOGMsrAG+EacL4tOEeNp1Y/kOJ63xAzhPJ4tw6ptLLQB6uA2zF+M8OcS4saW704H7zQF+LL3wVuIqnOq0lTi9kya7x35ZnCya3wIPq+q+gO1mAxPcRtnjA45djPOkM8r9TVX/hu7xnsFp33kbp8rQ315xuvNOw6kCBA/fizgdAZ4Ndkxxsm1+BXQVkQwR+Z07/3pxs4ziZNzciNO+9Qxwo98uhrnLTTVZ9lFjapiInIdTDXdnbcdSH7jtT7eHqLIzlbA2AmNqmKq+5VZXmDATp5vv21YIHBp7IjDGmHrO2giMMaaes4LAGGPqOSsIjDGmnrOCwBhj6jkrCIwxpp77/6I8r82QHmo7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth = 5)\n",
    "tree.fit(X_train, y_train)\n",
    "forest = RandomForestClassifier(max_depth=5)\n",
    "forest.fit(X_train, y_train)\n",
    "ax = plt.gca()\n",
    "rfc_disp = RocCurveDisplay.from_estimator(forest, X_test, y_test, ax=ax, alpha=0.8)\n",
    "tree_disp = RocCurveDisplay.from_estimator(tree, X_test, y_test, ax=ax, alpha=0.8)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "r0vvnmOJ8uS0",
   "metadata": {
    "id": "r0vvnmOJ8uS0"
   },
   "source": [
    "###**IV.2.3  KNN**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "O2fbdkyNOlnV",
   "metadata": {
    "id": "O2fbdkyNOlnV"
   },
   "source": [
    "**IV.2.3.1 Model training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "-1YahmCc8clA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-1YahmCc8clA",
    "outputId": "0752ee29-a909-4a66-9578-2149ac4dc851"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.91      0.98      0.94       600\n",
      "         1.0       0.98      0.91      0.94       600\n",
      "\n",
      "    accuracy                           0.94      1200\n",
      "   macro avg       0.95      0.94      0.94      1200\n",
      "weighted avg       0.95      0.94      0.94      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Create the KNN Classifier\n",
    "knn_model = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "#Train the model using the training sets\n",
    "knn_model.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = knn_model.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lE8w0YzUPZAP",
   "metadata": {
    "id": "lE8w0YzUPZAP"
   },
   "source": [
    "**IV.2.3.2 Parameters tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fqlG5hsX879L",
   "metadata": {
    "id": "fqlG5hsX879L"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1=[]\n",
    "for n in range(2,60):\n",
    "    knn_model = KNeighborsClassifier(n_neighbors=n)\n",
    "    knn_model.fit(X_train, y_train)\n",
    "    y_pred = knn_model.predict(X_test)\n",
    "    f1.append(f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "JAarSdEN9AKe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 540
    },
    "id": "JAarSdEN9AKe",
    "outputId": "82493ea9-aac5-4b06-e1c8-926443898f29"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x612 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure()\n",
    "plt.plot(range(2,60),f1)\n",
    "plt.title(\"Plotting the accuracy againts K-value\")\n",
    "plt.xlabel(\"K-value\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "f.set_size_inches(15.5, 8.5, forward=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nzmrQVvpP0oQ",
   "metadata": {
    "id": "nzmrQVvpP0oQ"
   },
   "source": [
    "We choose K=3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fjIgGV6C9MtA",
   "metadata": {
    "id": "fjIgGV6C9MtA"
   },
   "source": [
    "### **IV.2.4 Naive Bayes**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vIX_mkSVXXxm",
   "metadata": {
    "id": "vIX_mkSVXXxm"
   },
   "source": [
    "####  IV.2.4.1 Naive Bayes using Gaussian Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45mMxsuWrlPj",
   "metadata": {
    "id": "45mMxsuWrlPj"
   },
   "source": [
    "IV.2.4.1.1 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2RJIYfwf9G5F",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2RJIYfwf9G5F",
    "outputId": "e38b8240-8a1f-4008-ff32-6faaa972fd2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.75      0.94      0.83       600\n",
      "         1.0       0.91      0.69      0.79       600\n",
      "\n",
      "    accuracy                           0.81      1200\n",
      "   macro avg       0.83      0.81      0.81      1200\n",
      "weighted avg       0.83      0.81      0.81      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Import Gaussian Naive Bayes model\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "gnb = GaussianNB()\n",
    "\n",
    "#Train the model using the training sets\n",
    "gnb.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = gnb.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03gIAobxr1Rl",
   "metadata": {
    "id": "03gIAobxr1Rl"
   },
   "source": [
    "IV.2.4.1.2 Parameters tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "jjamBlx2mMQ5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jjamBlx2mMQ5",
    "outputId": "b5952664-57f2-4e65-84c6-82bca85dda6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.809 (std: 0.004)\n",
      "Parameters: {'var_smoothing': 1e-08, 'priors': None}\n",
      "\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.809 (std: 0.004)\n",
      "Parameters: {'var_smoothing': 1e-08, 'priors': None}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\souhm\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:296: UserWarning: The total space of parameters 3 is smaller than n_iter=100. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  UserWarning,\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import uniform, randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "gnb = GaussianNB()\n",
    "param_cv = {\n",
    "    'priors': [None],\n",
    "    'var_smoothing': [0.00000001, 0.000000001, 0.00000001]\n",
    "}\n",
    "\n",
    "\n",
    "search = RandomizedSearchCV(gnb, param_distributions=param_cv, random_state=42, n_iter=100, cv=3, verbose=1, n_jobs=1, return_train_score=True)\n",
    "search.fit(X, y)\n",
    "report_best_scores(search.cv_results_, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fCbAhJP-sYY3",
   "metadata": {
    "id": "fCbAhJP-sYY3"
   },
   "source": [
    "var_smoothing = 1e-08"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "V2F1IGqFXhBh",
   "metadata": {
    "id": "V2F1IGqFXhBh"
   },
   "source": [
    "#### IV.2.4.2 Naive Bayes using Multinomially distributed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5wD8tmtw9UtI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5wD8tmtw9UtI",
    "outputId": "33863443-e16d-4bf5-c390-79e37e369f91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.74      0.61      0.67       600\n",
      "         1.0       0.67      0.78      0.72       600\n",
      "\n",
      "    accuracy                           0.70      1200\n",
      "   macro avg       0.70      0.70      0.70      1200\n",
      "weighted avg       0.70      0.70      0.70      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Import Multinomial Naive Bayes model\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "#Create a Multinomial Classifier\n",
    "mnb = MultinomialNB()\n",
    "\n",
    "#Train the model using the training sets\n",
    "mnb.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = mnb.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HfqjYBAh9tyW",
   "metadata": {
    "id": "HfqjYBAh9tyW"
   },
   "source": [
    "###  **IV.2.5 Support Vector Claasification SVC**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "s_jcksVEfids",
   "metadata": {
    "id": "s_jcksVEfids"
   },
   "source": [
    "### SVC using linear kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "n1Q1OGLtr-06",
   "metadata": {
    "id": "n1Q1OGLtr-06"
   },
   "source": [
    "IV.2.5.1 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "J4zrcDXT9jSC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J4zrcDXT9jSC",
    "outputId": "2e6ffa7f-3378-4af4-ce96-59dc61789fc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.89      0.89      0.89       600\n",
      "         1.0       0.89      0.89      0.89       600\n",
      "\n",
      "    accuracy                           0.89      1200\n",
      "   macro avg       0.89      0.89      0.89      1200\n",
      "weighted avg       0.89      0.89      0.89      1200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Fit du  Training set\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "#Create a linear SVC Classifier\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "\n",
    "#Train the model using the training sets\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "#Prediction sur le Test set\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0v5g47L2sCX4",
   "metadata": {
    "id": "0v5g47L2sCX4"
   },
   "source": [
    "IV.2.5.2 Parameters tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nHlcy3P1nnph",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nHlcy3P1nnph",
    "outputId": "541b476b-6061-4a73-a973-9370a10b8ab6"
   },
   "outputs": [],
   "source": [
    "from scipy.stats import uniform, randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "classifier = SVC()\n",
    "param_cv = {'C': [0.1,1, 10], 'gamma': [0.1,0.01,0.001],'kernel': ['linear']}\n",
    "\n",
    "\n",
    "search = RandomizedSearchCV(classifier, param_distributions=param_cv, random_state=42, n_iter=100, cv=3, verbose=1, n_jobs=1, return_train_score=True)\n",
    "search.fit(X, y)\n",
    "report_best_scores(search.cv_results_, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "L49JIijD8rbZ",
   "metadata": {
    "id": "L49JIijD8rbZ"
   },
   "source": [
    "### **IV.2.6 MLP**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AjYUIU53cBpc",
   "metadata": {
    "id": "AjYUIU53cBpc"
   },
   "source": [
    "**IV.2.6.1 Model training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "FF9o3CbQ16o3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FF9o3CbQ16o3",
    "outputId": "b7c66387-c017-4a2f-f801-6ce0e90d0a7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=12, max_iter=1700, random_state=3,\n",
       "              solver='lbfgs')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "model_mlp = MLPClassifier(solver='lbfgs', random_state= 3,max_iter= 1700, hidden_layer_sizes=(12),activation='relu',alpha=0.0001)\n",
    "# fit the model \n",
    "model_mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aL2o5Bkn2Fom",
   "metadata": {
    "id": "aL2o5Bkn2Fom"
   },
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_mlp = model_mlp.predict(X_test)\n",
    "y_train_mlp = model_mlp.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4zCOCEUD2MNt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4zCOCEUD2MNt",
    "outputId": "5d426196-e2dc-4729-b8ae-5004ec47f9f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilayer Perceptrons: Accuracy on training Data: 0.987\n",
      "Multilayer Perceptrons: Accuracy on test Data: 0.987\n"
     ]
    }
   ],
   "source": [
    "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:\", round(acc_train_mlp,3))\n",
    "print(\"Multilayer Perceptrons: Accuracy on test Data:\",  round(acc_train_mlp,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wnKUrfLjcyGO",
   "metadata": {
    "id": "wnKUrfLjcyGO"
   },
   "source": [
    "**IV.2.6.2 Parameters tuning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "JF4jhEpNMaWn",
   "metadata": {
    "id": "JF4jhEpNMaWn"
   },
   "outputs": [],
   "source": [
    "model_mlp = MLPClassifier()\n",
    "\n",
    "parameters = {\n",
    "    'solver': ['lbfgs'],\n",
    "    'max_iter': [1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 ],\n",
    "    'alpha': 10.0 ** -np.arange(1, 10),\n",
    "    'hidden_layer_sizes':np.arange(10, 15),\n",
    "    'random_state':[0,1,2,3,4,5,6,7,8,9] }\n",
    "\n",
    "search = RandomizedSearchCV(model_mlp, param_distributions=parameters, random_state=42, n_iter=100, cv=3, verbose=1, n_jobs=1, return_train_score=True)\n",
    "\n",
    "search.fit(X, y)\n",
    "\n",
    "report_best_scores(search.cv_results_, 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Zp5dlM82d6N9",
   "metadata": {
    "id": "Zp5dlM82d6N9"
   },
   "source": [
    "solver='lbfgs'\n",
    "\n",
    "random_state = 3\n",
    "\n",
    "max_iter = 1700\n",
    "\n",
    "hidden_layer_sizes = 12\n",
    "\n",
    "alpha = 0.0001\n"
   ]
  }
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