ML_Algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36dab29a",
   "metadata": {},
   "source": [
    "## K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1edef50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7778ff0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Income($)\n",
       "0      Rob   27      70000\n",
       "1  Michael   29      90000\n",
       "2    Mohan   29      61000\n",
       "3   Ismail   28      60000\n",
       "4     Kory   42     150000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"files/income.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c243ae17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Income($)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Age'],df['Income($)'])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel('Income($)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7e9d0a",
   "metadata": {},
   "source": [
    "initially iam going to create 3 centroids,And here we use fit_predict instead of fit() since it is a unsupervised algorithm , here we did not pass the target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7cbba77",
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cdb6e26d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = km.fit_predict(df[['Age','Income($)']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c35a315",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cluster'] = y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89dcca20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>27</td>\n",
       "      <td>70000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>29</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>29</td>\n",
       "      <td>61000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>28</td>\n",
       "      <td>60000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>42</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Income($)  cluster\n",
       "0      Rob   27      70000        2\n",
       "1  Michael   29      90000        2\n",
       "2    Mohan   29      61000        0\n",
       "3   Ismail   28      60000        0\n",
       "4     Kory   42     150000        1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f4a9f1",
   "metadata": {},
   "source": [
    "if we want to see the centroids we can see it using the cluster_centres_ attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33ccb12b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.29090909e+01, 5.61363636e+04],\n",
       "       [3.82857143e+01, 1.50000000e+05],\n",
       "       [3.40000000e+01, 8.05000000e+04]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "980b4770",
   "metadata": {},
   "source": [
    "Now iam going to plot each clusters and centroids graphically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e668f2bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df.cluster==0]\n",
    "df2 = df[df.cluster==1]\n",
    "df3 = df[df.cluster==2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dce22f9",
   "metadata": {},
   "source": [
    "Now i just splitted my clusters into different dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4617dad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19f4898bf70>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
    "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
    "plt.scatter(df3.Age,df3['Income($)'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Income ($)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10315f8",
   "metadata": {},
   "source": [
    "It shows some error , so we want to scales our features into same scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "290956bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e632299f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler.fit(df[['Income($)']])\n",
    "df['Income($)'] = scaler.transform(df[['Income($)']])\n",
    "\n",
    "scaler.fit(df[['Age']])\n",
    "df['Age'] = scaler.transform(df[['Age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "26bc760b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.213675</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.136752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Age  Income($)  cluster\n",
       "0      Rob  0.058824   0.213675        2\n",
       "1  Michael  0.176471   0.384615        2\n",
       "2    Mohan  0.176471   0.136752        0\n",
       "3   Ismail  0.117647   0.128205        0\n",
       "4     Kory  0.941176   0.897436        1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "582b560c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19f4977e080>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.Age,df['Income($)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "59bf3492",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3)\n",
    "y_predicted = km.fit_predict(df[['Age','Income($)']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "090901f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income($)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.213675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Michael</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mohan</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.136752</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ismail</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.128205</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kory</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.897436</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name       Age  Income($)  cluster\n",
       "0      Rob  0.058824   0.213675        1\n",
       "1  Michael  0.176471   0.384615        1\n",
       "2    Mohan  0.176471   0.136752        1\n",
       "3   Ismail  0.117647   0.128205        1\n",
       "4     Kory  0.941176   0.897436        0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster']=y_predicted\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb4be544",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.72268908, 0.8974359 ],\n",
       "       [0.1372549 , 0.11633428],\n",
       "       [0.85294118, 0.2022792 ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e71b1bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19f48931f00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df[df.cluster==0]\n",
    "df2 = df[df.cluster==1]\n",
    "df3 = df[df.cluster==2]\n",
    "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
    "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
    "plt.scatter(df3.Age,df3['Income($)'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a91632",
   "metadata": {},
   "source": [
    "### Elbow plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e63081",
   "metadata": {},
   "source": [
    "Here i used inertia_ it gives me the within-sum of cluster squares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b8a8fae1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "sse = []\n",
    "k_rng = range(1,10)\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df[['Age','Income($)']])\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edeca5b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19f489de530>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of squared error')\n",
    "plt.plot(k_rng,sse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08cbf2f",
   "metadata": {},
   "source": [
    "### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "364f274b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"files/Mall_Customers.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75d16252",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Age','Gender'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "61df485d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1                  15                      39\n",
       "1           2                  15                      81\n",
       "2           3                  16                       6\n",
       "3           4                  16                      77\n",
       "4           5                  17                      40"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e6b43ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "049a1f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                200\n",
       "Annual Income (k$)         64\n",
       "Spending Score (1-100)     84\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "167db6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "479d5763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0450fb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Spending Score (1-100)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj0AAAGwCAYAAABCV9SaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABeN0lEQVR4nO3de1wU5f4H8M9yVRQRNFnwgnhLUVPDS95voWZpnjp11DQtM68l5i1PddQ6gehJrSxNu1iaWae0tMykNBTFVBDLy1EjUjM5nJQAbyDs/P7gtysLe5nZndmdnfm8Xy9exc7s7LPjzO7D8/1+n8cgCIIAIiIiIo3z83YDiIiIiDyBnR4iIiLSBXZ6iIiISBfY6SEiIiJdYKeHiIiIdIGdHiIiItIFdnqIiIhIFwK83QA1MJlM+P333xEaGgqDweDt5hAREZEIgiCguLgY0dHR8PNzPo7DTg+A33//HY0bN/Z2M4iIiMgF58+fR6NGjZzux04PgNDQUAAVJ61OnTpebg0RERGJUVRUhMaNG1u+x51hpwewhLTq1KnDTg8REZGPEZuawkRmIiIi0gV2eoiIiEgX2OkhIiIiXWCnh4iIiHSBnR4iIiLSBXZ6iIiISBfY6SEiIiJd8GqnZ8+ePRg2bBiio6NhMBjw+eefW20XBAELFy5EdHQ0atasiX79+uH48eNW+5SUlOCpp55C/fr1UatWLQwfPhy//fabB98FERER+QKvdnquXr2KDh06YOXKlTa3L1myBMuWLcPKlStx6NAhGI1GJCQkoLi42LJPYmIitmzZgk2bNiE9PR1XrlzBfffdh/Lyck+9DSIiIvIBBkEQBG83AqiYTXHLli0YMWIEgIpRnujoaCQmJmLevHkAKkZ1IiMjkZKSgkmTJqGwsBC33XYb1q9fj7/97W8Abq2jtX37dgwePNjma5WUlKCkpMTyu3ka68LCQlXMyFxuEnAw9zLyi2+gQWgNdI2NgL8fF0IlIiKqrKioCGFhYaK/v1Wb05Obm4u8vDwMGjTI8lhwcDD69u2L/fv3AwAyMzNx8+ZNq32io6PRrl07yz62JCcnIywszPKjpsVGdxy7iF4puzBq7QHM2JSNUWsPoFfKLuw4dtHbTSMiIvJpqu305OXlAQAiIyOtHo+MjLRsy8vLQ1BQEMLDw+3uY8v8+fNRWFho+Tl//rzMrXfNjmMXMWVDFi4W3rB6PK/wBqZsyGLHh4iIyA2qX3C06iJigiA4XVjM2T7BwcEIDg6WpX1yKTcJWLTtBGzFGgUABgCLtp1AQpzRYaiLoTEiIiLbVNvpMRqNACpGc6KioiyP5+fnW0Z/jEYjSktLUVBQYDXak5+fjx49eni2wW46mHu52ghPZQKAi4U3cDD3Mro3r2dznx3HLmLRthNWx4kKq4EFw+IwpF2UzecQERHphWrDW7GxsTAajUhNTbU8VlpairS0NEuHJj4+HoGBgVb7XLx4EceOHfO5Tk9+sf0Oj5j9GBojIiJyzKsjPVeuXMHPP/9s+T03NxfZ2dmIiIhAkyZNkJiYiKSkJLRs2RItW7ZEUlISQkJCMHr0aABAWFgYJkyYgFmzZqFevXqIiIjA7Nmz0b59e9x9993eelsuaRBaw+X95AqNERERaZlXOz2HDx9G//79Lb8/88wzAIBx48Zh3bp1mDt3Lq5fv46pU6eioKAA3bp1w86dOxEaGmp5zvLlyxEQEICHH34Y169fx8CBA7Fu3Tr4+/t7/P24o2tsBKLCaiCv8IbNzosBgDGsIkenKjlCY0R6wJw39/Ecki9TzTw93iS1zl8p5hAVAKuOj/njZNWYO23m5nyRfQEzNmU7Pf6rIzvi/o4N3W8okQ9izpv7eA5JbTQzT48eDWkXhVVj7oQxzDqEZQyrYbfDA7gXGiPSA+a8uY/nkLRAtdVbejWkXRQS4oySho/dCY0RaR1z3tzHc0hawZEeFfL3M6B783q4v2NDdG9ez+mHiL+fAQuGxQG4FQozM/++YFgcP4xIl6TkvJFtPIekFez0aISroTEirXN3OgjiOSTtYHhLQ1wJjRFpHXPe3MdzSFrBTo/GmENjRL5K7pJoteW8+WLJt9rOIZGr2OkhItVQoiTanPM2ZUMWDLA9HYSnct58teRbTeeQyB3M6SEiVVCyJFoNOW++XvKthnNI5C5OTgh1TE7oi0PeRHIpNwnolbLLboWQOXySPm+AW/eFt+4zT70/T+BnFamJ1O9vhrdUwFeHvInk4qmlVLyV86alpWKYN0i+jOEtL/P1IW8iOWi9JFrr74/IV3Ckx4s4yylRBblKopUKvbh7XJZ8E6kDOz1epKUhbyJ3yFESrVSYWI7jsuSbSB0Y3vIiDnkTVXB3KRWlwsRyHZdLxRCpAzs9XsQhb6JbXC2JdhYmBirCxOUmaYWqch+XJd9E3sfwlhfpZchbrjwLlspqnytLqSgVJlbiuLbeX3xMODLPFuCL7Au8rokUxk6PF+lhllO58ixY1q8fUkuilQoTK3Xcyu9vx7GL6Lt0N69rIg9heMvLtDzkLVc+BMv6yRGlwsRKh595XRN5Hkd6VECLq6PLVY7Psn5tkjNUqVSYWMnwM69r9WDYXF/Y6VEJrc1yKlc+BMv6tUfuUKVSYWIlw8+8rtWBYXP9YXiLFCFXPgTL+rVFqZCOUmFipY7L69r7GF7UJ470kCLkyodgWb92KB3SUSpMrMRxeV17F8OL+sVODylCrnwIvZT164EnQjpSwsRScjnkDj974rpmrop9DC/qFzs9pAi58iH0UNavF2oK6Xg7l0Pp69rb70/t1HQtkmcxp4cUI1c+hJbL+vVELSEdteRyKHVdq+X9qZlarkXyPIMgCNLmZtegoqIihIWFobCwEHXq1PF2czSHMzITUPHv1ytll9OQTvq8AYr9u5rbYC+04Yk22GqTXNe1Gt+fGqnhWiR5SP3+ZniLFCdXPoTWyvr1Rg2hSjXmcsh5Xavx/amRGq5F8g6Gt4jIY7wdqtR6LofW35+cvH0tkndwpIeIPMqbM5DXrxUs635qw1wVabQ4Gz45xk4PEXmcOyEdt3JgxH6X+eh3nhKl8FrPpWPYXF/Y6SEin+FuKfYfV0pEvY7Y/dRG7lwVlr6T1jCnh4h8ghyl2HoI/8iVq8LSd9IijvSQKFof4iZ1c3XZgKrXbcfGdeFnAEwOJurwMwDxMeFyvwWPcjdXhcs0kFax00NOcYibvM2VUmxb121ErUCHHR6gokOUebbA5/M83MlVYek7aRXDW+QQh7hJDaSWYtu7bi9fvSnr62kVS99Jq9jpIbucDXEDFUPc5c7+dCZyk5RcHEfXrdyvJ7dyk4CMnEv4IvsCMnIuee3eql9bXMm+L+c+kT4xvEV2cYib1EJKKbaz69YROVY3d5Vawsg7jl3Ewq3HHe7jzfNE5A6O9JBdHOImtTCXYgPVp9CpWort6vXozeUH1BJGNrcjr8h+yT6XaSBfxk4P2aWH8l7yHWJLscVejxG1ghwex1PUEkYWGxbkMg3kyxjeIruUmN2VyB1iSrHFXrdpc/oj82yB16dhUEsYWWxY8F9/7YCeLeu79Bqc+oK8jZ0esosrEZMaOSvFFnvdBgX4qSIXTS1hZLHH/+Oqa7NVqyVnifSN4S1yiCsRky/ypetWLWFkJduhlpwlIo70kFNciZh8ka9ct2oJIyvVDs7uTGrCkR4SxRxSuL9jQ3RvXo8fTuQTfOG6lVKZ5ovtkJKzRKQ0dnqIiLxMLeE4JdqhlpwlIoDhLSIiVVBLOE7udqglZ4kIYKeHiAiAOsqp3VkkVK3tUEvOEnmHGu6rytjpISLdYzm1cjj1hX6p8b5iTg8R6RrLqZWnlpwl8hy13lcGQRB0v0R2UVERwsLCUFhYiDp16ni7OaqgtiFJIiWUmwT0Stllt7rIHHpJnzeA178M+LmiD568r6R+fzO8RdWocUiSSAlqWQJCL9SSs0TKUvN9xfAWWVHrkCSRElhOTSQ/Nd9X7PSQhVpWeybyFJZTE8lPzfcVOz0KKTcJyMi5hC+yLyAj55JPdBSUmjnVF88F6YO5nNpeVoEBFaFdllMTiafm+4o5PQrw1ZwYJYYkffVckD6wnJpIfmq+rzjSIzNfzomRe0jSl88F6QfLqYnkp9b7iiXrkK9k3dfLX83tdzZzqpj2+/q5IP1hOTWR/JS+r6R+f6t6pKesrAzPP/88YmNjUbNmTTRr1gwvvvgiTCaTZR9BELBw4UJER0ejZs2a6NevH44fP+6V9vr6asJyrrLs6+eC9McXVmQn8jVqu69U3elJSUnB6tWrsXLlSpw8eRJLlizB0qVL8frrr1v2WbJkCZYtW4aVK1fi0KFDMBqNSEhIQHFxscfbq+YyPbHkGpLUwrkgIiJtUXUic0ZGBu6//37ce++9AICmTZvio48+wuHDhwFUjPKsWLECzz33HB544AEAwPvvv4/IyEhs3LgRkyZN8mh7lSrT8/SwuxyrLKu5ZJGIyNMYPlUHVXd6evXqhdWrV+P06dNo1aoVjh49ivT0dKxYsQIAkJubi7y8PAwaNMjynODgYPTt2xf79++32+kpKSlBSUmJ5feioiJZ2qvEasLeqn5yd+ZUrqxMRFSBVazqoerw1rx58zBq1Ci0bt0agYGB6NSpExITEzFq1CgAQF5eHgAgMjLS6nmRkZGWbbYkJycjLCzM8tO4cWNZ2itnTgzg29VPcp8LIiJf5Muf41qk6k7Pxx9/jA0bNmDjxo3IysrC+++/j3/96194//33rfYzGKy/OAVBqPZYZfPnz0dhYaHl5/z587K1Wa6cGC3MjqzWkkUiIk/Qwue41qg6vDVnzhw8++yzGDlyJACgffv2OHv2LJKTkzFu3DgYjUYAFSM+UVG3vkDz8/Orjf5UFhwcjODgYMXaLUdOjJoXbJNCjnNBpATmWJDStPI5riWq7vRcu3YNfn7Wg1H+/v6WkvXY2FgYjUakpqaiU6dOAIDS0lKkpaUhJSXF4+2tzN2cGC1VP3FlZVIb5liQJ2jpc1wrVN3pGTZsGF5++WU0adIEbdu2xZEjR7Bs2TI8/vjjACrCWomJiUhKSkLLli3RsmVLJCUlISQkBKNHj/Zy693D6iciZZhzLKoGFMw5Fgy9klz4Oa4+qu70vP7663jhhRcwdepU5OfnIzo6GpMmTcI//vEPyz5z587F9evXMXXqVBQUFKBbt27YuXMnQkNDvdjy6qQOpbP6ifTEU6EmZzkWBlTkWCTEGRnqIrfxc1x9uAwF5FuGwh5Xh9J3HLuIyRuy7G5fzb9ISQM8GWrKyLmEUWsPON3vo4l3MSRLsjCPLAK2F97kyKJ7NLUMhRawXJHIPk/fH8yxIE9jFau6qDq85evcGUo3P9ceDsOTr/NGqIk5FuQNrGJVD470KEhsueK6fbnV5mnggp2kdd64xs05Fva+agyoCK0xx4LkpraFN/WKnR4FiR0if+mrk+iVsstqKJ/D8KR13rjGzTOF20tkFMCZwom0jJ0eBUkZIq+aw8BheNI6XuNE5Gns9CjI2VB6ZVWnJOcwfIVyk4CMnEv4IvsCMnIucbp2DRFzf4TW8EfHxnVle02xuXJyXmfOrmFe40Sew0RmBZmH0qdsyIIBsDukblZ1SnJ7z9XLgp2cNVfbxNwfxTfK0XbBDkzsHYv5Q+Pcfk1PLwvg7BrmNU7kWRzpUZi9ckVHzDkMei51ZKm/Poi5P0wC8NaeXCRvtz9CI5Yn84icXcPJ20/wGifyMI70eIC5XHHdvly89NVJp/tXzmHQY6kjZ83VlyHtotC3VQPELdgBR1Olrt2bi1mDWiMowPW/1TyVRyRmde21e3N5jRN5GEd6PMTfz4DxPWNdytPRW6kjy/X1Z+MPZx12eICKEZ/1Gb+69TrxMeGIqBVkd7tcuXLOrmGg4v3Yw2ucxGJOmDQc6fEgRzkMesnTEYPl+vpz9vI1WfezxZw/c/lqqc3tct6Dcl2bvMbJEeaESceRHg/Tc56OWCxl1p+YiBBZ96vKXn5NZXLeg3Jdm7zGyR7mPbqGIz1e4Mk8ncqrV9evFQwYgD+ulLj9mu6siu3suVyZWH/Gdm+Kl7efdBjy8TNU7Cf12nOUX2MWWsMfu2b1Q80gf9ffRCXOrmGg4v0Igu2qNV7j5AjzHl3HTo+XmPN0lGRr6LMyV4dB3RlSFfNchgH1JyjADxN7x+KtPbl295nYOxa7/vNfydeemPya4hvl6JmyC0l/aSfLSI+Ya3hi71is2ZPLa5wk8/TUC1rC8JZGiRnOd2UY1J0hVSnPZRhQf+YPjcOkPrGo+j3vZwAm9YlFpybhLl17YvNiLl8tlTUs4Owanj80jtc4uYR5j64zCIKzmgntKyoqQlhYGAoLC1GnTh1vN8et0JH5+b1Sdjn96xa4NYyePm+A09dwdlxHx3L1ue6eC/I9pWUmrM/4FWcvX0NMRAjGdm8Kfz+D02u6bs1AvPHInbirmXWFY0bOJYxae0DUa0u5H8Rydg3zGiepxF7TH028S/MjPVK/vxneUhk5svHFDOebSRkGdWdI1dXneiIMSOoSFOCHCb2bWT2WkXPJ6TX95/WbeOTtH6rdL2Lya8yUCAs4u4Z5jZNUzHt0naTw1qlTp7Bw4UIMHDgQzZs3R1RUFO644w6MGzcOGzduRElJiVLt1AW5svFdGdIU8xx3hlQ5HEvukHJdVL1fzPk1Sr0ekadVvqarjgkyJ8wxUZ2eI0eOICEhAR06dMCePXvQpUsXJCYm4qWXXsKYMWMgCAKee+45REdHIyUlhZ0fF4iZwVXsQoiulLmKeY47peQsQyd3SLkubN0v5vyaiFqBsr8ekTcw79E1osJbI0aMwJw5c/Dxxx8jIsL+cFlGRgaWL1+OV155BX//+99la6QeyJmNL2U4X8owqDtDqhyOJVeVmwSYTALq1gzEn9dvinqOrftlSLsoDGgdibuSv3M4QSGvQ/IVelymyF2iOj1nzpxBUJD9qdvNunfvju7du6O01PYHCtknZ/hH7OruUodB3SklZxk6ucLZtAvOVL1fggL8kPSXdpiyIQsAr0PyfcwJk0ZUeEtMh8ed/Un+8I+Y1atdGQZ1Z0iVw7EkhZhpF5yxdb/wOiTSL0kl61evXsXGjRuxf/9+5OXlwWAwIDIyEj179sSoUaNQq1YtJduqGDWUrJtLup2Ff6SW0vrijMxEYqZdcDaK6ex+4XVI5Pukfn+L7vScOHECCQkJuHbtGvr27YvIyEgIgoD8/HykpaWhVq1a2LlzJ+LipFVJqIEaOj3Arb9sAdvD7vwrlPRC6tw6vF+I9EmxTk///v1hNBrx/vvvVwtflZaWYvz48bh48SJ2797tWsu9SC2dHoCr5hIBwBfZFzBjU7bT/R7v2RRfH8vj/UKkU4pNTvjDDz/g8OHDNvN1goKC8Pe//x1du3aV1lqqhtn4ROJz1xLijHju3jjeL0QkiuhOT3h4OM6cOWM3fPXzzz8jPDxctobpGbPxSe+kTHGgpftF63lGWn9/pH6iOz0TJ07EuHHj8PzzzyMhIQGRkZEwGAzIy8tDamoqkpKSkJiYqGBTiUgv9DjFgdZD21p/f+QbJFVvpaSk4NVXX7VUbgGAIAgwGo1ITEzE3LlzFWuoktSU00NEt+jli9JcxFD1w1grSdlaf3/kPYolMleWm5uLvLw8AIDRaERsbKz0lqoIOz1E6qX1kIiz8nwlVn5Xiq1/KwCaeX+kPh5ZZT02NtbnOzpE5Bu0lLNji5xL0HiTvVG5kV0aa+L9kTZI6vT89ttvWLVqVbXJCXv06IHJkyejcePGSrWTiEiT5FyCxlvsha/yCm9g+bdnRB1Dze+PtEPUMhQAkJ6ejjZt2mDLli3o0KEDHn30UYwZMwYdOnTA559/jrZt22Lfvn1KtpWISHPkXoLG08pNAhZtO2Gzyk5K7oRa3x9pi+iRnpkzZ+KJJ57A8uXL7W5PTEzEoUOHZGscEZHWSSnPVyNn4Tln1P7+SFtEj/QcO3YMkydPtrt90qRJOHbsmCyNIiLSC3N5PnCrmsnMF8rzpYSlfPH9kbaI7vRERUVh//79drdnZGQgKoolh0REUvnyyu9iw1Iz727lk++PtEV0eGv27NmYPHkyMjMzbU5O+Pbbb2PFihUKNpXMtF7CS6RHvroEjdjw3PQBLTB9QAufe3+kLZLm6fn444+xfPlyZGZmory8HADg7++P+Ph4PPPMM3j44YcVa6iSfGmeHr1M1kZEvsNcvQVwxXvyLI9MTnjz5k388ccfAID69esjMDBQektVxFc6PZzVlIjUin+QkTd4pNOjNb7Q6dHSrK1yYZiPSF14T5KneWRGZltycnIwceJE7Nq1S65DUiVambVVLvyrkkh9tD57Nvk+0dVbzly5cgVpaWlyHY6q0MKsrXIxh/mqdgLzCm9gyoYs7Dh20UstIyIiNRM90vPaa6853H7hwgW3G0P2+fqsrXJxNvurAcCibSeQEGfksDoREVkR3elJTExEVFQUgoKCbG4vLS2VrVFUna/P2ioXhvmIiMhVosNbMTExWL58OXJzc23+fPXVV0q2U/d8fdZWuTDMR0RErhLd6YmPj0dmZqbd7QaDASwEU5Yvz9oqF4b5iIjIVaLDWy+++CKuXbtmd3tcXBxyc3NlaRTZ56uztsqFYT7SGpZ5E3mO6E5PXFycw+2BgYGIiYlxu0HknJ7LQs1hvikbsmCA7dlf9RDmI23g1AtEnuVWyfrixYvx559/ytQUInEY5iMt4NQLRJ7n1ozMderUQXZ2Npo1ayZnmzzOF2ZkpuoYFiBfxRnWieTh0RmZmbhM3qTnMB/5Nk69QOQdss3ITERE4nDqBSLvcGuk58SJE4iOjparLUREusCpF4i8w61OT+PGjeVqB6mML+TL+EIbiWzh1AtE3iHbKutHjx7FnXfeifLycrkOSV7iC2W0vtBGIns49QKRd8ia08PEZt/nC2W0vtBGImc49QKR54ke6XnggQccbi8sLITBwL9KfJkvrGDuC20kEkvvM6wTeZroTs+2bduQkJCAyMhIm9sZ1vJ9vlBG62obq+b/xMeEI/NsgSa/aJjr5Fs49QKR54ju9LRp0wYPPvggJkyYYHN7dnY2vvzyS9kaZnbhwgXMmzcPX3/9Na5fv45WrVrhnXfeQXx8PICKkNqiRYuwZs0aFBQUoFu3bnjjjTfQtm1b2duidb5QRutKG23l//gZAFOl4SKt5AMx14mIyD5Jq6xnZWXZ3R4cHIwmTZrI0iizgoIC9OzZE4GBgfj6669x4sQJvPLKK6hbt65lnyVLlmDZsmVYuXIlDh06BKPRiISEBBQXF8vaFj3whTJaqW20l/9jqhIf00I+EHOdiIgcE70MRUlJCcrLyxESEqJ0myyeffZZ7Nu3D3v37rW5XRAEREdHIzExEfPmzbO0MzIyEikpKZg0aZKo1+EyFBXMU+M7K6P15tT4UtoIwOFU/46e6+j9VQ4fRdQMwn/+W4zzBdcQExGCsd2bIijA83N+yrWsgZTQGMNoRORtii1DERwc7FbDXLF161YMHjwYDz30ENLS0tCwYUNMnToVEydOBADk5uYiLy8PgwYNsmpn3759sX//frudnpKSEpSUlFh+LyoqUvaN+AhfKKOV0saMnEuiOzyAuJwlW+Gjyl7efhITe8di/tA40a8rBznysaSExhhGIyJfJOpP0qtXr0o6qNT97fnll1+watUqtGzZEt988w0mT56Mp59+Gh988AEAIC8vDwCqJVdHRkZattmSnJyMsLAwyw8nWbzFF8poxbbR1dwje8+zFz6qzCQAb+3JRfL2Ey69tqvczceSEhpjGI2IfJWokZ4WLVrgqaeewvjx4+0uOyEIAr799lssW7YMffr0wfz5891unMlkQufOnZGUlAQA6NSpE44fP45Vq1bh0UcftexXtVReEASH5fPz58/HM888Y/m9qKiIHZ9KfKGMVkwbXc09svU8R6Xytqzdm4tZg1p7LNRVv7a4kVip763qNAD4//93d8oAhsaIyBtEdXq+//57PP/881i0aBE6duyIzp07Izo6GjVq1EBBQQFOnDiBjIwMBAYGYv78+XjyySdlaVxUVBTi4qzDBG3atMFnn30GADAaKz6E8/LyEBV1awQiPz/fbmk9UBEC80a4zpf4QhmtszY6m+q/KkdT/zsLH1VlEoD1Gb9iQu9mop/jqh3HLmLh1uMO93HnvVUOjeH//1/MvnKE0YiI5CTqz9Dbb78d//73v5GTk4ORI0fi999/x6effoq1a9fi+++/R8OGDbF27Vr8+uuvmDJlCvz9/WVpXM+ePXHq1Cmrx06fPo2YmBgAQGxsLIxGI1JTUy3bS0tLkZaWhh49esjSBvJd5vwf4Fa+jz3OcpZcCZWdvXxN8nOkMoea8opK7O4j13vLL77h0TAaEZHcJK291ahRI8ycORMzZ85Uqj1WZs6ciR49eiApKQkPP/wwDh48iDVr1mDNmjUAKsJaiYmJSEpKQsuWLdGyZUskJSUhJCQEo0eP9kgbSd3M+T/O5ukxOhlpcCVUFhOhbKWj2JCbXO9NyjlwN4zGUBcRKUG2BUeV0KVLF2zZsgXz58/Hiy++iNjYWKxYsQKPPPKIZZ+5c+fi+vXrmDp1qmVywp07dyI0NNSLLSc1sZX/I3VGZqmhMj8DMLZ7U7fa7SzvRWzI7V9/7YCeLevb3S51xW+x+1Ztv0kQVD/jNxFpm+h5erSM8/SQGObQDACnHZ9JfdwrWxeT9/JF9gXM2JTt9FivjuyI+zs2dPp6tt6buYtVuSpOzL4AqrW/bs1A/Hn9piztJSICpH9/e34WNSIfZa9UvjI/gzwdHjF5L3KGpaRMVeBsXwA22y+mwyO2vURErlB1eItIDaqGadLm9LeExpzNyCy1NFtK3ovUsJQzQ9pFYUDrSKzP+BVnLzueYdrelAFAxSzYrgwfS20vEZFU7PQQOeAozGQOwfS+/TbJz7WXVCx1ZmU5Z9C21d6303PtttfWlAFSZ8F2p71ERFK5FN7au3cvxowZg+7du+PChQsAgPXr1yM9PV3WxhF5kzvl1a4+V2pJuFwzaMtVSi62/XVrBrrVXiIiV0ge6fnss88wduxYPPLIIzhy5IhlDavi4mIkJSVh+/btsjeSyNPcKa9257mu5Om4O4O2nKXk9WuJm/Tz9ZGdEBDgxxmZicijJI/0/POf/8Tq1auxdu1aBAbe+mutR48eyMrKkrVxRFKUmwRk5FzCF9kXkJFzCeUm1wsTpc5SXLkN6/bluvRc4Fb5uL2vfwMqQmRS8l6cnRdX36vdBorg518RGru/Y0N0b16PHR4i8gjJIz2nTp1Cnz59qj1ep04d/Pnnn3K0iUgyuZc2cGXmYWcrsIt5DfMs0pM32P4DQkD1vBdH7x2oXjpe9by4O8tyZX9csT8ztCv7ERHJSfJIT1RUFH7++edqj6enp6NZM+XXGSKqSomlDaSGmcSswO7qazji6L1P3pCFyR4ufVdidmciIrlI7vRMmjQJM2bMwA8//ACDwYDff/8dH374IWbPno2pU6cq0UYiu5zlowAVIx1VQzrOQj7xMeFwFnHxM1TsJ3UFdkchKvOxHD3X/H7EvHdbqp4XMe/VAKCs3OQ0ZKhEeI6ISC6Sw1tz585FYWEh+vfvjxs3bqBPnz4IDg7G7NmzMX36dCXaSGSX1BJvQFwoLPNsAZylBJmEiv0AxyuPV+asNFvOFc8dqXocZ+9VADD23YNOQ4bm8JxcZfRERHKSNNJTXl6OtLQ0zJo1C3/88QcOHjyIAwcO4H//+x9eeuklpdpIZJfUfBSxoTAlVh4HnJdmK/W6ch1HTMhQrjJ6IiK5SRrp8ff3x+DBg3Hy5ElERESgc+fOSrWLSBQpOSRSSrOVyE154d42GN8z1uEoR/3a4kq+69cOhp/B/dESqbk1YkvY3S2jJyJSguTwVvv27fHLL78gNjZWifYQSSJlKQYpoSMlVh531uGxNEIMAejaTNrK77baJKb9tpooZjV0WzM2ExF5k+RE5pdffhmzZ8/Gl19+iYsXL6KoqMjqh8iTzDkkQPUpYqrmkEgJHUk5rpR9nfnjqsiS76slol5XTJscHccROcJrRESeJLnTM2TIEBw9ehTDhw9Ho0aNEB4ejvDwcNStWxfh4eFKtJHIIbE5JFJDVnKuPC42j0XONq4ecydWu9l+OdpKRKQWBkEQJI2Mp6WlOdzet29ftxrkDUVFRQgLC0NhYSHq1Knj7eaQixytaF5uEnAg5xKmbczCn9dv2ny+OeSTPm+A1ahM1ePGx4RbVlm39Tru5LGUlpnQ+oWvHVZT+RmA/7x0j+jV3KW239XzRETkaVK/vyXn9Phip4b0wV4OiZiZkh2FoSofd8exi+i7dLfdcnd381iklMpXfh1Hryu1/T1b1sfiB9tLmhWaiMgXSO70AMCff/6Jd955BydPnoTBYEBcXBwef/xxhIWFyd0+IreYS9SdDWcaRSxZYe9Y5jJuOcqx5VwSoipPtJ+ISM0k5/QcPnwYzZs3x/Lly3H58mX88ccfWLZsGZo3b84FR0lVxMyUXLdmID58ohvS5w2w+4VfbhKw78wfePazn+yWuwsA/r7lJ5SWmdxqs9hVysXuZyZl5mops0ITEfkSySM9M2fOxPDhw7F27VoEBFQ8vaysDE888QQSExOxZ88e2RtJ5ApnJeoA8Of1m/AzGOyGaqQsInr56k3clfwdkv7SzvURE7ERI4mRJTlnehZbsk5EpDYujfTMmzfP0uEBgICAAMydOxeHDx+WtXFE7nA3VOTKIqKXr5a6vMgpoNwq5UrM9MySdSLyNZI7PXXq1MG5c+eqPX7+/HmEhobK0igiObgzq7LURUSrcjX8o1R4S8q54ErpRKRVksNbf/vb3zBhwgT861//Qo8ePWAwGJCeno45c+Zg1KhRSrSRyCVSZlWuWtZtEgRZFvOUHP5RKLylxAzT8THhyMi5xGUmiMhnSO70/Otf/4LBYMCjjz6KsrIyAEBgYCCmTJmCxYsXy95AIleJXfE79URetbydujUD3X59V8I/SoW3zOdCbBm6s32Hd4hyWPpORKRGksNbQUFBePXVV1FQUIDs7GwcOXIEly9fxvLlyxEcLG3InUhpzmZKBmAzb8fexHxSuBL+8ZXQ0lt7cp2uVE9EpDaSR3oKCwtRXl6OiIgItG/f3vL45cuXERAQwBmNSXXsrfgNAL1Sdrmct+OInwGIjwmXPEOz1DCUWM7K0IGKkvsBrSPh72dwuq8tYldgJyLyFsmdnpEjR2LYsGGYOnWq1eOffPIJtm7diu3bt8vWOCK52JqxOCPnkst5O86YBGDV9znYdOicpBCQ2JCc1A6FmPJ9c8n9uO4x3slnIiJSmOTw1g8//ID+/ftXe7xfv3744YcfZGkUkSeIzbmpmt8jNt9n+benXQoBybV4aWVi3+vlq6VY/u0Zycd39fWIiDxJ8khPSUmJJYG5sps3b+L69euyNIrIE8Tmxbwx+k74+RmsKrseedu1Dr7YEJC9kJyrIaP6tT2bb+ftnCMiIlskj/R06dIFa9asqfb46tWrER8fL0ujiDzBnD9jrxthQEU46q7m9dC9eT3c37Ehujevh7ua1XP4PGeqzn7sER5aMcJ8zqTmHBEReYLkkZ6XX34Zd999N44ePYqBAwcCAL777jscOnQIO3fulL2BREpxNX/GWfm3WI5CQLaWv3CnJPyPq9JK3F3FFdiJSM0kj/T07NkTGRkZaNy4MT755BNs27YNLVq0wI8//ojevXsr0UYixSiRPyOWvRCQveUv3CkJZ7iJiAgwCIKg+6WSi4qKEBYWhsLCQpbc+zCp5eGuPrfcJKBXyi6HFU5+BkAQbEeVzGXn6fMGVHsNZ8d29FxHzMe1VwovF1fbR0TkCqnf36LDWyaTCSaTyWqh0f/+979YvXo1rl69iuHDh6NXr16utZrITe6Gg2yVtNsjpvzbvOyW1LJzKauhSykJlysk5wxL1olIzUSHtyZMmGA1N09xcTG6dOmCN954A9988w369+/POXrIK5QIBzkithz78Z5NJYfNtLLCudrbR0T6JHqkZ9++fVi5cqXl9w8++ABlZWU4c+YMwsLCMG/ePCxduhRDhw5VpKFEtjhaDV2pGYLFrnA+sHUknrs3TlLITWxpudQSdDEzMsuJOUREpEaiOz0XLlxAy5YtLb9/9913ePDBBxEWFgYAGDduHN577z35W0jkgFzhIEc5PbZWYBfFIC1sZmmwm/vZei9iQnJycHWZDCIiTxDd6alRo4bV5IMHDhzA0qVLrbZfuXJF3tYROSFHOMhRPhAAl1dgl7oSOiC+tNzefvbey9B2RsltkcqdZTKIiDxBdE5Phw4dsH79egDA3r178d///hcDBgywbM/JyUF0dLT8LSRywN1VyR3lA03ekIXJbqzA7ulV1h29l3f2/SrquBG1gkTtZ4snyvyJiNwheqTnhRdewNChQ/HJJ5/g4sWLGD9+PKKibn24bdmyBT179lSkkUT2uLoqeblJwIGcS3j2s5/s5gO5yp0QT3xMOPwMt6q/bDGv4F6Zs9wm8/OcldGnzemPQ7mXMW1jlujOHQBE1ApE2pz+CAqQPPUXEZHHiO709O/fH5mZmUhNTYXRaMRDDz1ktb1jx47o2rWr7A0kcsSVWZVthYDk4m6IJ/NsgcMOD1DRIco8W2CVKyRXGX1QgB/8/AySOjxAxQrtVdtERKQ2kpahiIuLQ1xcnM1tTz75pCwNIpLKPKty1Y6M0cY8PeYQkFwT9NWtGWjVQbD1mlK4mqMkpYz+62N5Ds+Tq+XmLFMnIrWTvPYWkRqJWZXcUQjIVVVXYHdnJXTA9Zwesc9LiDM6LaN3tdycZepEpHbs9JBmOCsPl7Ns25wDc1fzerJWKplzlBy109Yq5lJym5ydJ2fHcnRsIiI1Y9Yh6YaU8IvBzv9X/l2J0mx/PwOGd3AcGhveIcruyu+V22cmtb2OjlUVy9SJyJew00O6ISX8YgyrgdVj7sRqD6/AXm4SsPWo42Uzth69iHIb2c5yrhhv71hV+zUsUyciX8LwFumGmLBN3ZqBeOORO3FXs1thK2e5QnISE4JzNMO0mNwmsYa0i8KA1pFYn/Erzl6+hpiIEIzuFoPs838iv/hGxXIchopJGDNyLtnMofLUeSMiEkNypyc8PBwGQ/UPLoPBgBo1aqBFixYYP348HnvsMVkaSCQXMeXtix9sj54t6ld7nqdKseWYYVqu9toq7X87PRcLhsUhOMAPsz89andVe3dXvSciUoLkTs8//vEPvPzyy7jnnnvQtWtXCIKAQ4cOYceOHZg2bRpyc3MxZcoUlJWVYeLEiUq0mchlUsrbvcHdGablYq+03zxTtS3mVe2f7BOLNXtybT53yoYshsOIyGsMgiB29cQKDz74IBISEjB58mSrx9966y3s3LkTn332GV5//XWsWbMGP/30k6yNVUpRURHCwsJQWFiIOnXqeLs55KLSMpNVKGZs96Z2ZwhWKvTi7nHLTQJ6pexyWoWVPm+Ay+2t3MbKIaoGoTUQHxPu0ozMlTmaUVqO9hMRmUn9/pbc6alduzays7PRokULq8d//vlndOzYEVeuXEFOTg7uuOMOXL16VVrrvYSdHt+XvP0E1u7Ntfqy9TMAE3vHYv5Q2xNqyk2ukM6OYxftjqYAwGo3RkqczUbtbAkMuXw08S7O3kxEbpP6/S25eisiIgLbtm2r9vi2bdsQEVExT8fVq1cRGhoq9dBELknefgJv7cmt9mVtEoC39uQiefsJxdvgaLHPKRuysOOY44osT7DXxso80eEBOHszEXmH5JyeF154AVOmTMHu3bvRtWtXGAwGHDx4ENu3b8fq1asBAKmpqejbt6/sjSWqqrTMhLV7cx3us3ZvLmYNaq3YYpjOFvs0AFi07QQS4oxOQzrmY9kj5Vhi2+gNnL2ZiLxB8rfAxIkTkZaWhlq1amHz5s349NNPERISgrS0NEyYMAEAMGvWLHz88ceyN5aoqvUZv4paoHN9xq9Wj5WbBGTkXMIX2ReQkXPJ5rw3YjkrMxdwq8zck8cyKzcJWLcvV5EFVm3xM9if1NAA2zNKExF5gkvz9PTs2RM9e/aUuy1Ekp29fE3yfnKXU8tRZq7EsQBlV5S3xYCKPKo1e3JFr3pPROQpLnV6TCYTfv75Z+Tn58NkMllt69OnjywNIxIjJiJE0n6OSrFdLaeWs8xczmPJvaK8M/VqBeHlv7TDkHZR6NQkXLXTAhCRfknu9Bw4cACjR4/G2bNnUbXwy2AwoLy8XLbGaRlnq5XH2O5N8fL2kw5DXH6Giv3kzL2pTMpin87Ex4Q7raDyM1Ts54inc3giagUiY/5AS96UrdmcHU0hoBTeZ0RUmeROz+TJk9G5c2d89dVXiIqKsjk7MznG2WrlExTgh4m9Y/HWHvvJzBN7xyIowA8ZOZdE58tIKacWM9Oz2JBO5tkCUTlKmWcLPLaifGX23l/SX9pbdWgczebsqWuc9xkRVSX5z64zZ84gKSkJbdq0Qd26dREWFmb1o6Tk5GQYDAYkJiZaHhMEAQsXLkR0dDRq1qyJfv364fjx44q2wx2+UNrsa+YPjcOkPrHVFsP0MwCT+tyap0fufJnK5FrsU642KlES/njPpqLenxqucTW0gYjUR/JIT7du3fDzzz9Xm5xQaYcOHcKaNWtwxx13WD2+ZMkSLFu2DOvWrUOrVq3wz3/+EwkJCTh16pTq5gpSKrxCFR2fWYNaOwynKL3EgxyLfcrVRiVKwhPijHju3jiH708N17ga2kBE6iS50/PUU09h1qxZyMvLQ/v27REYGGi1vWqnRA5XrlzBI488grVr1+Kf//yn5XFBELBixQo899xzeOCBBwAA77//PiIjI7Fx40ZMmjTJ5vFKSkpQUlJi+b2oqEj2NtvKJZBSjszZaqULCvDDhN7N7G6XM/fGHncX+5SrjWJWlBer8ms6e39quMbV0AYtY54U+TLJnZ4HH3wQAPD4449bHjMYDBAEQbFE5mnTpuHee+/F3XffbdXpyc3NRV5eHgYNGmR5LDg4GH379sX+/fvtdnqSk5OxaNEi2dtpZi+XYGg7o6jnc7ZaZciZe6MUudro6DhSSD0vSoYQxVJDG7SKeVLk6yTn9OTm5lb7+eWXXyz/ldumTZuQlZWF5OTkatvy8vIAAJGRkVaPR0ZGWrbZMn/+fBQWFlp+zp8/L1t7HeUSvLPvV1HH4Gy1ypEr90ZJcrXR3nEciagV5NZrqmGVeDW0QYuYJ0VaIHmkJyYmRol22HT+/HnMmDEDO3fuRI0a9j+gqlaQmUed7AkODkZwcLBs7TRzlksAVCTXCoLtv7zlCK+Qc3Lk3ihNrjaaj3Mg55LDldPN117anP7IPFvg8mt6IoToC23QGuZJkVaI6vRs3boV99xzDwIDA7F161aH+w4fPlyWhgFAZmYm8vPzER8fb3msvLwce/bswcqVK3Hq1CkAFSM+UVG3/hLNz8+vNvrjCWLKhM3lyGoNr+iFu7k3niBXG/39DOjZsj4WP9geU/5/9XZ7115QgJ9br6mGEKIa2qA1zJMirRDV6RkxYgTy8vLQoEEDjBgxwu5+cuf0DBw4ED/99JPVY4899hhat26NefPmoVmzZjAajUhNTUWnTp0AAKWlpUhLS0NKSops7RBLbI7A4z2b4utjeZytljzKHO5SeqZkT72O2tugJcyTIq0Q1empvNRE1WUnlBQaGop27dpZPVarVi3Uq1fP8nhiYiKSkpLQsmVLtGzZEklJSQgJCcHo0aM91k4zsTkCYkp/iZTgqdCeGkKIamiDVjBPirTCpbW31GTu3Lm4fv06pk6dioKCAnTr1g07d+70yhw9UnIJfCG8QtrkqWtPDde4GtqgBcyTIq0wCFUX0LLhtddeE33Ap59+2q0GeUNRURHCwsJQWFiIOnXquHUsc4UDYDuXQC0VQkREUvCzjdRI6ve3qE5PbGys1e//+9//cO3aNdStWxcA8OeffyIkJAQNGjRQpGxdaXJ2egDOZUFE2sTPNlIbRTo9lW3cuBFvvvkm3nnnHdx+++0AgFOnTmHixImYNGkSHnnkEdda7kVyd3oAzlrqCTzHJBWvGffxHJKaKN7pad68OT799FNLtZRZZmYm/vrXvyI31/5q12qlRKeHlMW/OEkqXjNE2iP1+1vyjMwXL17EzZvVJzgrLy/Hf//7X6mHI5KMM8OSVLxmiAhwodMzcOBATJw4EYcPH4Z5kOjw4cOYNGkS7r77btkbSNWVmwRk5FzCF9kXkJFzCeUm+4N1UvZVu3KTgH1n/sCzn/3kcNbrRdtO+PT71Du5r1kxM6Uv2nYCpWUmzdwrRGSb5JL1d999F+PGjUPXrl0tK6yXlZVh8ODBePvtt2VvIFmTMkSvpeF8W+/FFs4M69uUuGbFziZ8V/J3uHy1VLbXJSL1kZzTY3b69Gn85z//gSAIaNOmDVq1aiV32zzGV3J6zEP0Vf/BbJWMStlX7ey9F0deHdkR93dsqFibSH5KXbNfZF/AjE3Zkp/ni/cKkd4ontNj1qpVKwwfPhz333+/T3d4fIXYIfpykyBpX7Vz9F4c4cywvkXJa9bVa8HX7hUick5yeKu8vBzr1q3Dd999h/z8/GrLUuzatUu2xtEtUhb8w///v5h91R4CErOIa2WcGdY3ib2+1+3LxfiesbKu/O6IL90rROSc5JGeGTNmYMaMGSgvL0e7du3QoUMHqx9ShpQF/7S0OKCUNnIFbd8l9t/5pa9OolfKLknVVuZV14Fb14hUvnCvEJFzkkd6Nm3ahE8++QRDhw5Voj1khxIL/vlCCEhKG7mCtu+S8u9sLjOXkmtjb9X1iFqBuHy1+hQc7rSPiNRLcqcnKCgILVq0UKIt5IDUBf+0sjigmNBE3ZqBeOORO3FXs3oc4fFRUkJQAiqu4UXbTiAhzij639zWquvxMeHou3S3Ju4VInJOcnhr1qxZePXVV+Fi0Re5yNEQfdWwjpR91c7ZezEAWPxge/RsUd8n3g/ZJjUEVTWHTcrrdG9eD/d3bIjuzeshKMBPM/cKETknuWT9L3/5C3bv3o2IiAi0bdvWMleP2ebNm2VtoCf4Ssk6wHl6tPBeyD6x8zGZyTU1Aa8vIt+k+Npbjz32mMPt7733npTDqYIvdXoAaQv+aWlxQC29F7Kv3CRg3b5cvPTVSaf7fjTxLtmqqnh9EfkexTs9WuRrnR5P4BcAeVO5SUCvlF1Oc23S5w3gdakA3v/kK6R+f0tOZAYqlp34/vvvkZOTg9GjRyM0NBS///476tSpg9q1a7tySFIRDvWTt5lzfKZsyIIBsOr4MNdGWbz/Scskj/ScPXsWQ4YMwblz51BSUoLTp0+jWbNmSExMxI0bN7B69Wql2qoYjvTcoqXlK8j38QvYs3j/k69RfKRnxowZ6Ny5M44ePYp69W7F0v/yl7/giSeekHo4UhExSwE8+9lPCK0RyPJw8ghbZeYMtSjD2f3vyjQBRGojudOTnp6Offv2ISgoyOrxmJgYXLhwQbaGkeeJWfLhz+s38cjbP/CvbfIYc5k5KUvKUjf89yBfJXmeHpPJhPLy8mqP//bbbwgNDZWlUeQdUqbaN8+KK2U5ACJSLy0tX0Nkj+ROT0JCAlasWGH53WAw4MqVK1iwYAGXpvBxUqba5wrURNqixFI3RGojudOzfPlypKWlIS4uDjdu3MDo0aPRtGlTXLhwASkpKUq0kTzEvBSA2Gi9q7PiEpH6OLv/DahIIueSHOTLJHd6oqOjkZ2djdmzZ2PSpEno1KkTFi9ejCNHjqBBgwZKtJE8xNXVqDncTeT7tLR8DZE9nJwQLFmvSupSAHLOiktE3sVpAsiXeGRG5lOnTuH111/HyZMnYTAY0Lp1a0yfPh2tW7d2qdHepqVOj1wzqZabBBzIuYRpG7Pw5/WbNvfhrLgkF84ArC789yBfofg8PZ9++ilGjRqFzp07o3v37gCAAwcOoH379ti4cSMeeugh6a0mWcj5F5q/nwE9W9bH4gfbY8qGLACcFZeUwZEF9eE0AaRVkkd6mjVrhjFjxuDFF1+0enzBggVYv349fvnlF1kb6AlaGOlRciZVfimRUjgDMBG5Q/HwVkhICH788Ue0aNHC6vEzZ86gQ4cOuHbtmrQWq4Cvd3rMizPay8GRIwylxuFuNbaJxPPEdUukdXr/HFQ8vNWvXz/s3bu3WqcnPT0dvXv3lno4koEnZlJV23A3R598H2cAJnIPPwelk9zpGT58OObNm4fMzEzcddddACpyev79739j0aJF2Lp1q9W+pDy9zaRqLyRiniWaIRHfoLfrlkhO/Bx0jeROz9SpUwEAb775Jt58802b24CKmZptLVdB8tPTTKpcFFE79HTdEsmJn4Ouc2ntLTE/7PB4jp5mUpUSEiF109N160i5SUBGziV8kX0BGTmXuKwLOcXPQddJHukh9THPpDplQxYM0HZpOUMi2qGn69Ye5mSQK/g56DrRIz0//PADvv76a6vHPvjgA8TGxqJBgwZ48sknUVJSInsDSZwh7aKwasydMIZZhwKMYTU0FdtlSERb9HLd2mLOyaj6F7s5J2PHsYteahmpHT8HXSd6pGfhwoXo168f7rnnHgDATz/9hAkTJmD8+PFo06YNli5diujoaCxcuFCptpITQ9pFISHOqKnyxarlmPEx4YgKq4G8whs249nmMmeth0S0RIvXbWW2SooBMCeDXGYODfNzUDrRnZ7s7Gy89NJLlt83bdqEbt26Ye3atQCAxo0bY8GCBez0eJnaSsvdYW/of3iHKKzZk6vbkIgWaem6rczeNTyyS2OW65PLGBp2nejwVkFBASIjIy2/p6WlYciQIZbfu3TpgvPnz8vbOtItR0P/a/bk4sk+sboMiZDvcHQNL//2jKhjMCeD7NFzaNgdokd6IiMjkZubi8aNG6O0tBRZWVlYtGiRZXtxcTECAwMVaSTpi5hyzK1HLyJtTn9kni3QZEiEfJuza1gs5mSQI1oPDStBdKdnyJAhePbZZ5GSkoLPP/8cISEhVjMw//jjj2jevLkijSR9EVuOmXm2gEP/pErOrmFnmJNBYmk1NKwU0Z2ef/7zn3jggQfQt29f1K5dG++//z6CgoIs2999910MGjRIkUaSvrAck3ydlGuTORlEniO603Pbbbdh7969KCwsRO3ateHv72+1/d///jdq164tewNJf1iOSb5O7LU58+5W2HTonNWokJHz9BApRvLkhGFhYTYfj4jgMCzJg+WY5OvEXsPTB7TA9AEtNJOTofcVv0n9OCMzqQ7LMcnXSb2GtZCTwdmlyRdIXnuLyBNYjkm+Tk/XMGeXJl9hEARB96vbFRUVISwsDIWFhahTp463m0OVcLicfJ3Wr+Fyk4BeKbvsVquZQ3np8wYAgKbPBXme1O9vhrdI1ViOSb5O69ew2CkmVu76uVrSNsNf5GkMbxERkcvElucv//Y0w1/kdez0EBGRy9yZOsKcW7Fo2wmUm3SfaUEewE4PEelSuUlARs4lfJF9ARk5l/il6yJzeb6rmTmVF1clUhpzeohId1heLR9n5fliu5KcYZ08gSM9RKQrLK+Wn6Py/Jl3txR1DM6wTp7AkR4i0g1nq58bUJFfkhBnZCm1RPZW/AaATYfOc4Z1UgWO9BCRbogtr2Z+iWvM5fn3d2yI7s3rwd/PYAl/AaiW98MZ1snT2OkhIt0QmzfC/BJ56Wl2alI3hreISDfE5o0wv0R+9sJfHOEhT2Knh4h0Q+zq50rml2h9WQpHtD47NamfqsNbycnJ6NKlC0JDQ9GgQQOMGDECp06dstpHEAQsXLgQ0dHRqFmzJvr164fjx497qcVEpGbm/BJ7ZdQClM0v2XHsInql7MKotQcwY1M2Rq09gF4pu1gxRuQhqu70pKWlYdq0aThw4ABSU1NRVlaGQYMG4erVq5Z9lixZgmXLlmHlypU4dOgQjEYjEhISUFxc7MWWExFZY6k8kff51Crr//vf/9CgQQOkpaWhT58+EAQB0dHRSExMxLx58wAAJSUliIyMREpKCiZNmiTquFxlnUgfpKwILudoj7del0jrpH5/q3qkp6rCwkIAQERERbw9NzcXeXl5GDRokGWf4OBg9O3bF/v377d7nJKSEhQVFVn9EJH2eatknaXyROrgM50eQRDwzDPPoFevXmjXrh0AIC8vDwAQGRlptW9kZKRlmy3JyckICwuz/DRu3Fi5hhORanirZJ2l8kTq4DOdnunTp+PHH3/ERx99VG2bwWA9HCwIQrXHKps/fz4KCwstP+fPn5e9vUSkPt4qWWepPJE6+ETJ+lNPPYWtW7diz549aNSokeVxo9EIoGLEJyrq1uRW+fn51UZ/KgsODkZwcLByDSYiVfJWyboaSuWJvEFtUzSoeqRHEARMnz4dmzdvxq5duxAbG2u1PTY2FkajEampqZbHSktLkZaWhh49eni6uUSkct5aEoFLMZAeqXGKBlV3eqZNm4YNGzZg48aNCA0NRV5eHvLy8nD9+nUAFWGtxMREJCUlYcuWLTh27BjGjx+PkJAQjB492sutJyI18taSCFyKgfRErVM0qLpk3V5eznvvvYfx48cDqBgNWrRoEd566y0UFBSgW7dueOONNyzJzmKwZN2z1DbcSfpUWmbC+oxfcfbyNcREhGBs96YIClD+70A9X/96fu964skpGqR+f6u60+Mp7PR4zo5jF7Fo2wmrmyEqrAYWDIvjX7rkMbwOPY/nXD8yci5h1NoDTvf7aOJdbi9Loul5esi3qXW4k/SF16Hn8Zzri5qnaGCnhzyi3CRg0bYTNitXzI8t2nYC5SbdDzySgngdeh7PeYVyk4CMnEv4IvsCMnIuafr9qnmKBp8oWSffJ2VGWq7CTErhdeh5POf6C+2peYoGjvSQR6h5uJP0g9eh5+n9nOsxtKfmKRrY6SGPUPNwJ+lH/VriJiUVux85p+d7X8+hPbVO0cDwlg/QQpmnmoc7SUfE3jZeur3UeK+72yY93/t6D+0NaReFhDijqq5pdnpUTiuxYPNw55QNWTAAVh9+3h7uJP3440qJrPvJSY33uhxt0vO9r/fQHlDx76+mDh3DWyqmtViwWoc7ST/UGmpR470uZ5v0eu+r9XrTM470qFC5ScCBnEt49rOf7MaCDaiIBSfEGRX5C0nOYfaqx0qb0x+ZZwtUM9xJ+qHGUIuzvA9X73V37mEl2qTGUIfS1Hi96R07PSpjazjZFiVjwXIOszs61v0dG8rWZiIx1BhqUSLvw917WKlcFLWFOpSmxutN7xjeUhF7w8mOyB0LlnNIW41D9kRqC7XInfchx33HXBT5qO160zuO9KiEo+FkR+SMBcs5pK3UkD2RHNQUapEz70Ou+465KPJS0/Wmd+z0qISz4eSqlIgFyzmkrfdSTVI/tYRa4mPCEVErCJevltrcLuVel+u+Yy6K/NRyvekdw1sqIXWYWID8sWA5h7Q5PE7k3I5jF9F36W67HR5A2r0u132n5hl1idzBTo9KqGGYWM4hbQ6PEznmSg6fM3Led8xFIS1ieEslnA0nV6VEToycQ9ocHic1UsuMx1Jy+KTc63Lfd8xFIa3hSI9KOBpOtqVybN4TbZA6pM3hcVKbHccuolfKLoxaewAzNmVj1NoD6JWyyytVhFJy+KTc60rcd+ZclPs7NkT35vV4z5JPY6dHRewNJzsid06MnEPaHB4ntVDb9Amu3Ldin8P7jsg+hrdUxjycvG5fLl766qTT/ZXIiZFzSJvD4+Rtapw+wZX7VspzeN8R2cZOjwr5+xkwvmcs3k7P9VpOjJzllSzVJCnkzrtR4/QJUnL4XL3Xed8RVcdOj0px+nLSIyVWGlfj9AmO7u/KeK8TyYs5PSrG2DzpiVJ5N2qdPkFMDh/vdSJ5caRH5RibJz1wJ+/GWThMzdMnDGkXhQGtI7E+41ecvXwNjcNrorWxDi5fK+W9TqQAdnp8AGPzpHWu5t2ICYepOVTsqP2854nkx/AWEXmdK3k3UsJhagwVq62MnkgPONJDRF4nNe/GlXCYmkLFaiyjJ9IDdnqIyOvMeTeOQlxRlfJuXA2HqSVUrMYyeiI9YHiLiLzO38+A4R0ch5iGd4iyjHqosQxdCl9vP5GvYqeHiLyu3CRg61HHOSxbj15EuakiIKTWMnSxfL39RL6KnR4i8joxC3BWXnTTHA6zl+1igHU4TG18vf1aUm4SkJFzCV9kX0BGziVLx5q0iTk9ROR1UsM9ai5DF8PX268VSswATurGkR4i8jpXwj1qLEOXwtfb7+s4ZYA+caSHiLxOyqzJVWdgTpvTH5lnC7xehu4KNZXR6wmnDNAvdnqIyOvEhntST+TZDUfc37GhJ5ssG7WU0esJpwzQL4a3iEgVnIV7ADAcQbLglAH6xZEeIlINe+EeAOiVsovhCJIFpwzQL3Z6PMTZStDOthPpha1wT0bOJYYjSDZScshIW9jp8QBnZZEsmyRyjOEIkhOnDNAv5vQozFlZZPL2E8xTIHKC4QiSG6cM0CeO9CjIWVkkAKzdm+uxPAWG0MhXiQ1HxMeEIyPnkuzXOO8dbeKUAfrDTo+CxEyt72jGcznzFBhCI19mDkdM3pBlc7uAigVJ+y7dLfs1zntH2zhlgL4wvKUgufIL3D0OZx4lPXhrT67s1zjvHSJtYadHQXLlF7hzHDEhtkXbTnCRPVI183UslTvXOO8dIu1hp0dBzlZSBgA/AxRdaVnKzKNSSFmZmKsYk7vEhIrtcfUaV+reISLvYU6PgsSURU7sHYs1e3IVK5tUotRXSo4D8yFIDnKEiqUeg2XyRNrDkR6FOSuLnD80TtGySblLfaXkODAfguQiR6hY6jFYJk+kPRzp8QBnZZFVt9evFQwYgD+ulCAj55JbJZRyzjwqZWVi/P//c9kAkoOz69gRV2fX5ay9RNrDTo+HOCuLNG/fcewiZn96VLZwkJwzj0rNceCyASQXZ9exYOP/zb8DroWJOWsvkfYwvKUiSoWD5Jp5VEqOA/MhSG6OruPVY+7EagXCxJy1l0hbONKjElJCR678ZSnHzKNK5DgwH4KkcHYdKzG7LmftJdIOdnpUQkroyNVwkLszj0rNcWA+BCnB0XWs1Oy6nLWXSBsY3lIJXwgHmXMcgOpzC1XNcZCyLxERkSew06MSvlIeKyXHgfkQRESkJgxvqYQvlcdKyXFgPgSpVWmZCeszfsXZy9cQExGCsd2bIiiAfwcSaZlBEATdrwlQVFSEsLAwFBYWok6dOl5rh7l6C7BdHsvRESJ5JG8/gbV7c1F5RRQ/Q8UM6fOHxnmvYUQkidTvb/5ZoyIMBxEpL3n7Cby1x7rDAwAmoWKl9uTt0hc2JSLfwJEeqGekx6zcJDAcRKSA0jITWr/wdbUOT2V+BuA/L93DUBeRD5D6/c2cHhVieSyRMtZn/OqwwwNUjPisz/gVE3o380yjiMhjNPOnzJtvvonY2FjUqFED8fHx2Lt3r7ebREQqc/byNVn3IyLfoolOz8cff4zExEQ899xzOHLkCHr37o177rkH586d83bTiEhFYiJCZN2PiHyLJnJ6unXrhjvvvBOrVq2yPNamTRuMGDECycnJ1fYvKSlBSUmJ5feioiI0btxYNTk9RKQM5vQQaYvuqrdKS0uRmZmJQYMGWT0+aNAg7N+/3+ZzkpOTERYWZvlp3LixJ5pKRF4WFOCHib1jHe4zsXcsOzxEGuXzd/Yff/yB8vJyREZGWj0eGRmJvLw8m8+ZP38+CgsLLT/nz5/3RFOJSAXmD43DpD6xqFoQ6WcAJvXhPD1EWqaZ6i2DwfoTTBCEao+ZBQcHIzg42BPNIiIVmj80DrMGteaMzEQ64/Odnvr168Pf37/aqE5+fn610R8iIrOgAD+WpRPpjM//WRMUFIT4+HikpqZaPZ6amooePXp4qVVERESkNj4/0gMAzzzzDMaOHYvOnTuje/fuWLNmDc6dO4fJkyd7u2lERESkEpro9Pztb3/DpUuX8OKLL+LixYto164dtm/fjpiYGG83jYiIiFRCE/P0uEtta28RERGRc7qbp4eIiIhIDHZ6iIiISBfY6SEiIiJdYKeHiIiIdIGdHiIiItIFTZSsu8tcwFZUVOTllhAREZFY5u9tsYXo7PQAKC4uBgCutk5EROSDiouLERYW5nQ/ztMDwGQy4ffff0doaKjdRUq9raioCI0bN8b58+c5l5ADPE/i8DyJw/MkDs+TODxPzkk9R4IgoLi4GNHR0fDzc56xw5EeAH5+fmjUqJG3myFKnTp1eLOIwPMkDs+TODxP4vA8icPz5JyUcyRmhMeMicxERESkC+z0EBERkS6w0+MjgoODsWDBAgQHB3u7KarG8yQOz5M4PE/i8DyJw/PknNLniInMREREpAsc6SEiIiJdYKeHiIiIdIGdHiIiItIFdnqIiIhIF9jpUZHk5GR06dIFoaGhaNCgAUaMGIFTp05Z7SMIAhYuXIjo6GjUrFkT/fr1w/Hjx73UYnVITk6GwWBAYmKi5TGepwoXLlzAmDFjUK9ePYSEhKBjx47IzMy0bOd5AsrKyvD8888jNjYWNWvWRLNmzfDiiy/CZDJZ9tHjedqzZw+GDRuG6OhoGAwGfP7551bbxZyTkpISPPXUU6hfvz5q1aqF4cOH47fffvPgu1Ceo/N08+ZNzJs3D+3bt0etWrUQHR2NRx99FL///rvVMfR+nqqaNGkSDAYDVqxYYfW4HOeJnR4VSUtLw7Rp03DgwAGkpqairKwMgwYNwtWrVy37LFmyBMuWLcPKlStx6NAhGI1GJCQkWNYP05tDhw5hzZo1uOOOO6we53kCCgoK0LNnTwQGBuLrr7/GiRMn8Morr6Bu3bqWfXiegJSUFKxevRorV67EyZMnsWTJEixduhSvv/66ZR89nqerV6+iQ4cOWLlypc3tYs5JYmIitmzZgk2bNiE9PR1XrlzBfffdh/Lyck+9DcU5Ok/Xrl1DVlYWXnjhBWRlZWHz5s04ffo0hg8fbrWf3s9TZZ9//jl++OEHREdHV9smy3kSSLXy8/MFAEJaWpogCIJgMpkEo9EoLF682LLPjRs3hLCwMGH16tXeaqbXFBcXCy1bthRSU1OFvn37CjNmzBAEgefJbN68eUKvXr3sbud5qnDvvfcKjz/+uNVjDzzwgDBmzBhBEHieBEEQAAhbtmyx/C7mnPz5559CYGCgsGnTJss+Fy5cEPz8/IQdO3Z4rO2eVPU82XLw4EEBgHD27FlBEHieKvvtt9+Ehg0bCseOHRNiYmKE5cuXW7bJdZ440qNihYWFAICIiAgAQG5uLvLy8jBo0CDLPsHBwejbty/279/vlTZ607Rp03Dvvffi7rvvtnqc56nC1q1b0blzZzz00ENo0KABOnXqhLVr11q28zxV6NWrF7777jucPn0aAHD06FGkp6dj6NChAHiebBFzTjIzM3Hz5k2rfaKjo9GuXTvdnjeg4nPdYDBYRlx5niqYTCaMHTsWc+bMQdu2battl+s8ccFRlRIEAc888wx69eqFdu3aAQDy8vIAAJGRkVb7RkZG4uzZsx5vozdt2rQJWVlZOHToULVtPE8VfvnlF6xatQrPPPMM/v73v+PgwYN4+umnERwcjEcffZTn6f/NmzcPhYWFaN26Nfz9/VFeXo6XX34Zo0aNAsDryRYx5yQvLw9BQUEIDw+vto/5+Xpz48YNPPvssxg9erRlMU2epwopKSkICAjA008/bXO7XOeJnR6Vmj59On788Uekp6dX22YwGKx+FwSh2mNadv78ecyYMQM7d+5EjRo17O6n9/NkMpnQuXNnJCUlAQA6deqE48ePY9WqVXj00Uct++n9PH388cfYsGEDNm7ciLZt2yI7OxuJiYmIjo7GuHHjLPvp/TzZ4so50et5u3nzJkaOHAmTyYQ333zT6f56Ok+ZmZl49dVXkZWVJfk9Sz1PDG+p0FNPPYWtW7di9+7daNSokeVxo9EIANV6tfn5+dX+4tKyzMxM5OfnIz4+HgEBAQgICEBaWhpee+01BAQEWM6F3s9TVFQU4uLirB5r06YNzp07B4DXk9mcOXPw7LPPYuTIkWjfvj3Gjh2LmTNnIjk5GQDPky1izonRaERpaSkKCgrs7qMXN2/exMMPP4zc3FykpqZaRnkAnicA2Lt3L/Lz89GkSRPLZ/rZs2cxa9YsNG3aFIB854mdHhURBAHTp0/H5s2bsWvXLsTGxlptj42NhdFoRGpqquWx0tJSpKWloUePHp5urtcMHDgQP/30E7Kzsy0/nTt3xiOPPILs7Gw0a9aM5wlAz549q015cPr0acTExADg9WR27do1+PlZfxT6+/tbStZ5nqoTc07i4+MRGBhotc/Fixdx7NgxXZ03c4fnzJkz+Pbbb1GvXj2r7TxPwNixY/Hjjz9afaZHR0djzpw5+OabbwDIeJ6k512TUqZMmSKEhYUJ33//vXDx4kXLz7Vr1yz7LF68WAgLCxM2b94s/PTTT8KoUaOEqKgooaioyIst977K1VuCwPMkCBVVIgEBAcLLL78snDlzRvjwww+FkJAQYcOGDZZ9eJ4EYdy4cULDhg2FL7/8UsjNzRU2b94s1K9fX5g7d65lHz2ep+LiYuHIkSPCkSNHBADCsmXLhCNHjliqjsSck8mTJwuNGjUSvv32WyErK0sYMGCA0KFDB6GsrMxbb0t2js7TzZs3heHDhwuNGjUSsrOzrT7XS0pKLMfQ+3mypWr1liDIc57Y6VERADZ/3nvvPcs+JpNJWLBggWA0GoXg4GChT58+wk8//eS9RqtE1U4Pz1OFbdu2Ce3atROCg4OF1q1bC2vWrLHazvMkCEVFRcKMGTOEJk2aCDVq1BCaNWsmPPfcc1ZfSno8T7t377b5eTRu3DhBEMSdk+vXrwvTp08XIiIihJo1awr33XefcO7cOS+8G+U4Ok+5ubl2P9d3795tOYbez5Mttjo9cpwngyAIgvTBKCIiIiLfwpweIiIi0gV2eoiIiEgX2OkhIiIiXWCnh4iIiHSBnR4iIiLSBXZ6iIiISBfY6SEiIiJdYKeHiIiIdIGdHiJShXXr1qFu3breboZXnTp1CkajEcXFxQCknZOFCxfi119/rfb4l19+iU6dOlnWEiPSM3Z6iDRu//798Pf3x5AhQ7zdFLcZDAZ8/vnn3m6GYp577jlMmzYNoaGhsh3zvvvug8FgwMaNG2U7JpGvYqeHSOPeffddPPXUU0hPT8e5c+e83Ryy47fffsPWrVvx2GOPSXrev//9b8THx2Pp0qXo0qUL7rrrLnz22WdW+zz22GN4/fXX5WwukU9ip4dIw65evYpPPvkEU6ZMwX333Yd169ZZbf/+++9hMBjw3XffoXPnzggJCUGPHj1w6tQpyz4LFy5Ex44dsX79ejRt2hRhYWEYOXKkJQQDAE2bNsWKFSusjt2xY0csXLjQ8vuyZcvQvn171KpVC40bN8bUqVNx5coVl9/br7/+CoPBgM2bN6N///4ICQlBhw4dkJGRYbXfvn370LdvX4SEhCA8PByDBw9GQUEBAKCkpARPP/00GjRogBo1aqBXr144dOhQtfPzzTffoFOnTqhZsyYGDBiA/Px8fP3112jTpg3q1KmDUaNG4dq1a5bnCYKAJUuWoFmzZqhZsyY6dOiATz/91OH7+eSTT9ChQwc0atTI7j6XLl1C165dMXz4cNy4cQOnT5/GqFGjcO+992L8+PF47733MG3aNNy8edPqecOHD8fBgwfxyy+/iD6/RFrETg+Rhn388ce4/fbbcfvtt2PMmDF47733YGuN4eeeew6vvPIKDh8+jICAADz++ONW23NycvD555/jyy+/xJdffom0tDQsXrxYUlv8/Pzw2muv4dixY3j//fexa9cuzJ071633Z2777NmzkZ2djVatWmHUqFEoKysDAGRnZ2PgwIFo27YtMjIykJ6ejmHDhqG8vBwAMHfuXHz22Wd4//33kZWVhRYtWmDw4MG4fPmy1WssXLgQK1euxP79+3H+/Hk8/PDDWLFiBTZu3IivvvoKqampViMpzz//PN577z2sWrUKx48fx8yZMzFmzBikpaXZfR979uxB586d7W7/7bff0Lt3b7Ru3RqbN29GjRo18OOPP8LPzw+LFi3Cbbfdhnbt2mHs2LEYOXKk1XNjYmLQoEED7N27V/L5JdIUF1aIJyIf0aNHD2HFihWCIAjCzZs3hfr16wupqamW7bt37xYACN9++63lsa+++koAIFy/fl0QBEFYsGCBEBISIhQVFVn2mTNnjtCtWzfL7zExMcLy5cutXrtDhw7CggUL7Lbtk08+EerVq2f5/b333hPCwsIcvh8AwpYtWwRBEITc3FwBgPD2229bth8/flwAIJw8eVIQBEEYNWqU0LNnT5vHunLlihAYGCh8+OGHlsdKS0uF6OhoYcmSJYIg2D4/ycnJAgAhJyfH8tikSZOEwYMHW45bo0YNYf/+/VavN2HCBGHUqFF231uHDh2EF1980eox8zk5deqU0KRJE+Gpp54STCaTZfsvv/wiBAcHC7NmzRImTJgg5Obm2j1+p06dhIULF9rdTqQHHOkh0qhTp07h4MGDlr/6AwIC8Le//Q3vvvtutX3vuOMOy/9HRUUBAPLz8y2PNW3a1Cq5Nioqymq7GLt370ZCQgIaNmyI0NBQPProo7h06RKuXr0q6ThS2m4e6bElJycHN2/eRM+ePS2PBQYGomvXrjh58qTd14iMjERISAiaNWtm9Zj5NU+cOIEbN24gISEBtWvXtvx88MEHyMnJsfs+rl+/jho1ath8vFevXhgxYgRee+01GAwGy7bY2Fikpqbi2LFj+Oijj3DnnXdi9OjRNl+nZs2aViE4Ij0K8HYDiEgZ77zzDsrKytCwYUPLY4IgIDAwEAUFBQgPD7c8HhgYaPl/85dq5RLnytvN+1Te7ufnVy1sVjmv5OzZsxg6dCgmT56Ml156CREREUhPT8eECROq5Z9I5ajtNWvWtPs8c3srdyLMj1d9rOprODof5v9+9dVXVuceAIKDg+22p379+pZco6rPufvuu/HVV19hzpw51XJ+evfujR07dmDhwoVo27Yt3nnnHQwYMAA5OTkICLj1EX/58mXcdtttdl+fSA840kOkQWVlZfjggw/wyiuvIDs72/Jz9OhRxMTE4MMPP5T19W677TZcvHjR8ntRURFyc3Mtvx8+fBhlZWV45ZVXcNddd6FVq1b4/fffZW2DLXfccQe+++47m9tatGiBoKAgpKenWx67efMmDh8+jDZt2rj8mnFxcQgODsa5c+fQokULq5/GjRvbfV6nTp1w4sSJao/7+flh/fr1iI+Px4ABAxyety5dumDp0qU4d+4czp49a3n8xo0byMnJQadOnVx+X0RawJEeIg368ssvUVBQgAkTJiAsLMxq21//+le88847mD59umyvN2DAAKxbtw7Dhg1DeHg4XnjhBfj7+1u2N2/eHGVlZXj99dcxbNgw7Nu3D6tXr5bt9e2ZP38+2rdvj6lTp2Ly5MkICgrC7t278dBDD6F+/fqYMmUK5syZg4iICDRp0gRLlizBtWvXMGHCBJdfMzQ0FLNnz8bMmTNhMpnQq1cvFBUVYf/+/ahduzbGjRtn83mDBw/GE088gfLycqtzBwD+/v748MMPMWrUKAwYMADff/89jEYjduzYgZMnT+L++++HyWRCfn4+1q5di/r166NJkyaW5x84cADBwcHo3r27y++LSAs40kOkQe+88w7uvvvuah0eAHjwwQeRnZ2NrKws2V5v/vz56NOnD+677z4MHToUI0aMQPPmzS3bO3bsiGXLliElJQXt2rXDhx9+iOTkZNle355WrVph586dOHr0KLp27Yru3bvjiy++sIR9Fi9ejAcffBBjx47FnXfeiZ9//hnffPONVejPFS+99BL+8Y9/IDk5GW3atMHgwYOxbds2xMbG2n3O0KFDERgYiG+//dbm9oCAAHz00Udo27atpWy+adOm+OGHH9CnTx8kJSVh4MCBOHnyJL788kurENxHH32ERx55BCEhIW69LyJfZxCqBuKJiMgr3nzzTXzxxRf45ptvJD934cKFGD9+PJo2bWr1+P/+9z+0bt0ahw8fdtjpItIDhreIiFTiySefREFBAYqLi2VbiiI3NxdvvvkmOzxE4EgPERER6QRzeoiIiEgX2OkhIiIiXWCnh4iIiHSBnR4iIiLSBXZ6iIiISBfY6SEiIiJdYKeHiIiIdIGdHiIiItIFdnqIiIhIF/4P/KBthnSu5IgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Annual Income (k$)'],df['Spending Score (1-100)'])\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel(\"Spending Score (1-100)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e323590",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9981111c",
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1caf0b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_predicted = km.fit_predict(df[['Annual Income (k$)','Spending Score (1-100)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2709dafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
       "       2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
       "       2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
       "       2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,\n",
       "       2, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9be4a30d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cluster'] = y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41b764d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Annual Income (k$)  Spending Score (1-100)  cluster\n",
       "0           1                  15                      39        0\n",
       "1           2                  15                      81        0\n",
       "2           3                  16                       6        0\n",
       "3           4                  16                      77        0\n",
       "4           5                  17                      40        0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dabff452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[44.15447154, 49.82926829],\n",
       "       [86.53846154, 82.12820513],\n",
       "       [87.        , 18.63157895]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84b802f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df.cluster==0]\n",
    "df2 = df[df.cluster==1]\n",
    "df3 = df[df.cluster==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb726d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151aa45ff70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1['Annual Income (k$)'],df1['Spending Score (1-100)'],color='green')\n",
    "plt.scatter(df2['Annual Income (k$)'],df2['Spending Score (1-100)'],color='red')\n",
    "plt.scatter(df3['Annual Income (k$)'],df3['Spending Score (1-100)'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ba8e4673",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da2645a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler.fit(df[['Annual Income (k$)']])\n",
    "df['Annual Income (k$)'] = scaler.transform(df[['Annual Income (k$)']])\n",
    "\n",
    "scaler.fit(df[['Spending Score (1-100)']])\n",
    "df['Spending Score (1-100)'] = scaler.transform(df[['Spending Score (1-100)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d3f4c90f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>0.775510</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.016393</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Annual Income (k$)  Spending Score (1-100)  cluster\n",
       "0           1            0.000000                0.387755        0\n",
       "1           2            0.000000                0.816327        0\n",
       "2           3            0.008197                0.051020        0\n",
       "3           4            0.008197                0.775510        0\n",
       "4           5            0.016393                0.397959        0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "df6beac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x151aa5907c0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Annual Income (k$)'],df['Spending Score (1-100)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a38f32b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0, 2,\n",
       "       0, 2])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3)\n",
    "y_predicted = km.fit_predict(df[['Annual Income (k$)','Spending Score (1-100)']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "513e7ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cluster2'] = y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39ef9bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>0.775510</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.016393</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Annual Income (k$)  Spending Score (1-100)  cluster  cluster2\n",
       "0           1            0.000000                0.387755        0         1\n",
       "1           2            0.000000                0.816327        0         1\n",
       "2           3            0.008197                0.051020        0         0\n",
       "3           4            0.008197                0.775510        0         1\n",
       "4           5            0.016393                0.397959        0         1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "79246181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.46737705, 0.1577551 ],\n",
       "       [0.25631369, 0.5426549 ],\n",
       "       [0.58638083, 0.82783883]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c722e1c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151aa548a60>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df[df.cluster2==0]\n",
    "df2 = df[df.cluster2==1]\n",
    "df3 = df[df.cluster2==2]\n",
    "plt.scatter(df1['Annual Income (k$)'],df1['Spending Score (1-100)'],color='green')\n",
    "plt.scatter(df2['Annual Income (k$)'],df2['Spending Score (1-100)'],color='red')\n",
    "plt.scatter(df3['Annual Income (k$)'],df3['Spending Score (1-100)'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "917fbe38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e65083b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "sse = []\n",
    "k_rng = range(1,10)\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df[['Annual Income (k$)','Spending Score (1-100)']])\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8bdd5874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151aaa8c8e0>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('K')\n",
    "plt.ylabel('Sum of squared error')\n",
    "plt.plot(k_rng,sse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7db3b429",
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(n_clusters=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd4bd645",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
       "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 0,\n",
       "       4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 2, 1, 2, 1, 2,\n",
       "       0, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = km.fit_predict(df[['Annual Income (k$)','Spending Score (1-100)']])\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b24b55b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cluster3'] = y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a336aecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster2</th>\n",
       "      <th>cluster3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.816327</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.008197</td>\n",
       "      <td>0.775510</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.016393</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Annual Income (k$)  Spending Score (1-100)  cluster  cluster2  \\\n",
       "0           1            0.000000                0.387755        0         1   \n",
       "1           2            0.000000                0.816327        0         1   \n",
       "2           3            0.008197                0.051020        0         0   \n",
       "3           4            0.008197                0.775510        0         1   \n",
       "4           5            0.016393                0.397959        0         1   \n",
       "\n",
       "   cluster3  \n",
       "0         4  \n",
       "1         3  \n",
       "2         4  \n",
       "3         3  \n",
       "4         4  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1110e94d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.33029751, 0.49508692],\n",
       "       [0.6       , 0.16443149],\n",
       "       [0.58638083, 0.82783883],\n",
       "       [0.08792846, 0.79962894],\n",
       "       [0.09265859, 0.20319432]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bf6afa78",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df.cluster3==0]\n",
    "df2 = df[df.cluster3==1]\n",
    "df3 = df[df.cluster3==2]\n",
    "df4 = df[df.cluster3==3]\n",
    "df5 = df[df.cluster3==4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5fc85f18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x151aa9cf8b0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1['Annual Income (k$)'],df1['Spending Score (1-100)'],color='green')\n",
    "plt.scatter(df2['Annual Income (k$)'],df2['Spending Score (1-100)'],color='red')\n",
    "plt.scatter(df3['Annual Income (k$)'],df3['Spending Score (1-100)'],color='black')\n",
    "plt.scatter(df4['Annual Income (k$)'],df4['Spending Score (1-100)'],color='red')\n",
    "plt.scatter(df5['Annual Income (k$)'],df5['Spending Score (1-100)'],color='black')\n",
    "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ac4462f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "00e09260",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_labels = km.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9fa2ee5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
       "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 0,\n",
       "       4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 2, 1, 2, 1, 2,\n",
       "       0, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,\n",
       "       1, 2])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bb3a47d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_avg = silhouette_score(df[['Annual Income (k$)','Spending Score (1-100)']],cluster_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b669bc25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5594854531227246"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe4ba9f",
   "metadata": {},
   "source": [
    "# DBSCAN clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c3e849c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c719a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"files/Mall_Customers.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aebe05b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,[3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4b201e7b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dbscan = DBSCAN(eps=3,min_samples=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f36aab8",
   "metadata": {},
   "source": [
    "here eps is radius of our boundary and min_samples refers to how many datapoints is required to become  core point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1367100c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DBSCAN(eps=3, min_samples=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DBSCAN</label><div class=\"sk-toggleable__content\"><pre>DBSCAN(eps=3, min_samples=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DBSCAN(eps=3, min_samples=4)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8380bdd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f0fc9f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1,  0,  0,  0,  0, -1, -1,  0, -1,  0, -1,  0,  0,\n",
       "       -1,  0, -1, -1,  0, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1, -1,  2,  1,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  2,\n",
       "        3,  3, -1,  3, -1, -1,  4, -1, -1, -1,  4,  5,  4, -1,  4,  5, -1,\n",
       "        5,  4, -1,  4,  5, -1, -1,  6, -1, -1, -1,  7, -1,  6, -1,  6, -1,\n",
       "        7, -1,  6, -1,  7, -1,  7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "        8, -1,  8, -1,  8, -1,  8, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ba864aee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cluster'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "96f14f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)  \\\n",
       "0             1    Male   19                  15                      39   \n",
       "1             2    Male   21                  15                      81   \n",
       "2             3  Female   20                  16                       6   \n",
       "3             4  Female   23                  16                      77   \n",
       "4             5  Female   31                  17                      40   \n",
       "..          ...     ...  ...                 ...                     ...   \n",
       "195         196  Female   35                 120                      79   \n",
       "196         197  Female   45                 126                      28   \n",
       "197         198    Male   32                 126                      74   \n",
       "198         199    Male   32                 137                      18   \n",
       "199         200    Male   30                 137                      83   \n",
       "\n",
       "     cluster  \n",
       "0         -1  \n",
       "1         -1  \n",
       "2         -1  \n",
       "3         -1  \n",
       "4         -1  \n",
       "..       ...  \n",
       "195       -1  \n",
       "196       -1  \n",
       "197       -1  \n",
       "198       -1  \n",
       "199       -1  \n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cf5bb0",
   "metadata": {},
   "source": [
    "all the -1 here are outliers which are not in any clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e38b9c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dcd35bfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = silhouette_score(X,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fce17021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1908319132560097"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2303b4",
   "metadata": {},
   "source": [
    "### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "78f13b3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.062924</td>\n",
       "      <td>176.086355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.804094</td>\n",
       "      <td>178.388669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.930863</td>\n",
       "      <td>170.284496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.733843</td>\n",
       "      <td>168.691992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65.431230</td>\n",
       "      <td>173.763679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Weight      Height\n",
       "0  67.062924  176.086355\n",
       "1  68.804094  178.388669\n",
       "2  60.930863  170.284496\n",
       "3  59.733843  168.691992\n",
       "4  65.431230  173.763679"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"files/Clustering_gmm (2).csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8f58fb4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight    0\n",
       "Height    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "13df7f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 2)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d6b543f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Height')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Weight'],df['Height'])\n",
    "plt.xlabel(\"Weight\")\n",
    "plt.ylabel(\"Height\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3d058440",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b26ce8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbscan = DBSCAN(eps=3,min_samples=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5b376ed1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = dbscan.fit_predict(df[['Weight','Height']])\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69dc0c68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e7fa0bff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6649421222120799"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = silhouette_score(df[['Weight','Height']],labels=model)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b43398",
   "metadata": {},
   "source": [
    "#### Hirarchical clustering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "407a2608",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5325d150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('files/Mall_Customers.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9fdefae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop(['CustomerID','Gender','Spending Score (1-100)'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "25260192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18108819f60>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.scatter(df['Age'],df['Annual Income (k$)'],s=100,c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "900b7b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "91169240",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "76e432e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dentogram = sch.dendrogram(sch.linkage(data,method=\"ward\"))\n",
    "plt.title(\"Dendogram\")\n",
    "plt.xlabel(\"customers\")\n",
    "plt.ylabel(\"Euclidean distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bbfff469",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "model = AgglomerativeClustering(n_clusters=2,linkage='ward',affinity='euclidean')\n",
    "model.fit_predict(df[['Age','Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6d2c5e04",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "cluster = model.fit_predict(df[['Age','Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3f2da6d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4104652474372429"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "score = silhouette_score(df[['Age','Annual Income (k$)']],cluster)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2e0abed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scale = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2f753cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = scale.fit_transform(df[['Age']])\n",
    "df['Annual Income (k$)'] = scale.fit_transform(df[['Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f23f402",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "67e31598",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_predict(df[['Age','Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ef59bf60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "cluseter = model.fit_predict(df[['Age','Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5755a4a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3020584188539522"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = silhouette_score(df[['Age','Annual Income (k$)']],cluster)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "06edce58",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "model = KMeans(n_clusters=2)\n",
    "km_cluster = model.fit_predict(df[['Age','Annual Income (k$)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "97181736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41300834916124046"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = silhouette_score(df[['Age','Annual Income (k$)']],km_cluster)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16684943",
   "metadata": {},
   "source": [
    "## Adaboost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "913a5485",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ea0abe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Senior Manager</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Director</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender Education Level          Job Title  Years of Experience  \\\n",
       "0  32.0    Male      Bachelor's  Software Engineer                  5.0   \n",
       "1  28.0  Female        Master's       Data Analyst                  3.0   \n",
       "2  45.0    Male             PhD     Senior Manager                 15.0   \n",
       "3  36.0  Female      Bachelor's    Sales Associate                  7.0   \n",
       "4  52.0    Male        Master's           Director                 20.0   \n",
       "\n",
       "     Salary  \n",
       "0   90000.0  \n",
       "1   65000.0  \n",
       "2  150000.0  \n",
       "3   60000.0  \n",
       "4  200000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"files/ens/Salary Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0e46eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    2\n",
       "Gender                 2\n",
       "Education Level        2\n",
       "Job Title              2\n",
       "Years of Experience    2\n",
       "Salary                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c4765d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e309b1dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "917bcbed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    0\n",
       "Gender                 0\n",
       "Education Level        0\n",
       "Job Title              0\n",
       "Years of Experience    0\n",
       "Salary                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cedfcb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Gender',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f9d952f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Senior Manager</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Director</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Education Level          Job Title  Years of Experience    Salary\n",
       "0  32.0      Bachelor's  Software Engineer                  5.0   90000.0\n",
       "1  28.0        Master's       Data Analyst                  3.0   65000.0\n",
       "2  45.0             PhD     Senior Manager                 15.0  150000.0\n",
       "3  36.0      Bachelor's    Sales Associate                  7.0   60000.0\n",
       "4  52.0        Master's           Director                 20.0  200000.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "693cf068",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c453682",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb45095e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Education Level'] = scaler.fit_transform(df['Education Level'])\n",
    "df['Job Title'] = scaler.fit_transform(df['Job Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40c7004a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Education Level  Job Title  Years of Experience    Salary\n",
       "0  32.0                0        159                  5.0   90000.0\n",
       "1  28.0                1         17                  3.0   65000.0\n",
       "2  45.0                2        130                 15.0  150000.0\n",
       "3  36.0                0        101                  7.0   60000.0\n",
       "4  52.0                1         22                 20.0  200000.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5f43ab64",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AdaBoostRegressor(n_estimators=50,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a9b47854",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "936bcdf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Salary',axis=1)\n",
    "Y = df.Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fb41a673",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99055bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostRegressor(random_state=42)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f15eabd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9209780999612166"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0fb4ae9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([88098.5915493])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[32,1,22,15]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051ca066",
   "metadata": {},
   "source": [
    "## Gradient Boosting classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fa1fdc05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5a1e90fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>job</th>\n",
       "      <th>degree</th>\n",
       "      <th>salary_more_than_100k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>google</td>\n",
       "      <td>sale executive</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>google</td>\n",
       "      <td>sale executive</td>\n",
       "      <td>masters</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>google</td>\n",
       "      <td>business manager</td>\n",
       "      <td>masters</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>google</td>\n",
       "      <td>computer programmer</td>\n",
       "      <td>bachelors</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company                  job     degree  salary_more_than_100k\n",
       "0  google       sale executive  bachelors                      0\n",
       "1  google       sale executive    masters                      0\n",
       "2  google     business manager  bachelors                      1\n",
       "3  google     business manager    masters                      1\n",
       "4  google  computer programmer  bachelors                      0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('files/ens/Book1 - Copy.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "af2d0d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company                  0\n",
       "job                      0\n",
       "degree                   0\n",
       "salary_more_than_100k    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a7f32648",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['company'] = scaler.fit_transform(df['company'])\n",
    "df['job'] = scaler.fit_transform(df['job'])\n",
    "df['degree'] = scaler.fit_transform(df['degree'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8008f9e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f2c462f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('salary_more_than_100k',axis=1)\n",
    "Y = df.salary_more_than_100k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "7d93c81c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d206b715",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GradientBoostingClassifier(n_estimators=80,learning_rate=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "11b27b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.2, n_estimators=80, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.2, n_estimators=80, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.2, n_estimators=80, random_state=42)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2e4b058a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6c7cc8",
   "metadata": {},
   "source": [
    "### Bagging techniques\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2558d37b",
   "metadata": {},
   "source": [
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43e2396c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8162fe34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Senior Manager</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Director</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender Education Level          Job Title  Years of Experience  \\\n",
       "0  32.0    Male      Bachelor's  Software Engineer                  5.0   \n",
       "1  28.0  Female        Master's       Data Analyst                  3.0   \n",
       "2  45.0    Male             PhD     Senior Manager                 15.0   \n",
       "3  36.0  Female      Bachelor's    Sales Associate                  7.0   \n",
       "4  52.0    Male        Master's           Director                 20.0   \n",
       "\n",
       "     Salary  \n",
       "0   90000.0  \n",
       "1   65000.0  \n",
       "2  150000.0  \n",
       "3   60000.0  \n",
       "4  200000.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"files/Salary Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fe4c4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(375, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa62cdff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    2\n",
       "Gender                 2\n",
       "Education Level        2\n",
       "Job Title              2\n",
       "Years of Experience    2\n",
       "Salary                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d67ccc81",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90311f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                    0\n",
       "Gender                 0\n",
       "Education Level        0\n",
       "Job Title              0\n",
       "Years of Experience    0\n",
       "Salary                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a88824d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_5600\\1134722465.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()\n"
     ]
    },
    {
     "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>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979128</td>\n",
       "      <td>0.922335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years of Experience</th>\n",
       "      <td>0.979128</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.922335</td>\n",
       "      <td>0.930338</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Age  Years of Experience    Salary\n",
       "Age                  1.000000             0.979128  0.922335\n",
       "Years of Experience  0.979128             1.000000  0.930338\n",
       "Salary               0.922335             0.930338  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb860698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Education Level', 'Job Title', 'Years of Experience',\n",
       "       'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82ffa788",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Gender',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2d2854d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Senior Manager</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Master's</td>\n",
       "      <td>Director</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Education Level          Job Title  Years of Experience    Salary\n",
       "0  32.0      Bachelor's  Software Engineer                  5.0   90000.0\n",
       "1  28.0        Master's       Data Analyst                  3.0   65000.0\n",
       "2  45.0             PhD     Senior Manager                 15.0  150000.0\n",
       "3  36.0      Bachelor's    Sales Associate                  7.0   60000.0\n",
       "4  52.0        Master's           Director                 20.0  200000.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b612a426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                     31\n",
       "Education Level          3\n",
       "Job Title              174\n",
       "Years of Experience     28\n",
       "Salary                  36\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb28cffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2bb73eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler= LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "212319ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Education Level'] = scaler.fit_transform(df['Education Level'])\n",
    "df['Job Title'] = scaler.fit_transform(df['Job Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b634b63a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Years of Experience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>7.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Education Level  Job Title  Years of Experience    Salary\n",
       "0  32.0                0        159                  5.0   90000.0\n",
       "1  28.0                1         17                  3.0   65000.0\n",
       "2  45.0                2        130                 15.0  150000.0\n",
       "3  36.0                0        101                  7.0   60000.0\n",
       "4  52.0                1         22                 20.0  200000.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e7635167",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Salary',axis=1)\n",
    "Y = df['Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b6f55a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2854d063",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "04463f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "70428843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d175b606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9801829545524086"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23000fed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb6ddef1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b180dd70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17c21a09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f7e5d1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}