Heart_Disease_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align='center'>HEART DISEASE PREDICTION ENSEMBLE METHODS</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Important Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas: https://pandas.pydata.org/docs/user_guide/index.html\n",
    "\n",
    "Numpy: https://numpy.org/devdocs/user/quickstart.html\n",
    "\n",
    "sklearn: https://scikit-learn.org\n",
    "        \n",
    "Collections: https://www.guru99.com/python-counter-collections-example.html\n",
    "\n",
    "Seaborn: https://seaborn.pydata.org/tutorial/introduction.html#:~:text=Seaborn%20is%20a%20library%20for,explore%20and%20understand%20your%20data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #data structures and functions\n",
    "import numpy as np #support for arrays and mathematical functions\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression #model for binary classification and regression\n",
    "from sklearn.tree import DecisionTreeClassifier #decision trees for classification\n",
    "from sklearn.ensemble import AdaBoostClassifier #boosting model\n",
    "from sklearn.metrics import confusion_matrix #function to compute a confusion matrix\n",
    "from collections import Counter #class for counting\n",
    "\n",
    "import matplotlib.pyplot as plt #plotting library for visualizations\n",
    "import seaborn as sns #data visualization library based on Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and Understand the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: https://www.kaggle.com/datasets/zeeshanmulla/heart-disease-dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Heart_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display basic info about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 302 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       302 non-null    int64  \n",
      " 1   sex       302 non-null    int64  \n",
      " 2   cp        302 non-null    int64  \n",
      " 3   trestbps  302 non-null    int64  \n",
      " 4   chol      302 non-null    int64  \n",
      " 5   fbs       302 non-null    int64  \n",
      " 6   restecg   302 non-null    int64  \n",
      " 7   thalach   302 non-null    int64  \n",
      " 8   exang     302 non-null    int64  \n",
      " 9   oldpeak   302 non-null    float64\n",
      " 10  slope     302 non-null    int64  \n",
      " 11  ca        302 non-null    int64  \n",
      " 12  thal      302 non-null    int64  \n",
      " 13  target    302 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 35.4 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
      "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
      "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
      "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
      "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
      "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
      "\n",
      "   ca  thal  target  \n",
      "0   0     1       1  \n",
      "1   0     2       1  \n",
      "2   0     2       1  \n",
      "3   0     2       1  \n",
      "4   0     2       1  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
      "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>302.00000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>302.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.42053</td>\n",
       "      <td>0.682119</td>\n",
       "      <td>0.963576</td>\n",
       "      <td>131.602649</td>\n",
       "      <td>246.500000</td>\n",
       "      <td>0.149007</td>\n",
       "      <td>0.526490</td>\n",
       "      <td>149.569536</td>\n",
       "      <td>0.327815</td>\n",
       "      <td>1.043046</td>\n",
       "      <td>1.397351</td>\n",
       "      <td>0.718543</td>\n",
       "      <td>2.314570</td>\n",
       "      <td>0.543046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.04797</td>\n",
       "      <td>0.466426</td>\n",
       "      <td>1.032044</td>\n",
       "      <td>17.563394</td>\n",
       "      <td>51.753489</td>\n",
       "      <td>0.356686</td>\n",
       "      <td>0.526027</td>\n",
       "      <td>22.903527</td>\n",
       "      <td>0.470196</td>\n",
       "      <td>1.161452</td>\n",
       "      <td>0.616274</td>\n",
       "      <td>1.006748</td>\n",
       "      <td>0.613026</td>\n",
       "      <td>0.498970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.50000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  302.00000  302.000000  302.000000  302.000000  302.000000  302.000000   \n",
       "mean    54.42053    0.682119    0.963576  131.602649  246.500000    0.149007   \n",
       "std      9.04797    0.466426    1.032044   17.563394   51.753489    0.356686   \n",
       "min     29.00000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.00000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.50000    1.000000    1.000000  130.000000  240.500000    0.000000   \n",
       "75%     61.00000    1.000000    2.000000  140.000000  274.750000    0.000000   \n",
       "max     77.00000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  302.000000  302.000000  302.000000  302.000000  302.000000  302.000000   \n",
       "mean     0.526490  149.569536    0.327815    1.043046    1.397351    0.718543   \n",
       "std      0.526027   22.903527    0.470196    1.161452    0.616274    1.006748   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.250000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  152.500000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  302.000000  302.000000  \n",
       "mean     2.314570    0.543046  \n",
       "std      0.613026    0.498970  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " classes are balanced or imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    164\n",
      "0    138\n",
      "Name: target, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "class_distribution = df['target'].value_counts()\n",
    "print(class_distribution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histograms and Distributions: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hk2PNg3OAjUuWnQZcUlVHAJe0xyQ5EjgReEx7ztuS7DO6UCVJ0jitODlOsjbJJ5Jck+TqJK9oy3u2yEnjUFWfBG5dsvgE4Nx2/1zgOV3Lz6+qu6vqq8B1wDGjiFOSJI3fakar2AlsqqrPJjkQuDzJxcApdFrkNic5jU6L3GtWH6o0UGuqahtAVW1L8tC2/DDgU13bbW3LdpHkVOBUgDVr1rCwsLDLNjt27Fh2+TyY9bJvOmrnsstnvdySNOtWnBy3xGIxubgzyTV0kogTgA1ts3OBBUyONT2yzLJabsOqOhM4E2D9+vW1YcOGXbZZWFhgueXzYNbLfkqPIQ/P2bj/TJdbkmbdQMY5TrIOeDzwaXq3yEmT5OYkh7Y6eiiwvS3fCqzt2u5w4KaVvsmVN97eM4ly3GhJ0jB1z1uw6aid//F75O/P7q06OU5yAPBB4JVVdUeyXMPbss/b4ynpceg+JdrrtOkgY+31HoOyZr+9j3cU5Z4AFwEnA5vb3wu7lr8vyZuAhwFHAJeOJUJJkjRyq0qOk+xLJzE+r6o+1Bb3apG7l35OSY9D96ngni1+J20Y2Pv1eo9B2XTUTp6/l5/tKMo9SkneT6erzyFJtgKvp5MUX5DkJcD1wPMAqurqJBcAX6DTr/5lVXXPWAKXJEkjt+LkOJ0m4rOAa6rqTV2rerXIzYxe0yv3Mu7TF3sb76ypqhf0WHVcj+3PAM4YXkSSJGlSrabl+FjgRcCVSa5oy15LjxY5SZKkbknWAu8Bvg/4DnBmVf1RkoOBDwDrgC3A86vqtnHFuVIraZya9watSbCa0Sr+nuWv7IceLXKSJEldHBZWE8cZ8iRJ0lhU1baq+my7fyfQPSzschM1SUM3kKHcJEnjkWQLcCdwD7CzqtbPyilpzZeVDAu72pGv9nbSnmGPMLUSu4u/O941+3338YyNPrWL1U7GZHIsSdPvqVX1ja7Hp+EpaU2RlQ4Lu9qRr/Z2sqJhjzC1ErsbSeqUJeMcv/HK++7xObNgtZNQ2a1CkmaPp6Q1NXY3LGxb33NYWGkYbDmWpOlWwN8kKeCdrSVtYKeku0/FLjXOU7NX3nj7vR6v2Q/efN6FHHXYg8YUUW+9Tm3DZJ7eXu0p6b0xz8PCanKZHEvSdDu2qm5qCfDFSb7Y7xP7OSX95vMu/I9TsUuN89Ts0tPbi6eMJ/F0ca9T2zCZp7dXe0p6LzksrCaOybEkTbGquqn93Z7kw8Ax9DlTqTRuDgurSWSfY0maUkn2b2PDkmR/4GeAq/juKWnwlLQk7RVbjiVpeq0BPtyu7L8v8L6q+qskn8FT0pK0IibHkjSlquorwOOWWX4LnpKWpBWxW4UkSZLUmBxLkiRJjd0qRmDdBM6oI0mSpF3ZcixJkiQ1JseSJElSY7cKSZKkKWXXzcGz5ViSJElqbDmWJElSz1boLZuPH3Ek42XLsSRJktTYcixJktTFfrzzzZZjSZIkqTE5liRJkpq571ax9NTJpqN2coqnU5a1ktNM89aJX5KmRa9j+jkb9x9xJNJkmfvkWJIkzacrb7zdBjHtYuaSY4chkSRJ0krZ51iSJElqTI4lSZKkxuRYkiRJamauz3EvDugtSZI0WLN4rZctx5IkSVJjcixJkiQ1JseSJElSMzd9jiVJkjSa67D29j0mqY+yybHGYhY78EuSJs/ukrRNR40wkCk2b4MaDK1bRZKNSa5Ncl2S04b1PtIwWH817azDmnbWYY3LUFqOk+wDvBX4aWAr8JkkF1XVF/b2tWxhnG7T+N/mIOuvNA7WYU0767C67W0ucc7G/Vf1fsNqOT4GuK6qvlJV3wbOB04Y0ntJg2b91bSzDmvaWYc1Nqmqwb9o8lxgY1X9Ynv8IuBJVfXyrm1OBU5tDx8FXDvwQFbmEOAb4w5igGalPI+oqoeM4o36qb9teT91eFY+/5WY17L3Krd1eLiMc3BmrQ7vzjTsj0GxrH3W4WFdkJdllt0rC6+qM4Ezh/T+K5bksqpaP+44BmXWyjMie6y/0F8dnufPf17LPiHlnrs6bJyDMyExDqwO7/ZNJqOsI2FZ+zesbhVbgbVdjw8HbhrSe0mDZv3VtLMOa9pZhzU2w0qOPwMckeSRSe4HnAhcNKT3kgbN+qtpZx3WtLMOa2yG0q2iqnYmeTnw18A+wNlVdfUw3msIJq6rxyrNWnmGbsD1d54//3kt+9jLPad12DgHZ+wxjjCPGHtZR8iy9mkoF+RJkiRJ02hok4BIkiRJ08bkWJIkSWrmPjlOsk+Sf0rykfb44CQXJ/lS+/vgccfYryQHJfmzJF9Mck2Sp0xzeabJnqY5Tccft/WfT/KEccQ5aH2Ue0OS25Nc0W6vG0ecg5bk7CTbk1zVY/1M7O9pmL53T/tiEiRZm+QT7bh8dZJXjDum5SR5QJJLk3yuxfmb445ptZarH7v7XUxyeqvv1yZ52niiXpkeZX1Dkhu7jsHP6Fo3lWXt9X0a6H6tqrm+Af8DeB/wkfb4fwGntfunAb837hj3oiznAr/Y7t8POGiayzMtNzoXi3wZ+P72uX8OOHLJNs8A/pLO2J1PBj497rhHVO4Ni9+tWboBPw48Abiqx/qp39/97N9JuO1pX0zCDTgUeEK7fyDwzxP6WQY4oN3fF/g08ORxxzXo+tHrdxE4stXz+wOPbPV/n3GXYZVlfQPwa8tsO7Vl7fV9GuR+neuW4ySHA8cD7+5afAKdJJP29zkjDmtFkjyQzhfjLICq+nZVfZMpLc+U6Wea0xOA91THp4CDkhw66kAHbG6nd62qTwK37maTWdjfU7F/+9gXY1dV26rqs+3+ncA1wGHjjWpXrb7uaA/3bbepvmq/R/3o9bt4AnB+Vd1dVV8FrqPzPZgKe/ldmNqy7ub7NLD9OtfJMfCHwKuB73QtW1NV26CzA4CHjiGulfh+4F+AP2ndRN6dZH+mtzzT5DDghq7HW9n1h6+fbaZNv2V6SjtN+5dJHjOa0MZuFvb3LJRh4iRZBzyeTqvsxGldDa8AtgMXV9VExrlKvX4XZ7XOv7x17zq7q6vBTJR1yfdpYPt1bpPjJM8EtlfV5eOOZUDuS+d0ytur6vHAXXROK2j4+pnmtK+pUKdMP2X6LJ257B8HvBn482EHNSFmYX/PQhkmSpIDgA8Cr6yqO8Ydz3Kq6p6qOprOjHTHJHnsmEMapVms828HfgA4GtgGvLEtn/qy7sX3aa/LOrfJMXAs8OwkW+icLvzJJO8Fbl48/dn+bh9fiHtlK7C167/8P6OTLE9reaZJP9OczuJUqHssU1XdsXiatqo+Buyb5JDRhTg2s7C/Z6EMEyPJvnR+yM+rqg+NO549ad3yFoCN441kKHr9Ls5cna+qm9s/PN8B3sV3uxNMdVl7fJ8Gtl/nNjmuqtOr6vCqWkdnWsqPV9UL6UxPeXLb7GTgwjGFuFeq6uvADUke1RYdB3yBKS3PlOlnmtOLgBe3UQyeDNy+ePpniu2x3Em+L0na/WPoHHNuGXmkozcL+9vpewekfQfOAq6pqjeNO55ekjwkyUHt/n7ATwFfHGtQw9Hrd/Ei4MQk90/ySOAI4NIxxDcwS651+FlgcSSLqS3rbr5PA9uvQ5k+esptBi5I8hLgeuB5Y45nb/w34Lz2Q/YV4OfpJCPTWp6pUD2mOU3y0rb+HcDH6IxgcB3wr3T2zVTrs9zPBX4lyU7gW8CJ1S4fnmZJ3k9nJI5DkmwFXk/n4qWZ2d+99u+Yw9rFcvuiqs4ab1S7OBZ4EXBl688L8Np2NmWSHAqcm2Qf2m9HVX1kzDGtSo/v6rK/8+34dQGdhqWdwMuq6p6xBL4CPcq6IcnRdLoRbAF+Gaa+rMt+nxjgfnX6aEmSJKmZ224VkiRJ0lImx5IkSVJjcixJkiQ1JseSJElSY3IsSZIkNSbHkiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJElSY3IsSZIkNSbHkiRJUmNyPGZJTkny9yt87huSvHfQMUn9SPKoJP+U5M4ktyb5nXHHJEmzIMm6JJXkvit8fiX5wVXGcM68HtdNjiWt1KuBhao6ELho3MFIq7GahgppEJJsSfJT445DJseSVu4RwNXjDkLqttKWNklaZHI8QknWJvlQkn9JckuSt3St+/0ktyX5apKndy1/WJKL2mnr65L80niil74ryceBpwJvSbIDuB9wSJKLWzeLv0vyiLZtkvxBku1Jbk/y+SSPHWf8mi2txe01ST4P3JXkx5L8Q5JvJvlckg1d256S5Cutnn41yUlJHg28A3hKkh1Jvtm2vX87Nl+f5OYk70iyX9drnZDkiiR3JPlyko1t+SOTfLK9x98meatd4LQ7Sf4UeDjwF+2Y+vy26qRW/76R5Ne7tj8myT+2Or4tyVuS3K/Hax/fusDdkeSGJG9Ysr77+3JDklO6Vj84yUdbXf50kh8YaMEnlMnxiCTZB/gI8DVgHXAYcH5b/STgWuAQ4H8BZyVJW/d+YCvwMOC5wP9MctzoIpd2VVU/Cfwf4OVVdQDwbeAk4Lfp1OMrgPPa5j8D/DjwQ8BBwM8Bt4w2Ys2BFwDHA98PXAj8DnAw8GvAB5M8JMn+wB8DT2/dgf4TcEVVXQO8FPjHqjqgqg5qr/l7dOrt0cAP0jluvw46yQnwHuBVdOr1jwNb2vPeB1wKfC/wBuBFwymyZkVVvQi4HnhWO6Ze0Fb9GPAo4Djgde0fOYB7gP9O53j7lLb+v/Z4+buAF9Opp8cDv5LkOQBJHg78JfBm4CF06voVXc99AfCbwIOB64AzVlPOaWFyPDrH0ElwX1VVd1XVv1XVYv+2r1XVu6rqHuBc4FBgTZK1dL4Yr2nbXwG8Gw+0mkwfrapPVtXdwK/TaYVbC/w7cCDww0Cq6pqq2jbOQDWT/riqbgBeCHysqj5WVd+pqouBy4BntO2+Azw2yX5Vta2qlu0a1Boofgn471V1a1XdCfxP4MS2yUuAs6vq4vY+N1bVF1uy8aPA66rq2+04b598rdRvVtW3qupzwOeAxwFU1eVV9amq2llVW4B3Aj+x3AtU1UJVXdnq6efpNLotbnsS8LdV9f6q+vequqXlGos+VFWXVtVOOg0eRw+jkJPG5Hh01tJJgncus+7ri3eq6l/b3QPoJNOLB+VFX6PTeiFNmhsW71TVDuBW4GFV9XHgLcBbgZuTnJnkgWOKUbNrsf49AnheO0X8zdZF4seAQ6vqLjpnLl4KbGuni3+4x+s9BPge4PKu1/mrthw6x/QvL/O8xeP2v3Ytu2GZ7aR+fL3r/r/SyQ1I8kNJPpLk60nuoPOP2yHLvUCSJyX5ROvSeTud+r+4ba96vNv3n3Umx6NzA/DwvbxY5Cbg4CQHdi17OHDjQCOTBmPt4p0kB9A5pX0TQFX9cVU9EXgMndPUrxpLhJpl1f7eAPxpVR3Uddu/qjYDVNVfV9VP0zlD90XgXUuev+gbwLeAx3S9zoPaKe/F91mu/+U2Osft7+latnaZ7aSlltbB3Xk7nfp7RFU9EHgtkB7bvo/O2Yu1VfUgOv3rF7ftVY/nmsnx6FxK56C5Ocn+SR6Q5NjdPaGdIvwH4Hfb9j9C51Teebt7njQmz2gXdtyPTt/jT1fVDUl+tLVc7Eun79u/0ekvJw3De4FnJXlakn3asXNDksOTrEny7Nb3+G5gB9+tizcDhy9e1FRV36GTOP9BkocCJDksydPa9mcBP5/kuCT3aet+uKq+RqcbxxuS3C/JU4Bnjaz0mmY30+kz348DgTuAHe3sx6/sYdtbq+rfWl/5/9K17jzgp5I8P8l9k3xvkqNXEPtMMTkekdaf+Fl0Luq4ns5Fdj/Xx1NfQOcCvpuADwOvb33opEnzPuD1dLpTPJFOXzaAB9JJMm6j0y3oFuD3xxGgZl9rVDiBTkvav9BpGXsVnd+7+wCb6BxPb6XT73LxIqaP0xma8OtJvtGWvYbORUifaqeu/5bOxVFU1aXAzwN/ANwO/B2dLh3QqftPoVPXfwf4AJ1kXNqd3wV+o3Xhee4etv01OknunXSOrx/Yzbb/FfitJHfSuaB08WI/qup6Ov3xN9H5TlxB69c8z1K1N634kiRpbyT5APDFqnr9uGORtGe2HEuSNECtK9EPtO4WG+m0ZP/5mMOS1CdnEpIkabC+D/gQnXGOtwK/UlX/NN6QJPXLbhWSJElSY7cKSZIkqZmIbhWHHHJIrVu3bmTvd9ddd7H//vuP7P3GYdbKePnll3+jqh6y5y3Ho1cdnrX9sDfmtey9ym0dnj7zWvZZq8OjNs31Zppjhz3H328dnojkeN26dVx22WUje7+FhQU2bNgwsvcbh1krY5KvjTuG3elVh2dtP+yNeS17r3Jbh6fPvJZ9HHU4yT50xoe+saqemeRgOsOTrQO2AM+vqtt29xqjziV6meZ6M82xw57j77cO261CkiZckrOTbE9yVdeyNyS5MckV7faMrnWnJ7kuybVdk1ZIk+wVwDVdj08DLqmqI4BL2mNpJEyOJWnynQNsXGb5H1TV0e32MYAkRwIn0pmqeyPwttYqJ02kJIcDxwPv7lp8AnBuu38u8JwRh6U5NhHdKsZp3Wkf7bluy+bjRxiJZtGVN97OKT3qmPVL/aqqTyZZ1+fmJwDnV9XdwFeTXAccA/zjsOKbVb2+v353B+4PgVfTmeZ40Zqq2gZQVdsWp/BeKsmpwKkAa9asYWFhYbiR9mHHjh0TEcdK9Ir9yhtv7/mcow570BAj2juD+uznPjmWpCn28iQvptNXc1Prk3kY8Kmubba2ZbvoJ7GY5h/61VqzH2w6aucuy2f98xjlPk/yTGB7VV2eZMPePr+qzgTOBFi/fn1NQn/Zae632yv2Xo08AFtO2nX7cRnUZ29yLEnT6e3AbwPV/r4R+AUgy2y77ID2/SQW0/xDv1pvPu9C3njlrj+Tk5QMDMOI9/mxwLNbn/kHAA9M8l7g5iSHtlbjQ4HtowpIMjmWpClUVTcv3k/yLuAj7eFWYG3XpocDN630fewapGGqqtOB0wFay/GvVdULk/x/wMnA5vb3wnHFqPkzN8lxd9/iTUft3O0pAkmadIutau3hzwKLI1lcBLwvyZuAhwFHAJeOIURpNTYDFyR5CXA98Lwxx6M5MjfJsSRNqyTvBzYAhyTZCrwe2JDkaDpdJrYAvwxQVVcnuQD4ArATeFlV3TOGsKW9UlULwEK7fwtw3Djj0fwyOZakCVdVL1hm8Vm72f4M4IzhRSRJs8vkeDd6DfNmPztJkjRrdneNwTxxEhBJkiSpMTmWJEmSGpNjSZIkqTE5liRJkhqTY0mSJKkxOZYkSZIak2NJkiSpMTmWJEmSGpNjSZIkqTE5liRJkhqTY828JGuTfCLJNUmuTvKKtvzgJBcn+VL7++Cu55ye5Lok1yZ52viilyRJo2RyrHmwE9hUVY8Gngy8LMmRwGnAJVV1BHBJe0xbdyLwGGAj8LYk+4wlckmSNFImx5p5VbWtqj7b7t8JXAMcBpwAnNs2Oxd4Trt/AnB+Vd1dVV8FrgOOGWnQkiRpLO477gCkUUqyDng88GlgTVVtg04CneShbbPDgE91PW1rW7b0tU4FTgVYs2YNCwsLu7zfmv1g01E7l41lue1nyY4dO2a+jMuZ13JLmk/rTvvoXm2/ZfPxQ4pkcFadHLfTzZcBN1bVM5McDHwAWAdsAZ5fVbet9n0mSa+KMA07fJ4lOQD4IPDKqrojSc9Nl1lWuyyoOhM4E2D9+vW1YcOGXZ705vMu5I1XLv8123LSrtvPkoWFBZb7TGbdvJZbkmbFILpVvILOaepFy/bjlMYpyb50EuPzqupDbfHNSQ5t6w8FtrflW4G1XU8/HLhpVLFKkqTxWVVynORw4Hjg3V2Le/XjlMYinSbis4BrqupNXasuAk5u908GLuxafmKS+yd5JHAEcOmo4pUkSeOz2m4Vfwi8Gjiwa1mvfpzSuBwLvAi4MskVbdlrgc3ABUleAlwPPA+gqq5OcgHwBTojXbysqu4ZedSSJGnkVpwcJ3kmsL2qLk+yYQXP3+PFTIPUfVHU7i6SWo1JugjHi4K+q6r+nuX7EQMc1+M5ZwBnDC0oSZI0kVbTcnws8OwkzwAeADwwyXtp/Thbq3F3P8576edipkE6pesiuk1H7ex5kdRqTNIFVl4UJEmStPdWnCFW1enA6QCt5fjXquqFSf4/Ov03N3PvfpySJEkaEUfXWplhTAKyGfjpJF8Cfro9liStUJKzk2xPclXXMqc/l6QhGEhyXFULVfXMdv+Wqjquqo5of28dxHtI0hw7h85U5t2c/lyShsDpoyVpwlXVJ4GlDQ1Ofy5JQ+D00ZI0nVY1/Tk4Bfqe9Cr7rJfb0Y4070yOJWm29DX9OTgF+p70Kvusl9vRjjTv7FYhSdPJ6c8laQhsOZak6bQ4/fnSYTMvAt6X5E3Aw3D6c0lL9BribdNRIw5kQpkcS9KES/J+YANwSJKtwOtx+nNJGgqTY0macFX1gh6rnP5ckgbMPseSJElSY3IsSZIkNSbHkiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJGkskqxN8okk1yS5Oskr2vKDk1yc5Evt74PHHavmh8mxJEkal53Apqp6NPBk4GVJjgROAy6pqiOAS9pjaSRMjiVJ0lhU1baq+my7fydwDXAYcAJwbtvsXOA5YwlQc8kZ8iRJ0tglWQc8Hvg0sKaqtkEngU7y0B7PORU4FWDNmjUsLCyMJtjd2LFjx0jjuPLG23uu23TU3r3Wmv1g01E7VxnR7r35vAuXXX7UYQ9a9WsP6rM3OR6gdad9dNnlWzYfP+JIJEmaHkkOAD4IvLKq7kjS1/Oq6kzgTID169fXhg0bhhZjvxYWFhhlHKf0yD1WYtNRO3njleNJDbectGHVrzGoz95uFZIkaWyS7EsnMT6vqj7UFt+c5NC2/lBg+7ji0/wxOZYkSWORThPxWcA1VfWmrlUXASe3+ycDy5+Ll4bAbhWSJGlcjgVeBFyZ5Iq27LXAZuCCJC8BrgeeN57wNI9MjiVJ0lhU1d8DvToYHzfKWKRFJscj0OtCPfBiPUmSpElin2PNvCRnJ9me5KquZT1nX0pyepLrklyb5GnjiVqSJI2DybHmwTnAxiXLlp19qc3MdCLwmPactyXZZ3ShSpKkcVpxcux86JoWVfVJ4NYli3vNvnQCcH5V3V1VXwWuA44ZRZySJGn8VtPneHE+9M8mORC4PMnFwCl0WuQ2JzmNTovca1YfqjRQvWZfOgz4VNd2W9uyXfQzM9PuZhuahJmchmnUs0RNinkttyTNihUnxy2xWEwu7kzSPR/6hrbZucACJseaHstdNV3LbdjPzExvPu/CnrMNDWI2oEk26lmiJsW8llvScO3u4n4N1kBGq5iG+dC7W+9GMXd4v4ZVbluv9ujmJIe2Oto9+9JWYG3XdocDN408OqlPSbYAdwL3ADuran2Sg4EPAOuALcDzq+q2ccUoSdNk1cnxtMyH3j33+DjnDl9qWK2Htl7t0eLsS5u59+xLFwHvS/Im4GHAEcClY4lQ6t9Tq+obXY8XLzi1e5sk7aVVZYi7mw99mRa5kfC0g5ZK8n46XX0OSbIVeD09Zl+qqquTXAB8gU6/+pdV1T1jCVxaObu3SROuV77i/Afjt+LkuI/50Je2yEljUVUv6LFq2dmXquoM4IzhRSQNVAF/k6SAd7azcgPr3jbPF5X2Kvusl9tueZp3q2k5dj50SRq/Y6vqppYAX5zki/0+0YtKd69X2We93HbL07xbzWgVzocuSWNWVTe1v9uTfJjOuNxj7d4mSdNsMq5KkyTttST7A/dpw2nuD/wM8FvYvU2aWl47NX4mx5I0vdYAH26jBN0XeF9V/VWSz2D3NklaEZNjSZpSVfUV4HHLLL8Fu7dJ0orcZ9wBSJIkSZPC5FiSJElqTI4lSZKkxuRYkiRJakyOJUmSpMbkWJIkSWpMjiVJkqTGcY7HrNdMOFs2Hz/iSCRJ0iA52910suVYkiRJamw5lqQV6NUidM7G/UcciSRpkKY2OfZUhSRJkgZtapNjSZIkzYbdNXqO+jos+xxLkiRJjcmxJEmS1JgcS5IkSY3JsSRJktR4QZ4kSVIfnLhrPEb9udtyLEmSJDW2HEuSJK1Cd8vmpqN2copzMUw1W44lSZKkZuJbjud1Jry97V+zdPvu/1ztCyVJktSfiU+OJUmSRmleG+bUMbRuFUk2Jrk2yXVJThvW+0jDYP3VtLMOa9pZhzUuQ2k5TrIP8Fbgp4GtwGeSXFRVXxjG+0mDZP3VtLMOqx+9WkfP2bj/iCPZ1SDr8O5age12ON16dSld7X4dVreKY4DrquorAEnOB04APDCPwSSOyziJMXWx/mraWYc17azDGptU1eBfNHkusLGqfrE9fhHwpKp6edc2pwKntoePAq4deCC9HQJ8Y4TvNw6zVsZHVNVDRvFG/dTftryfOjxr+2FvzGvZe5XbOjx95rXss1aHR22a6800xw57jr+vOjysluMss+xeWXhVnQmcOaT3360kl1XV+nG896jMQxmHaI/1F/qrw/O8H+a17BNSbuvwAMxr2Sek3AOrw6M2IZ/fikxz7DC4+Id1Qd5WYG3X48OBm4b0XtKgWX817azDmnbWYY3NsJLjzwBHJHlkkvsBJwIXDem9pEGz/mraWYc17azDGpuhdKuoqp1JXg78NbAPcHZVXT2M91qhiToFMyTzUMahGHD9nef9MK9lH3u5rcMDM69lH3u5pyCP2J2xf36rMM2xw4DiH8oFeZIkSdI0GtokIJIkSdK0MTmWJEmSmplLjpOsTfKJJNckuTrJK9ryg5NcnORL7e+Du55zepue8tokTxtf9P1Lsk+Sf0rykfZ4pso3bfY0zWk6/rit/3ySJ4wjzkHro9wbktye5Ip2e9044hy0JGcn2Z7kqh7rZ2J/z+v0vXvav7Oq1++nvmu5ujFNv7894n9Dkhu7jtPP6Fo3MfGPNL+rqpm6AYcCT2j3DwT+GTgS+F/AaW35acDvtftHAp8D7g88EvgysM+4y9FHOf8H8D7gI+3xTJVvmm50Lhb5MvD9wP3a533kkm2eAfwlnbE7nwx8etxxj6jcGxbr6CzdgB8HngBc1WP91O/vfvbvrN72tH9n9dbr93PccU3Sbbm6MU2/vz3ifwPwa8tsO1HxjzK/m7mW46raVlWfbffvBK4BDqMz7eS5bbNzgee0+ycA51fV3VX1VeA6OtNWTqwkhwPHA+/uWjwz5ZtC/zHNaVV9G1ic5rTbCcB7quNTwEFJDh11oAPWT7lnUlV9Erh1N5vMwv52/86Z3fx+qulRN6bm93cv6/ZExT/K/G7mkuNuSdYBjwc+Daypqm3Q+YCBh7bNDgNu6HraVib/YPCHwKuB73Qtm6XyTZt+PuNZ3A/9lukpST6X5C+TPGY0oY3dLOzvWSiDVmjJ76d2bxZ+f1/euoCd3dUtYWLjH3Z+N7PJcZIDgA8Cr6yqO3a36TLLJnZ8uyTPBLZX1eX9PmWZZRNbvinVz2c8i/uhnzJ9ls5c9o8D3gz8+bCDmhCzsL9noQxagb34/dTuTct36O3ADwBHA9uAN7blExn/KPK7mUyOk+xL54M7r6o+1BbfvHhas/3d3pZP2xSVxwLPTrKFzmnOn0zyXmanfNOon894FvfDHstUVXdU1Y52/2PAvkkOGV2IYzML+3sWyqC91OP3U7s31b+/VXVzVd1TVd8B3sV3ux5MXPyjyu9mLjlOEuAs4JqqelPXqouAk9v9k4ELu5afmOT+SR4JHAFcOqp491ZVnV5Vh1fVOjrTaX68ql7IjJRvSvUzzelFwIvbKAZPBm5fPA00xfZY7iTf176TJDmGzjHnlpFHOnqzsL+dvnfO7Ob3U7s31b+/S66H+FlgcSSLiYp/pPnduK46HNYN+DE6zeafB65ot2cA3wtcAnyp/T246zm/TucqxmuBp4+7DHtR1g18d7SKmSvfNN1aHfvn9jn/elv2UuCl7X6At7b1VwLrxx3ziMr9cuBqOlcMfwr4T+OOeUDlfj+d04//Tqd14iWzuL+X27/zcFtu/447phGVe9nfz3HHNUm3Ht/9qfn97RH/n7bj1OfpJJSHTmL8o8zvnD5akiRJamauW4UkSZK0UibHkiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJElSY3IsSZIkNSbHkiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJElSY3IsaVlJzknyO7tZX0l+cMgxrGvvc99hvo/mS5I3JHnvuOOQNJlMjiVJkqTG5FiSJElqTI6HIMnDknwwyb8k+WqSX01ycJKtSZ7VtjkgyXVJXtweH5/kn5LckeSGJG/oer3FU8snJ7k+yTeS/HrX+v2SnJvktiTXJHl1kq0jL7imUpJHJ1lI8s0kVyd5do/tXpVkW5KbkvzCknXnJHlHkouT3Jnk75I8omv9D7d1tya5Nsnzu9b1rPvLxPCfk2xJ8tgBFF1zIMlrktzY6uW1SY5bZptnt7r/zfZdeHTXui1JTk/yhXaM/ZMkD+ha/8wkV7Tn/kOSHxlV2aRFSdYm+VDLO25J8pYkP5Dk4+3xN5Kcl+Sgccc6DUyOByzJfYC/AD4HHAYcB7wS+FHgF4B3JXko8AfAFVX1nvbUu4AXAwcBxwO/kuQ5S17+x4BHtdd8XdcB/PXAOuD7gZ8GXjj4kmkWJdmXTn39G+ChwH8DzkvyqCXbbQR+jU79OgL4qWVe7iTgt4FDgCuA89pz9wcuBt7X3uMFwNuSPKY9r5+6T5KfB34P+KmqumqFRdYcafX45cCPVtWBwNOALUu2+SHg/XSO0w8BPgb8RZL7dW12UnvuDwA/BPxGe+4TgLOBXwa+F3gncFGS+w+tUNISSfYBPgJ8jU4ucBhwPhDgd4GHAY8G1gJvGEuQU8bkePB+FHhIVf1WVX27qr4CvAs4sar+BvjfwCV0koBfXnxSVS1U1ZVV9Z2q+jydg/VPLHnt36yqb1XV5+gk349ry58P/M+quq2qtgJ/PNQSapY8GTgA2Nzq68fpHGRfsGS75wN/UlVXVdVdLH+A/WhVfbKq7gZ+HXhKkrXAM4EtVfUnVbWzqj4LfBB4LvRd918JvArYUFXXDaDcmg/3APcHjkyyb1VtqaovL9nm5+jU3Yur6t+B3wf2A/5T1zZvqaobqupW4Ay++/34JeCdVfXpqrqnqs4F7qbzvZJG5Rg6CfCrququqvq3qvr7qrqu1eu7q+pfgDex67FVyzA5HrxHAA9rp9i+meSbwGuBNW39mcBj6SQatyw+KcmTknyinRK5HXgpnRa4bl/vuv+vdJIa6Hwpbuha131f2p2HATdU1Xe6ln2NTsvDLtst2Wap/1hfVTuAW9vzHgE8acl34iTg+6Dvuv8q4K3tnz+pL+0fqVfS+Wdue5LzkzxsyWYPo6s+t+/CDdz7O7C07i++xiOATUvq9tqu9dIorAW+VlU7uxcmeWir8zcmuQN4L7seW7UMk+PBuwH4alUd1HU7sKqe0U59vBN4D51Tx93DYL0PuAhYW1UPAt5B55RIP7YBh3c9Xrv6YmhO3ASsbd2BFj0cuHHJdtu4d716+DKv9R/rkxwAHNxe/wbg75Z8Jw6oql9pm/dT938G+I0k/3nviqd5V1Xvq6ofo5PIFp2uOd1uausASBI6dbn7O7C07t/U7t8AnLGkbn9PVb1/0OWQduMG4OHZdcjL36VT53+kqh5Ip8tlv3nFXDM5HrxLgTvaRSD7JdknyWOT/CidFmTo9D3+feA9LWEGOBC4tar+LckxwH/Zi/e8ADg9yYOTHEanj53Uj0/T6fP76iT7JtkAPItOf7VuFwCnJDkyyffQ6ee+1DOS/Fjrq/nbwKer6gY63TR+KMmL2nvsm+RHu/rM91P3rwY2Am9NjwsGpaWSPCrJT7Y+wP8GfItOV4tuFwDHJzmu9cHfRKdrxD90bfOyJIcnOZjOcfwDbfm7gJe2sx9Jsn+7wPTAoRZMurdL6TRgbG518AFJjqVzbN0BfLPlBq8aZ5DTxOR4wKrqHjrJxdHAV4FvAO8GfhL4H8CL2za/R+c/utPaU/8r8FtJ7gReR+eA3a/fAra29/tb4M/oHNyl3aqqbwPPBp5Op66+jU4d/eKS7f4S+EPg48B17e9S76OTNN8KPJFO1wmq6k46Lb8n0mlx+zqd+r940VJfdb/1tX8mnYtan76iAmve3B/YTKduf53OBaGv7d6gqq6l06L25rbds4Bnte/GovfRuWj1K+32O+25l9Hpd/wW4DY6341ThlYaaRldeccPAtfTyQd+DvhN4AnA7cBHgQ+NK8Zpk6oadwwasCS/QucCQDveaySSnANsrarfGHcs0iAl2QL8YlX97bhjkTQathzPgCSHJjk2yX3a0EWbgA+POy5JkqRps7TztqbT/ehc6PdI4Jt0+ou+bZwBSZIkTSO7VUiSJEmN3SokSZKkZiK6VRxyyCG1bt26Zdfddddd7L///qMNaAJY7nu7/PLLv1FVDxlDSH3pVYenYT9OeoyzEp91ePrMa9k9Ds+OeS37qutwVY399sQnPrF6+cQnPtFz3Syz3PcGXFYTUFd73XrV4WnYj5Me46zEZx2ePvNado/Ds2Ney77aOmy3CkmSJKnZY3KcZG2STyS5JsnVSV7Rlh+c5OIkX2p/H9z1nNOTXJfk2iRPG2YBJEmSpEHpp+V4J7Cpqh4NPJnONJpH0pnZ7ZKqOgK4pD2mrTsReAyd6V7f1jVFsiRJkjSx9nhBXlVtozNnN1V1Z5JrgMOAE4ANbbNzgQXgNW35+VV1N/DVJNcBxwD/uJIAr7zxdk457aO7LN+y+fiVvJw0Ur3qL1iHpUk3678/63ocm87ZOFsXcHkc1t7aq9EqkqwDHg98GljTEmeqaluSh7bNDgM+1fW0rW3Z0tc6FTgVYM2aNSwsLCz7nmv2g01H7dxlea/tZ8WOHTtmvozLmddyS5KkydB3cpzkAOCDwCur6o4kPTddZtkuM41U1ZnAmQDr16+vDRs2LPtibz7vQt545a5hbjlp+e1nxcLCAr0+k1k2r+WWJEmToa/RKpLsSycxPq+qPtQW35zk0Lb+UGB7W74VWNv19MOBmwYTriRJkjQ8/YxWEeAs4JqqelPXqouAk9v9k4ELu5afmOT+SR4JHAFcOriQJUmSpOHop1vFscCLgCuTXNGWvRbYDFyQ5CXA9cDzAKrq6iQXAF+gM9LFy6rqnkEHLkmSJA1aP6NV/D3L9yMGOK7Hc84AzlhFXJIkSdLIOUOeJEmS1JgcS5IkSY3JsSRJktSYHEuSpKFKcnaS7Umu6lr2hiQ3Jrmi3Z7Rte70JNcluTbJ08YTteaVybEkSRq2c4CNyyz/g6o6ut0+BpDkSOBE4DHtOW9Lss/IItXcMznWzOvRYnFwkouTfKn9fXDXOlssJGmAquqTwK19bn4CcH5V3V1VXwWuA44ZWnDSEn1PHy1NsXOAtwDv6Vp2GnBJVW1Oclp7/JolLRYPA/42yQ85VrckDcXLk7wYuAzYVFW3AYcBn+raZmtbtoskpwKnAqxZs4aFhYVdtlmzH2w6aueyb77c9rNkx44dM1/G5ay23CbHmnlV9ckk65YsPgHY0O6fCywAr6GrxQL4apLFFot/HEmwkjQ/3g78NlDt7xuBX2D5uRVquReoqjOBMwHWr19fGzZs2GWbN593IW+8cvl0Z8tJu24/SxYWFljuM5l1qy23ybHm1Zqq2gZQVduSPLQtn7sWi0lvWTA+aTZV1c2L95O8C/hIe7gVWNu16eHATSMMTXPO5Fi6t7lrsZj0lgXjk2ZTkkMXGymAnwUWrwu5CHhfkjfR6d52BHDpGELUnDI51ry6efHAnORQYHtbbouFJA1YkvfT6cp2SJKtwOuBDUmOptMAsQX4ZYCqujrJBcAXgJ3Ay7zuQ6Nkcqx5dRFwMrC5/b2wa7ktFpI0QFX1gmUWn7Wb7c8AzhheRFJvJseaeT1aLDYDFyR5CXA98DywxUKSpHlncqyZ16PFAuC4HtvbYiFJ0pxyEhBJkiSpMTmWJEmSGpNjSZpwToEuSaNjcixJk+8cYOOSZYtToB8BXNIes2QK9I3A25LsM7pQJWm6mRxL0oSrqk8Cty5ZfAKdqc9pf5/Ttfz8qrq7qr4KLE6BLknqg8mxJE2ne02BDnRPgX5D13Y9p0CXJO3Kodwkabb0PQV6klOBUwHWrFnDwsLCLtvs2LFj2eXzYM1+sOmonbssn5XPY7mywXzvcwlMjiVpWq16CvSqOhM4E2D9+vW1YcOGXbZZWFhgueXz4M3nXcgbr9z1Z3LLSRtGH8wQnHLaR5ddfs7G/ed2n0tgtwpJmlaLU6DDrlOgn5jk/kkeiVOgS9JeseVYkiacU6BL0uiYHEvShHMKdEkaHbtVSJIkSY3JsSRJktSYHEuSJEmNybEkSZLUmBxLkqShSnJ2ku1JrupadnCSi5N8qf19cNe605Ncl+TaJE8bT9SaVybHkiRp2M4BNi5ZdhpwSVUdAVzSHpPkSOBE4DHtOW9Lss/oQtW8MzmWJElDVVWfBG5dsvgE4Nx2/1zgOV3Lz6+qu6vqq8B1wDGjiFMCxzmWJEnjsaaqtgG0adAf2pYfBnyqa7utbdkukpwKnAqwZs0aFhYWdn2T/WDTUTuXDWC57WfJjh07Zr6My1ltuU2OJUnSJMkyy2q5DavqTOBMgPXr19eGDRt22ebN513IG69cPt3ZctKu28+ShYUFlvtMZt1qy223CkmSNA43JzkUoP3d3pZvBdZ2bXc4cNOIY9McMzmWJEnjcBFwcrt/MnBh1/ITk9w/ySOBI4BLxxCf5pTdKiRJ0lAleT+wATgkyVbg9cBm4IIkLwGuB54HUFVXJ7kA+AKwE3hZVd0zlsA1l/aYHCc5G3gmsL2qHtuWHQx8AFgHbAGeX1W3tXWnAy8B7gF+tar+eiiRS5KkqVBVL+ix6rge258BnDG8iKTe+ulWcQ6OTShJkqQ5sMfk2LEJJUmSNC9W2ud4JGMTQu/xCWd93L5ZH5vwyhtvX3b5Ix+0z0yXW5IkTbZBX5A30LEJoff4hI5NON1OOe2jyy4/Z+P+M11uSZI02VY6lJtjE0qSJGnmrDQ5dmxCSZIkzZx+hnJzbELNrCRbgDvpDD24s6rW726oQkmSNNv2mBw7NqHmwFOr6htdjxeHKtyc5LT2+DXjCU2SJI2S00dLu+o1VKEkSZpxTh+teVfA3yQp4J1tFJVeQxXeSz/DEfYaihAmZzjCSR820PgkSaNkcqx5d2xV3dQS4IuTfLHfJ/YzHGGvoQhhcoYjnPRhA41PkjRKdqvQXKuqm9rf7cCH6czo2GuoQmniJNmS5MokVyS5rC07OMnFSb7U/j543HFK0rQwOdbcSrJ/kgMX7wM/A1xF76EKpUn11Ko6uqrWt8eLF5UeAVzSHkuS+mC3Cs2zNcCHk0Dnu/C+qvqrJJ9hmaEKpSlyAp0hOKFzUekCjrgijdy6HrPBAmzZfPwII9HeMDnW3KqqrwCPW2b5LfQYqlCaQCu+qFSStCuTY0mabiu+qLSfEVfmeTSOXqPNzMrn0WsknXne5xKYHEvSVOu+qDTJvS4qba3GPS8q7WfElXkejaPXaDOTMtLMap3S45T/ORv3n9t9LoEX5EnS1PKiUkkaPFuOJWl6eVGppl6SLcCdwD3Azqpan+Rg4APAOmAL8Pyqum1cMWq+mBxL0pTyolLNkKdW1Te6Hi8OR7g5yWntsSOuaCTsViFJkibNCXSGIaT9fc74QtG8seVYkiSN04qHI+xnxJVeo47A8Ece6fW+o3hvmN+RR1ZbbpNjaUr0GkzegeQlTbkVD0fYz4grvUYdgeGPPNJrRJBRvDfM72gzqy233SokSdLYdA9HCNxrOEKA3Q1HKA2DybEkSRoLhyPUJLJbhSRJGheHI9TEMTmWxsQ+xJLmncMRahKZHEuSJM2gK2+8fdmLAm2E2T37HEuSJEmNybEkSZLUmBxLkiRJjX2OpRnmRX+SJO0dW44lSZKkxpZjacr1ah2WJEl7z+RYmjAmu5okvYaCArvnSJpNdquQJEmSGluOpTnU3Tq96aid/9EyaEugJGnemRxLkiRpoKZ5tCSTY0mrMs0HQEmSlrLPsSRJktSYHEuSJEmN3Sok/QeHkZMkzTtbjiVJkqTGlmNJQ+GFepKkaTS05DjJRuCPgH2Ad1fV5mG9lzRo1t/h2duuG5uO2smG4YQy06zDmnbWYY3LUJLjJPsAbwV+GtgKfCbJRVX1hWG8nzRI1l9NO+uwpp11WP3o1dhyzsb9V/W6w2o5Pga4rqq+ApDkfOAEwEqtaWD9nTB20dhr1mFNO+uwxmZYyfFhwA1dj7cCTxrSe0mDZv2dEqNImvfUDaR7+u1Bv/cqWIc17azDGpthJcdZZlnda4PkVODU9nBHkmt7vNYhwDd2eYPfW1V802DZcs+6p/5ez3I/YoRh7LH+Qt91eOL3469OeIwriW+Ux4el8e3mvWeuDs/rcXjWyz1Px+Fx7ssRvffE1eFRvPdq6/CwkuOtwNqux4cDN3VvUFVnAmfu6YWSXFZV6wcb3uSz3GO1x/oL/dXhCSnPbk16jMa3InNVh4dlXss+IeW2Dg/AvJZ9teUe1jjHnwGOSPLIJPcDTgQuGtJ7SYNm/dW0sw5r2lmHNTZDaTmuqp1JXg78NZ0hWM6uqquH8V7SoFl/Ne2sw5p21mGN09DGOa6qjwEfG8BL7bHrxYyy3GM0Z/V30mM0vhWYszo8LPNa9okot3V4IOa17Ksqd6p26d8uSZIkzaVh9TmWJEmSps5EJMdJNia5Nsl1SU5bZn2S/HFb//kkTxhHnMPQR9k3JLk9yRXt9rpxxDlISc5Osj3JVT3Wz8T+3tO+Hacka5N8Isk1Sa5O8opxx7ScJPsk+ackHxl3LMtJclCSP0vyxfZZPmXcMQ3SJNfhYdrTMWpWTctxYW/MYx2exf24NwbxuzH25LhrisinA0cCL0hy5JLNng4c0W6nAm8faZBD0mfZAf5PVR3dbr810iCH4xxg427WT/3+3ot9Oy47gU1V9WjgycDLJiy+Ra8Arhl3ELvxR8BfVdUPA49jsmPdK1NQh4fpHHZ/jJpV03Jc6Msc1+GZ2o8rsOrfjbEnx3RNEVlV3wYWp4jsdgLwnur4FHBQkkNHHegQ9FP2mVNVnwRu3c0ms7C/J3rfVtW2qvpsu38nnQPJYeON6t6SHA4cD7x73LEsJ8kDgR8HzgKoqm9X1TfHGtRgTXQdHqY+jlEzaRqOC3tpLuvwDO7Hvg3qd2MSkuPlpohcuhP72WYa9VuupyT5XJK/TPKY0YQ2VrOwv6emDEnWAY8HPj3mUJb6Q+DVwHfGHEcv3w/8C/An7RTeu5PsP+6gBmhq6rAGb4KPC3tj7uvwjOzHvfGHDOB3YxKS436miOxrGskp1E+5Pgs8oqoeB7wZ+PNhBzUBZmF/T0UZkhwAfBB4ZVXdMe54FiV5JrC9qi4fdyy7cV/gCcDbq+rxwF3ALPVpnIo6rMGb1OPCCsx1HZ6h/diXQf5uTEJy3M8UkX1NIzmF+plm+46q2tHufwzYN8khowtxLGZhf098GZLsS+fAeV5VfWjc8SxxLPDsJFvonAr9ySTvHW9Iu9gKbK2qxRaZP6OTLM+Kia/DGrwJPy7srbmtwzO2H/s1sN+NSUiO+5ki8iLgxW0UgycDt1fVtlEHOgR7LHuS70uSdv8YOvvslpFHOlqzsL8neurTVqfOAq6pqjeNO56lqur0qjq8qtbR+ew+XlUvHHNY91JVXwduSPKotug44AtjDGnQJroOa/Am/biwAnNZh2dwP/ZlkL8bQ5shr1+9pohM8tK2/h10Zsh5BnAd8K/Az48r3kHqs+zPBX4lyU7gW8CJNeUztyR5P7ABOCTJVuD1wL4wO/t7CqY+PRZ4EXBlkivaste2sxPq338Dzms/vF9hCutqL1NQh4dmuWNUVZ013qhGYqaOC3Nch2dqP46DM+RJkiRJzSR0q5AkSZImgsmxJEmS1JgcS5IkSY3JsSRJktSYHEuSJEmNybEkSZLUmBxLkiRJjcmxJEmS1JgcS5IkSY3JsSRJktSYHEuSJEmNybEkSZLUmByPUZJ1SSrJfVf4/Eryg4OOS5IkaV6ZHI9Yki1JfmrccUi7M8566ndEkjROJseSBirJPuOOQZKklTI5HqEkfwo8HPiLJDuA57dVJyW5Psk3kvx61/bHJPnHJN9Msi3JW5Lcbxyxa34sradJXp3kfyf5epLbk3wyyWO6tj8nyduTfCzJXcBTkzwhyT8lubM99wNJfqfrOc9MckWr2/+Q5Ed6vfeIiy9JmnMmxyNUVS8CrgeeVVUHABe0VT8GPAo4Dnhdkke35fcA/x04BHhKW/9fRxq05s7SelpV/wv4S+AI4KHAZ4HzljztvwBnAAcClwIfBs4BDgbeD/zs4oZJngCcDfwy8L3AO4GLkty/x3tLkjQyJseT4Ter6ltV9Tngc8DjAKrq8qr6VFXtrKotdJKInxhjnJpTVXV2Vd1ZVXcDbwAel+RBXZtcWFX/t6q+AxwN3Bf446r696r6EJ2EedEvAe+sqk9X1T1VdS5wN/DkkRRGkqTdMDmeDF/vuv+vwAEASX4oyUfa6ew7gP9JpxVZGpkk+yTZnOTLrR5uaau66+INXfcfBtxYVdVj/SOATa1LxTeTfBNY254nSdJYmRyPXu15k//wduCLwBFV9UDgtUCGEpV0b9319L8AJwA/BTwIWNeWp8f224DDknSvX9t1/wbgjKo6qOv2PVX1/mVeS5KkkTI5Hr2bge/vc9sDgTuAHUl+GPiVoUUl3Vt3PT2QTreHW4DvoXMGY3f+kU5/+ZcnuW+SE4Bjuta/C3hpkielY/8kxyc5cJn3liRppEyOR+93gd9op5Kfu4dtf41Oq92ddBKKDww3NOk/dNfTg4GvATcCXwA+tbsnVtW3gf8XeAnwTeCFwEfoJNhU1WV0+h2/BbgNuA44Zbn3TvJrgyqQJEn9yL27BUrS4CX5NPCOqvqTccciSdLu2HIsaeCS/ESS72vdKk4GfgT4q3HHJUnSntx33AFImkmPojOO9wHAl4HnVtW28YYkSdKe2a1CkiRJauxWIUmSJDUT0a3ikEMOqXXr1i277q677mL//fcfbUB7aRpihOmIs1eMl19++Teq6iFjCKkvverwNHzmwzKvZZ/WOixJ6piI5HjdunVcdtlly65bWFhgw4YNow1oL01DjDAdcfaKMcnXRh9N/3rV4Wn4zIdlXss+rXVYktRhtwpJkiSpMTmWJEmSGpNjSZIkqZmIPscarnWnfRSATUft5JR2H2DL5uPHFZI09dZ1fZe6nbNx/i5ClKRZYsuxJEmS1JgcS5IkSY3JsSRJktSYHEuSJEnNHpPjJGcn2Z7kqq5lb0hyY5Ir2u0ZXetOT3JdkmuTPG1YgUv9sg5LkqR+9dNyfA6wcZnlf1BVR7fbxwCSHAmcCDymPedtSfYZVLDSCp2DdViSJPVhj8lxVX0SuLXP1zsBOL+q7q6qrwLXAcesIj5p1azDkiSpX6sZ5/jlSV4MXAZsqqrbgMOAT3Vts7Ut20WSU4FTAdasWcPCwsKyb7Jjx46e6ybFpMe46aidAKzZ77v3gYmMecSf5dDr8KTXjWGa9bJ3f5e6zXq5JWnWrTQ5fjvw20C1v28EfgHIMtvWci9QVWcCZwKsX7++NmzYsOwbLSws0GvdpJj0GE/pmgTkjVd+d5dvOWnDmCLqbYSf5Ujq8KTXjWGa9bKfsptJQGa53JI061Y0WkVV3VxV91TVd4B38d3TzluBtV2bHg7ctLoQpcGzDkuSpOWsKDlOcmjXw58FFkcBuAg4Mcn9kzwSOAK4dHUhSoNnHZYkScvZY7eKJO8HNgCHJNkKvB7YkORoOqebtwC/DFBVVye5APgCsBN4WVXdM5TIpT5ZhyVJUr/2mBxX1QuWWXzWbrY/AzhjNUFJg2QdliRJ/XKGPEmSJKlZzVBukvbgyhtv7zmqwZbNx484GkmStCe2HEuSJEmNybEkSZLUmBxLkiRJjcmxJEmS1JgcS5IkSY3JsSRJktSYHEuSJEmNybEkSZLUmBxLkiRJjcmxJEmS1JgcS5IkSc19xx2A5tO60z667PJzNu4/4kgkSZK+y5ZjSZIkqTE5liRJkhqTY0mSJKkxOZYkSZIak2NJkiSp2WNynOTsJNuTXNW17OAkFyf5Uvv74K51pye5Lsm1SZ42rMClflmHJUlSv/ppOT4H2Lhk2WnAJVV1BHBJe0ySI4ETgce057wtyT4Di1ZamXOwDkuSpD7sMTmuqk8Cty5ZfAJwbrt/LvCcruXnV9XdVfVV4DrgmMGEKq2MdViSJPVrpZOArKmqbQBVtS3JQ9vyw4BPdW23tS2TJs2q63CSU4FTAdasWcPCwsKub7IfbDpq57IBLLf9LNmxY8dMl7HXfp31ckvSrBv0DHlZZlktu2EfiQVMxw/NpMe4+CO+NFEbZ8wTnFj0XYer6kzgTID169fXhg0bdtnmzeddyBuvXP5rtuWkXbefJQsLCyz3mcyKU3Yzy+Msl1uSZt1Kk+ObkxzaWtwOBba35VuBtV3bHQ7ctNwL9JNYwHT8wE56jIs/4puO2nmvRG2cydkEJBarrsOSJGn2rHQot4uAk9v9k4ELu5afmOT+SR4JHAFcuroQpaGwDkuSpF3sseU4yfuBDcAhSbYCrwc2AxckeQlwPfA8gKq6OskFwBeAncDLquqeIcUu9cU6LEmS+rXH5LiqXtBj1XE9tj8DOGM1QUmDZB2WJEn9coY8SZIkqTE5liRJkhqTY0mSJKkxOZYkSZIak2NJkiSpMTmWJEmSGpNjSZIkqTE5liRJkhqTY0mSJKkxOZYkSZIak2NJkiSpMTmWJEmSGpNjSZIkqTE5liRJkhqTY0mSJKkxOZYkSZIak2NJkiSpMTmWJEmSGpNjSZIkqbnvap6cZAtwJ3APsLOq1ic5GPgAsA7YAjy/qm5bXZjScFiHJUlSt0G0HD+1qo6uqvXt8WnAJVV1BHBJeyxNMuuwJEkChtOt4gTg3Hb/XOA5Q3gPaZisw5IkzalVdasACvibJAW8s6rOBNZU1TaAqtqW5KGrDVIaohXX4SSnAqcCrFmzhoWFhV22WbMfbDpq57JvvNz2s2THjh0zXcZe+3XWyy1Jsy5VtfInJw+rqpta8nAx8N+Ai6rqoK5tbquqBy/z3O7E4onnn3/+su+xY8cODjjggBXHOAqTHuOVN94OdBK1m7/13eVHHfagMUX03ZiWeuSD9ln2s3zqU596eVe3h4FZTR3utn79+rrssst2Wf7m8y7kjVcu/z/ols3Hryb0ibewsMCGDRvGHcbQrDvto8suP2fj/suWO8lQ6rAkabBW1XJcVTe1v9uTfBg4Brg5yaGtxe1QYHuP554JnAmdxKLXj+g0/MBOeoyntB/xTUftvFeituWkDWOK6LsxLdUrsRiW1dRhSZI0e1bc5zjJ/kkOXLwP/AxwFXARcHLb7GTgwtUGKQ2DdViSJC21mpbjNcCHkyy+zvuq6q+SfAa4IMlLgOuB560+TGkorMOSJOleVpwcV9VXgMcts/wW4LjVBCWNgnVYkiQt5Qx5kiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJElSY3IsSZIkNSbHkiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJElSY3IsSZIkNSbHkiRJUnPfcQcwjdad9tF7Pd501E5OOe2jbNl8/JgikiRJ0iDYcixJkiQ1JseSJElSY3IsSZIkNSbHkiRJUmNyLEmSJDUmx5IkSVIztOQ4ycYk1ya5Lslpw3ofaRisv5IkzaehJMdJ9gHeCjwdOBJ4QZIjh/Fe0qBZfyVJml/DmgTkGOC6qvoKQJLzgROAL+ztC1154+2csmTSDcAJNzRMA6u/kiRpuqSqBv+iyXOBjVX1i+3xi4AnVdXLu7Y5FTi1PXwUcG2PlzsE+MbAgxysaYgRpiPOXjE+oqoeMooA+qm/bXk/dXgaPvNhmdeyj70OS5JWblgtx1lm2b2y8Ko6Ezhzjy+UXFZV6wcV2DBMQ4wwHXFOSIx7rL/QXx2ekPKMxbyWfV7LLUmzYlgX5G0F1nY9Phy4aUjvJQ2a9VeSpDk1rOT4M8ARSR6Z5H7AicBFQ3ovadCsv5IkzamhdKuoqp1JXg78NbAPcHZVXb3Cl9tj14sJMA0xwnTEOfYY57D+Dsu8ln1eyy1JM2EoF+RJkiRJ08gZ8iRJkqTG5FiSJElqJjY5nobpe5OcnWR7kqvGHUsvSdYm+USSa5JcneQV445pOUkekOTSJJ9rcf7muGPaG3uqr+n447b+80meMI44B62Pcm9IcnuSK9rtdeOIc9D29N2f1f0tSfNgIpPjKZq+9xxg47iD2IOdwKaqejTwZOBlE/pZ3g38ZFU9Djga2JjkyeMNqT991tenA0e026nA20ca5BDsxff0/1TV0e32WyMNcnjOYfff/Znb35I0LyYyOaZr+t6q+jawOH3vRKmqTwK3jjuO3amqbVX12Xb/TuAa4LDxRrWr6tjRHu7bbtNytWg/9fUE4D2tnJ8CDkpy6KgDHbCp+J4OQx/f/Vnc35I0FyY1OT4MuKHr8VYmMKGbNknWAY8HPj3mUJaVZJ8kVwDbgYuraiLjXEY/9XUW63S/ZXpK6y7zl0keM5rQxm4W97ckzYVJTY77mr5X/UtyAPBB4JVVdce441lOVd1TVUfTmZHumCSPHXNI/eqnvs5ine6nTJ8FHtG6y7wZ+PNhBzUhZnF/S9JcmNTk2Ol7ByjJvnQS4/Oq6kPjjmdPquqbwAKT3597UT/1dRbr9B7LVFV3LHaXqaqPAfsmOWR0IY7NLO5vSZoLk5ocO33vgCQJcBZwTVW9adzx9JLkIUkOavf3A34K+OJYg+pfP/X1IuDFbRSDJwO3V9W2UQc6YHssd5Lva3WQJMfQOebcMvJIR28W97ckzYWhTB+9WgOevndokrwf2AAckmQr8PqqOmu8Ue3iWOBFwJWtPy/Aa1sr3iQ5FDi3jYBwH+CCqvrImGPqS6/6muSlbf07gI8BzwCuA/4V+PlxxTsofZb7ucCvJNkJfAs4sWZgWs7lvvt0LiKd2f0tSfPC6aMlSZKkZlK7VUiSJEkjZ3IsSZIkNSbHkiRJUmNyLEmSJDUmx5IkSVJjcixJkiQ1JseSJElS8/8HgsHwBL1vvBsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(10, 8), bins=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation Matrix: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corr.html "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df.corr()\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box Plots: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BnlBJNia5PcmeJFd0z6PJkOTqJPcm+Xb3LJPAAE+gJFPAlcC5wAbgwiQbeqfShLgG2Ng9xKQwwJPpHGBPVd1RVY8C24Hzm2fSBKiqm4CfdM8xKQzwZJoG7pq3vHe0TtIYMcCTKYus8/WG0pgxwJNpL3D6vOXTgH1Ns0g6AgM8mXYB65OcmeR44AJgR/NMkhYwwBOoqg4BlwM3At8FvlhVu3un0iRI8gXg34FXJtmb5JLumZ7NfCuyJDXxDFiSmhhgSWpigCWpiQGWpCYGWJKaGGBNlCSnJHnfMhznHX7AkX5RBliT5hRgyQHOnKP5e/AO5j5pTjpqvg5YEyXJ45/8djvwz8CrgRcCzwH+sqquS7IO+Opo+28wF9OLgPcw9yFG9wG3VNVfJ3kZcx/tuRY4CPwJ8CLgy8ADo593VtV/LdMfURNkVfcA0jF2BfCqqjo7ySrgpKp6MMka4OYkj78l+5XAxVX1viQzwDuBX2Pu78StwC2j/bYBl1XV95O8DvhsVf326H6+XFX/uJx/OE0WA6xJFuDjSd4IHGbuIzlfMtr2o6q6efT7bwLXVdUjAEmuH/33ecAbgH9InviAuROWaXatAAZYk+w9zF06eG1V/W+SHwInjrYdmLffYh/fCXPPkdxfVWcPNqFWNJ+E06R5CFg9+v0FwL2j+L4FeOkRbvNvwHlJThyd9W4CqKoHgR8k+UN44gm7sxY5jnRUDLAmSlX9GPja6EsjzwZmkswydzb8n0e4zS7mPq7zm8C1wCxzT64xut0lSb4J7ObnX+20Hfhgkv8YPVEnPWO+CkJi7npvVT2c5CTgJmBzVd3aPZcmm9eApTnbRm+sOBH4e+Or5eAZsCQ18RqwJDUxwJLUxABLUhMDLElNDLAkNfk/4LmjJeOABdIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = df.drop(\"target\",axis=\"columns\")\n",
    "y1 = df.target\n",
    "for i in data1.columns:\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    sns.boxplot(x=y1, y=i, data=data1)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is slight imbalance in our dataset but since it is not major we will not worry about it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Train test split</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"target\",axis=\"columns\")\n",
    "y = df.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling data using standard scalar: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.94979429,  0.68265615,  1.97647049,  0.76406571, -0.26128493,\n",
       "         2.38979311, -1.0025412 ,  0.01882584, -0.69834428,  1.08402203,\n",
       "        -2.27118179, -0.71491124, -2.1479552 ],\n",
       "       [-1.92854796,  0.68265615,  1.005911  , -0.09140084,  0.06774054,\n",
       "        -0.41844626,  0.90165655,  1.63697881, -0.69834428,  2.11892611,\n",
       "        -2.27118179, -0.71491124, -0.51399432],\n",
       "       [-1.48572607, -1.46486632,  0.0353515 , -0.09140084, -0.82256367,\n",
       "        -0.41844626, -1.0025412 ,  0.98097085, -0.69834428,  0.30784398,\n",
       "         0.97951442, -0.71491124, -0.51399432]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler #standerdization process\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting Data for training and testing: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  #splitting dataset \n",
    "X_train1, X_test1, y_train, y_test = train_test_split(X_scaled, y, random_state=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection: https://scikit-learn.org/stable/modules/feature_selection.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_classif #for selecting more important features\n",
    "num_features_to_select = 5\n",
    "selector = SelectKBest(score_func=f_classif, k=num_features_to_select)\n",
    "X_train = selector.fit_transform(X_train1, y_train)\n",
    "X_test = selector.transform(X_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-NN classifier function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclidean_distance(p1, p2):  #Euclidean Distance function\n",
    "    return sum((a - b) ** 2 for a, b in zip(p1, p2)) ** 0.5 #square root of squared distance\n",
    "\n",
    "def knn1(train_data, labels, test_data, k): #KNN predictor function\n",
    "    distances = [(euclidean_distance(test_data, data_point), label) for data_point, label in zip(train_data, labels)] \n",
    "    sorted_distances = sorted(distances, key=lambda x: x[0]) #Sort the list of distances\n",
    "    k_near_neighbors = sorted_distances[:k]  # K nearest points\n",
    "    label_cnts = {} \n",
    "    label_cnts = Counter(k_near_neighbors)\n",
    "    return label_cnts.most_common(1)[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bagging_FUNC(X_train, X_test, y_train, estimater, n_bags):\n",
    "\n",
    "    predictions = []\n",
    "    for _ in range(n_bags):\n",
    "        # Bootstrap sample of the training data\n",
    "        index = np.random.choice(len(X_train), len(X_train), replace=True)\n",
    "        X_train_btstr = X_train[index]\n",
    "        y_train_btstr = y_train.iloc[index]\n",
    "\n",
    "        # KNN classifier on the bootstrap sample\n",
    "        if estimater>0:\n",
    "            #predicted_labels = knnsrc(X_train_btstr, y_train_btstr, X_test, estimater) \n",
    "            predicted_labels = [knn1(X_train_btstr, y_train_btstr, X_t, estimater) for X_t in X_test]\n",
    "        # LogisticRegression on the bootstrap sample\n",
    "        if estimater==0:\n",
    "            model = LogisticRegression(random_state=42)\n",
    "            # Fit the model to the training data\n",
    "            model.fit(X_train, y_train)\n",
    "            # Make predictions on the test set\n",
    "            predicted_labels = model.predict(X_test)\n",
    "            \n",
    "        predictions.append(predicted_labels) #collecting all predictions togather\n",
    "\n",
    "    # Aggregating predictions using majority voting\n",
    "    bagged_predictions = []\n",
    "    for i in range(len(predictions[0])):\n",
    "        labels = [predictions[j][i] for j in range(n_bags)]\n",
    "        unique_labels, label_counts = np.unique(labels, return_counts=True)\n",
    "        major_label = unique_labels[np.argmax(label_counts)]\n",
    "        bagged_predictions.append(major_label)\n",
    "    return bagged_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MAIN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "References:\n",
    "confusion matrix: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html\n",
    "\n",
    "Ensemble methods Bagging: https://scikit-learn.org/stable/modules/ensemble.html\n",
    "\n",
    "AdaBoost: https://scikit-learn.org/stable/modules/ensemble.html#adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Menu:\n",
      "1. Bagging\n",
      "2. Boosting\n",
      "3. Traditional Algorithm\n",
      "4. Comparision\n",
      "5. Exit\n",
      "Enter your choice: 2\n",
      "\n",
      "Choose base estimater\n",
      "1. Decision Tree\n",
      "2. LOGESTIC\n",
      "Enter your choice: 2\n",
      "Enter number of estimators: 50\n",
      "\n",
      "Boosting with Logestic Regression Accuracy: 75.000000 %\n",
      "\n",
      "Menu:\n",
      "1. Bagging\n",
      "2. Boosting\n",
      "3. Traditional Algorithm\n",
      "4. Comparision\n",
      "5. Exit\n",
      "Enter your choice: 5\n",
      "\n",
      "Thank You.\n"
     ]
    }
   ],
   "source": [
    "while 1:\n",
    "    print(\"\\nMenu:\")\n",
    "    print(\"1. Bagging\")\n",
    "    print(\"2. Boosting\")\n",
    "    print(\"3. Traditional Algorithm\")\n",
    "    print(\"4. Comparision\")\n",
    "    print(\"5. Exit\")\n",
    "    choice = input(\"Enter your choice: \")\n",
    "#--------------------------------------------------------------------------------------------------------------------\n",
    "#For Bagging\n",
    "    if choice == \"1\":   \n",
    "        print(\"\\nChoose base estimater\")\n",
    "        print(\"1. KNN\")\n",
    "        print(\"2. LOGESTIC\")\n",
    "        choice1 = input(\"Enter your choice: \")\n",
    "        num_bags = int(input(\"Enter number of bags: \"))\n",
    "        if choice1 == \"1\":\n",
    "            KN = int(input(\"Enter K: \"))\n",
    "            bagged_predictions = bagging_FUNC(X_train, X_test, y_train, KN, num_bags)\n",
    "            bagged_predictions = np.array(bagged_predictions)\n",
    "            bagged_predictions = bagged_predictions.astype(int)\n",
    "            # Plot the confusion matrix as a heatmap\n",
    "            cm = confusion_matrix(y_test, bagged_predictions)\n",
    "            plt.figure(figsize=(8, 6))\n",
    "            sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
    "                        xticklabels=[\"Predicted 0\", \"Predicted 1\"],\n",
    "                        yticklabels=[\"Actual 0\", \"Actual 1\"])\n",
    "            plt.xlabel('Predicted')\n",
    "            plt.ylabel('Actual')\n",
    "            plt.title('Confusion Matrix - Bagging with k-NN')\n",
    "            plt.show()\n",
    "            accuracy = format((np.mean(bagged_predictions == y_test)*100),\".6f\")\n",
    "            print(\"\\nBagging k-NN Accuracy:\", accuracy, \"%\")\n",
    "        elif choice1 == \"2\":\n",
    "            bagged_predictions = bagging_FUNC(X_train, X_test, y_train, 0, num_bags)\n",
    "            # Plot the confusion matrix as a heatmap\n",
    "            cm = confusion_matrix(y_test, bagged_predictions)\n",
    "            plt.figure(figsize=(8, 6))\n",
    "            sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
    "                        xticklabels=[\"Predicted 0\", \"Predicted 1\"],\n",
    "                        yticklabels=[\"Actual 0\", \"Actual 1\"])\n",
    "            plt.xlabel('Predicted')\n",
    "            plt.ylabel('Actual')\n",
    "            plt.title('Confusion Matrix - Bagging with Logestic Regression')\n",
    "            plt.show()\n",
    "            accuracy = format((np.mean(bagged_predictions == y_test)*100),\".6f\")\n",
    "            print(\"\\nBagging Logestic Accuracy:\", accuracy, \"%\")\n",
    "        else:\n",
    "            print(\"\\nInvalid choice. Please try again.\")\n",
    "\n",
    "#--------------------------------------------------------------------------------------------------------------------\n",
    "#For Boosting\n",
    "    elif choice == \"2\":   \n",
    "        print(\"\\nChoose base estimater\")\n",
    "        print(\"1. Decision Tree\")\n",
    "        print(\"2. LOGESTIC\")\n",
    "        choice1 = input(\"Enter your choice: \")\n",
    "        estimators_boost = int(input(\"Enter number of estimators: \"))\n",
    "        if choice1 == \"1\":\n",
    "            KN = int(input(\"Enter Deapth: \"))\n",
    "\n",
    "            dt_classifier = DecisionTreeClassifier(max_depth=KN)\n",
    "\n",
    "            adaboost_classifier = AdaBoostClassifier(base_estimator=dt_classifier, n_estimators=estimators_boost)\n",
    "            \n",
    "            # Train the AdaBoost classifier\n",
    "            adaboost_classifier.fit(X_train, y_train)\n",
    "\n",
    "            # Evaluate the classifier on the test data\n",
    "            accuracy = format((adaboost_classifier.score(X_test, y_test)*100),\".6f\")\n",
    "\n",
    "            print(\"\\nBoosting with Decision Tree Classifier Accuracy:\", accuracy, \"%\")\n",
    "        elif choice1 == \"2\":\n",
    "            # Create a base Logistic Regression\n",
    "            LgRegression = LogisticRegression()\n",
    "\n",
    "            # Create an AdaBoost classifier with KNN as base learner\n",
    "            adaboost_classifier = AdaBoostClassifier(base_estimator=LgRegression, n_estimators=estimators_boost)\n",
    "\n",
    "            # Train the AdaBoost classifier\n",
    "            adaboost_classifier.fit(X_train, y_train)\n",
    "\n",
    "            # Evaluate the classifier on the test data\n",
    "            accuracy = format((adaboost_classifier.score(X_test, y_test)*100),\".6f\")\n",
    "            print(\"\\nBoosting with Logestic Regression Accuracy:\", accuracy, \"%\")\n",
    "        else:\n",
    "            print(\"\\nInvalid choice. Please try again.\")\n",
    "\n",
    "#--------------------------------------------------------------------------------------------------------------------\n",
    "#Traditional Algorithms\n",
    "    elif choice == \"3\":    \n",
    "        print(\"\\n Choose estimater\")\n",
    "        print(\"1. KNN\")\n",
    "        print(\"2. LOGESTIC\")\n",
    "        print(\"3. Decision Tree\")\n",
    "        choice2 = input(\"\\n Enter your choice: \")\n",
    "        if choice2 == \"1\":\n",
    "            KKN = int(input(\"Enter K: \"))\n",
    "            predicted_labels = [knn1(X_train, y_train, d, KKN) for d in X_test]\n",
    "            predicted_labels1 =  [predicted_labels[i][1] for i in range(len(predicted_labels))]\n",
    "            # Plot the confusion matrix as a heatmap\n",
    "            cm = confusion_matrix(y_test, predicted_labels1)\n",
    "            plt.figure(figsize=(8, 6))\n",
    "            sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
    "                        xticklabels=[\"Predicted 0\", \"Predicted 1\"],\n",
    "                        yticklabels=[\"Actual 0\", \"Actual 1\"])\n",
    "            plt.xlabel('Predicted')\n",
    "            plt.ylabel('Actual')\n",
    "            plt.title('Confusion Matrix - k-NN')\n",
    "            plt.show()\n",
    "                 \n",
    "            accuracy = format((np.mean(predicted_labels1 == y_test)*100),\".6f\")\n",
    "            print(\"\\nTraditional k-NN Accuracy:\", accuracy, \"%\")\n",
    "        elif choice2 == \"2\":\n",
    "            model = LogisticRegression(random_state=42)\n",
    "            # Fit the model to the training data\n",
    "            model.fit(X_train, y_train)\n",
    "            # Make predictions on the test set\n",
    "            predicted_labels = model.predict(X_test)\n",
    "            cm = confusion_matrix(y_test, predicted_labels)\n",
    "            plt.figure(figsize=(8, 6))\n",
    "            sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
    "                        xticklabels=[\"Predicted 0\", \"Predicted 1\"],\n",
    "                        yticklabels=[\"Actual 0\", \"Actual 1\"])\n",
    "            plt.xlabel('Predicted')\n",
    "            plt.ylabel('Actual')\n",
    "            plt.title('Confusion Matrix - Logestic Rreegression')\n",
    "            plt.show()\n",
    "            accuracy = format((np.mean(predicted_labels == y_test)*100),\".6f\")\n",
    "            print(\"\\nTraditional Logestic Accuracy:\", accuracy, \"%\")\n",
    "        elif choice2 == \"3\":\n",
    "            KN = int(input(\"Enter Deapth: \"))\n",
    "\n",
    "            dt_classifier = DecisionTreeClassifier(max_depth=KN)\n",
    "            # Fit the model to the training data\n",
    "            dt_classifier.fit(X_train, y_train)\n",
    "            # Make predictions on the test set\n",
    "            predicted_labels = dt_classifier.predict(X_test)\n",
    "            cm = confusion_matrix(y_test, predicted_labels)\n",
    "            plt.figure(figsize=(8, 6))\n",
    "            sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", \n",
    "                        xticklabels=[\"Predicted 0\", \"Predicted 1\"],\n",
    "                        yticklabels=[\"Actual 0\", \"Actual 1\"])\n",
    "            plt.xlabel('Predicted')\n",
    "            plt.ylabel('Actual')\n",
    "            plt.title('Confusion Matrix - Decision Tree')\n",
    "            plt.show()\n",
    "            accuracy = format((np.mean(predicted_labels == y_test)*100),\".6f\")\n",
    "            print(\"\\nTraditional Decision Tree Classifier Accuracy:\", accuracy, \"%\")\n",
    "        else:\n",
    "            print(\"\\nInvalid choice. Please try again.\")\n",
    "#--------------------------------------------------------------------------------------------------------------------------------\n",
    "#Comarision\n",
    "    elif choice == \"4\":\n",
    "        print(\"\\n Choose compair\")\n",
    "        print(\"1. Bagging\")\n",
    "        print(\"2. Boosting\")\n",
    "        choice2 = input(\"\\n Enter your choice: \")\n",
    "        if choice2 == \"1\":\n",
    "            baggingc=[]\n",
    "            KKN = int(input(\"Enter K: \"))\n",
    "            nbag= int(input(\"Enter num of bags: \"))\n",
    "            predicted_labels = [knn1(X_train, y_train, d, KKN) for d in X_test]\n",
    "            predicted_labels1 =  [predicted_labels[i][1] for i in range(len(predicted_labels))]\n",
    "            baggingc.append((np.mean(predicted_labels1 == y_test)*100))\n",
    "            \n",
    "            bagged_predictions = bagging_FUNC(X_train, X_test, y_train, KKN, nbag)\n",
    "            bagged_predictions = np.array(bagged_predictions)\n",
    "            bagged_predictions = bagged_predictions.astype(int)\n",
    "            baggingc.append(np.mean(bagged_predictions == y_test)*100)\n",
    "            \n",
    "            model = LogisticRegression(random_state=42)\n",
    "            model.fit(X_train, y_train)\n",
    "            predicted_labelsl = model.predict(X_test)\n",
    "            baggingc.append(np.mean(predicted_labelsl == y_test)*100)\n",
    "            \n",
    "            bagged_predictionsl = bagging_FUNC(X_train, X_test, y_train, 0, nbag)\n",
    "            baggingc.append(np.mean(bagged_predictionsl == y_test)*100)\n",
    "            \n",
    "            labels = ['KNN', 'Bagging KNN', 'Logistic Regression', 'Bagging Logistic Regression']\n",
    "            colors = ['purple', 'purple', 'green', 'green']\n",
    "            plt.figure(figsize=(10, 6))\n",
    "            # Create an array of indices for the x-axis\n",
    "            x = np.arange(len(labels))\n",
    "            # Create the bar graph\n",
    "            plt.bar(x, baggingc, width=0.4, align='center', tick_label=labels, color=colors)\n",
    "            # Set the y-axis label\n",
    "            plt.ylabel('Accuracy (%)')\n",
    "            # Set a title for the graph\n",
    "            plt.title('Comparison of Different Models with Bagging')\n",
    "            # Show the graph\n",
    "            plt.show()\n",
    "            \n",
    "        elif choice2 == \"2\":\n",
    "            boostc=[]\n",
    "            estimators_boost = int(input(\"Enter number of Estimators: \"))\n",
    "            KN = int(input(\"Enter Deapth for Decision Tree: \"))\n",
    "            dt_classifier = DecisionTreeClassifier(max_depth=KN)\n",
    "            dt_classifier.fit(X_train, y_train)\n",
    "            predicted_labels = dt_classifier.predict(X_test)\n",
    "            boostc.append(np.mean(predicted_labels == y_test)*100)\n",
    "            \n",
    "            adaboost_classifier = AdaBoostClassifier(base_estimator=dt_classifier, n_estimators=estimators_boost)\n",
    "            adaboost_classifier.fit(X_train, y_train)\n",
    "            boostc.append(adaboost_classifier.score(X_test, y_test)*100)\n",
    "            \n",
    "            model = LogisticRegression(random_state=42)\n",
    "            model.fit(X_train, y_train)\n",
    "            predicted_labelsl = model.predict(X_test)\n",
    "            boostc.append(np.mean(predicted_labelsl == y_test)*100)\n",
    "            \n",
    "            adaboost_classifierl = AdaBoostClassifier(base_estimator=model, n_estimators=estimators_boost)\n",
    "            adaboost_classifierl.fit(X_train, y_train)\n",
    "            boostc.append(adaboost_classifierl.score(X_test, y_test)*100)\n",
    "            \n",
    "            labels = ['Decision Tree', 'AdaBoost Decision Tree', 'Logistic Regression', 'AdaBoost Logistic Regression']\n",
    "            colors = ['purple', 'purple', 'green', 'green']\n",
    "            # Create an array of indices for the x-axis\n",
    "            \n",
    "            x = np.arange(len(labels))\n",
    "            plt.figure(figsize=(10, 6))\n",
    "            # Create the bar graph\n",
    "            plt.bar(x, boostc, width=0.4, align='center', tick_label=labels, color=colors)\n",
    "           \n",
    "            # Set the y-axis label\n",
    "            \n",
    "            plt.ylabel('Accuracy (%)')\n",
    "            # Set a title for the graph\n",
    "            plt.title('Comparison of Different Models with AdaBoost')\n",
    "            # Show the graph\n",
    "            plt.show()\n",
    "        else:\n",
    "            print(\"\\nInvalid choice. Please try again.\")\n",
    "#--------------------------------------------------------------------------------------------------------------------------------\n",
    "#Exit\n",
    "    elif choice == \"5\":\n",
    "        print(\"\\nThank You.\")\n",
    "        break\n",
    "#---------------------------------------------------------------------------------------------------------------------------------\n",
    "#Invalid choice\n",
    "    else:\n",
    "        print(\"\\nInvalid choice. Please try again.\")\n",
    "        "
   ]
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