MiniProject_Mall_Customers_Segmentation(Clustering).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dacd519e",
   "metadata": {},
   "source": [
    "# Clustering Mini Projects"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3e1a6a",
   "metadata": {},
   "source": [
    "# Mall Customers Segmentation\n",
    "\n",
    "Customers who come for shopping in a mall can be grouped in a number of ways. Shopping pattern, \n",
    "spending pattern, shopping score, salary etc are some of the factors that can be used to divide the \n",
    "customers into different categories.\n",
    "\n",
    "The reason for these clusters is to help identify those customers who would be interested in certain \n",
    "products, offers and services. The store may strategize its offerings in such a way that it targets only \n",
    "right customers for specific products. This helps in creating a win-win situation for both the store (in \n",
    "terms of revenue and more customers through customer recommendation) and the customer \n",
    "(discounts, offers, reward points, gifts, etc)\n",
    "\n",
    "## Business Objective: \n",
    "- To create Customer cluster to identify the different types of customers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc635cfc",
   "metadata": {},
   "source": [
    "### Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1994883",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                                                  # for working with dataframes\n",
    "import numpy as np                                                   # for working with arrays\n",
    "import matplotlib.pyplot as plt                                      # for visualizations\n",
    "import seaborn as sb                                                 # for visualizations\n",
    "from tabulate import tabulate                                        # for tabulations\n",
    "\n",
    "from sklearn import preprocessing                                    # for preprocessing datas\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler        # for data scaling\n",
    "from sklearn.decomposition import PCA                                # for feature reduction\n",
    "from sklearn.cluster import KMeans                                   # for kmeans clustering algorithm\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score     # for silhouette score calculations\n",
    "import matplotlib.cm as cm                                           # for visualization colormap\n",
    "import scipy.cluster.hierarchy as sch                                # for Dendrogram\n",
    "from sklearn.cluster import AgglomerativeClustering                  # for hierarchical clustering algorithm\n",
    "from sklearn.cluster import DBSCAN                                   # for dbscan algorithm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')                                    # to ignore warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2cb5ab",
   "metadata": {},
   "source": [
    "### Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59c8f599",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DS3_C6_S3_MallCustomers_Data_Project.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c2d8ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9475b641",
   "metadata": {},
   "source": [
    "**Data Dictionary**:\n",
    "- **CustomerID**: Customer unique identification number\n",
    "- **Gender**: Gender of the customer\n",
    "- **Age**: Age of the customer \n",
    "- **Annual Income**: Annual Income of the customer\n",
    "- **Spending Score**: Spending scores by customer to purchase products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8baa9e09",
   "metadata": {},
   "source": [
    "### Shape of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d459e123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape   : (200, 5)\n",
      "Rows    : 200\n",
      "Columns : 5\n"
     ]
    }
   ],
   "source": [
    "print('Shape   :',df.shape)\n",
    "print('Rows    :',df.shape[0])\n",
    "print('Columns :',df.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3dafb86",
   "metadata": {},
   "source": [
    "### Information of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71ebdc1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   CustomerID              200 non-null    int64 \n",
      " 1   Gender                  200 non-null    object\n",
      " 2   Age                     200 non-null    int64 \n",
      " 3   Annual Income (k$)      200 non-null    int64 \n",
      " 4   Spending Score (1-100)  200 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 7.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1054142",
   "metadata": {},
   "source": [
    "The dataset contains 4 numerical and 1 categorical columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7ec27e",
   "metadata": {},
   "source": [
    "### Summary of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4152fcda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
       "count  200.000000  200.000000          200.000000              200.000000\n",
       "mean   100.500000   38.850000           60.560000               50.200000\n",
       "std     57.879185   13.969007           26.264721               25.823522\n",
       "min      1.000000   18.000000           15.000000                1.000000\n",
       "25%     50.750000   28.750000           41.500000               34.750000\n",
       "50%    100.500000   36.000000           61.500000               50.000000\n",
       "75%    150.250000   49.000000           78.000000               73.000000\n",
       "max    200.000000   70.000000          137.000000               99.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerical\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e67b3af9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender\n",
       "count      200\n",
       "unique       2\n",
       "top     Female\n",
       "freq       112"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical\n",
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220be425",
   "metadata": {},
   "source": [
    "### DataTypes of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb7ab8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                 int64\n",
       "Gender                    object\n",
       "Age                        int64\n",
       "Annual Income (k$)         int64\n",
       "Spending Score (1-100)     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471df0ca",
   "metadata": {},
   "source": [
    "### Segregation of Qualitative and Quantitative types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "519a5808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical    Numerical\n",
      "-------------  ----------------------\n",
      "Gender         CustomerID\n",
      "               Age\n",
      "               Annual Income (k$)\n",
      "               Spending Score (1-100)\n"
     ]
    }
   ],
   "source": [
    "a = df.select_dtypes(exclude='object')\n",
    "b = df.select_dtypes(include='object')\n",
    "\n",
    "a_lst = list(a.columns)\n",
    "b_lst = list(b.columns)\n",
    "table = [a_lst,b_lst]\n",
    "\n",
    "print(tabulate({'Categorical':b_lst,\n",
    "                'Numerical':a_lst},headers = ['Categorical','Numerical']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979ba224",
   "metadata": {},
   "source": [
    "### Checking for Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "053a4022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Gender                    0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a34920ec",
   "metadata": {},
   "source": [
    "***There are no missing values in the dataset***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e46e25d",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b1ceb6",
   "metadata": {},
   "source": [
    "### Gender Ratio of Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c47de2b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df['Gender'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.pie(data.values,labels=data.index,autopct='%.1f%%')\n",
    "plt.title('Gender Ratio of Mall Customers',fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cc582c",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- Female customers are more than Male customers.  \n",
    "- There are 56% of Female customers and 44% of male customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54fa8cc1",
   "metadata": {},
   "source": [
    "### Age Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "193d874d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,2,tight_layout=True,figsize=(15,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sb.boxplot(df['Age'])\n",
    "plt.suptitle('Age Distribution of Customers',fontsize=20)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sb.histplot(df['Age'],kde=True,color='g',bins=[10,20,30,40,50,60,70])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7a398e",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- There are more number of customers of age group between 30 to 50."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41511f51",
   "metadata": {},
   "source": [
    "### Annual Income Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "feec9a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Annual Income (k$)', ylabel='Count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,2,tight_layout=True,figsize=(15,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sb.boxplot(df['Annual Income (k$)'])\n",
    "plt.suptitle('Annual Income Distribution of Customers',fontsize=20)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sb.histplot(df['Annual Income (k$)'],kde=True,color='g',bins=[0,20,40,60,80,100,120,140])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee6d1c9",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- Most customers Annual income ranges between 40k to 80k dollars."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51abb6c",
   "metadata": {},
   "source": [
    "### Spending Score Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64824fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Spending Score (1-100)', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,2,tight_layout=True,figsize=(15,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sb.boxplot(df['Spending Score (1-100)'])\n",
    "plt.suptitle('Spending Score Distribution among Customers',fontsize=20)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sb.histplot(df['Spending Score (1-100)'],kde=True,color='g',bins=[0,20,40,60,80,100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e02ac5f",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- Most customers spending scores are between 35 to 70."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c5cfc7",
   "metadata": {},
   "source": [
    "### Ratio of spending score and annual income among genders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe1de8cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = df.groupby(['Gender'])['Spending Score (1-100)'].mean()\n",
    "data2 = df.groupby(['Gender'])['Annual Income (k$)'].mean()\n",
    "\n",
    "fig,ax = plt.subplots(2,2,tight_layout=True,figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.pie(data1.values,labels=data1.index,autopct='%.2f%%')\n",
    "plt.title('Ratio of Spending Score among Gender',fontsize=15)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.pie(data2.values,labels=data2.index,autopct='%.2f%%')\n",
    "plt.title('Ratio of Annual Income among Gender',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ecabbe",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- The ratio of spending score of female customers are higher than male customers.\n",
    "- The ratio of Annual Income of female is lower than male customers.\n",
    "- Eventhough the female customers' annual income is lower their spending score is little higher than male customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cf696c",
   "metadata": {},
   "source": [
    "# Data Preparation for Model:\n",
    "- As the customer_id and gender columns are irrelevant as we are not seggregating the customers by genderwise ,we are not using those columns for the model.\n",
    "- There is no huge difference in the ranges of values in columns so data scaling is not required for the model.\n",
    "- We are using the columns: Age, Annual Income (k$), Spending Score (1-100)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a5a2c3ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[['Age','Annual Income (k$)','Spending Score (1-100)']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a370abd1",
   "metadata": {},
   "source": [
    "## DIMENSION REDUCTION ALGORITHM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55ab155",
   "metadata": {},
   "source": [
    "## PCA (Principal Component Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "edcfb3c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.18705078e+01, -3.30014253e+01],\n",
       "       [ 7.63396908e-01, -5.68438653e+01],\n",
       "       [-5.74087256e+01, -1.31229362e+01],\n",
       "       [-2.16989648e+00, -5.34779049e+01],\n",
       "       [-3.21749197e+01, -3.03870050e+01],\n",
       "       [-2.17827777e+00, -5.22265808e+01],\n",
       "       [-5.90660565e+01, -9.54170669e+00],\n",
       "       [ 1.23693261e+01, -6.16176843e+01],\n",
       "       [-6.63156568e+01, -3.21359934e+00],\n",
       "       [-5.65677534e+00, -4.72664534e+01],\n",
       "       [-5.82366179e+01, -9.13375312e+00],\n",
       "       [ 1.46202592e+01, -6.21072940e+01],\n",
       "       [-5.51612126e+01, -1.00779778e+01],\n",
       "       [-4.30660584e-03, -5.01134367e+01],\n",
       "       [-5.27647474e+01, -1.16804199e+01],\n",
       "       [ 1.94563480e+00, -5.15231943e+01],\n",
       "       [-3.45062349e+01, -2.37600734e+01],\n",
       "       [-7.30573032e+00, -4.35158724e+01],\n",
       "       [-4.12574933e+01, -1.64734320e+01],\n",
       "       [ 1.61888268e+01, -5.82998775e+01],\n",
       "       [-3.27403118e+01, -2.13349462e+01],\n",
       "       [-9.82702739e-01, -4.44533143e+01],\n",
       "       [-5.78102821e+01, -1.86854579e+00],\n",
       "       [-1.52790692e+00, -4.28591474e+01],\n",
       "       [-5.04821841e+01, -3.56091922e+00],\n",
       "       [ 7.68993306e+00, -4.58611728e+01],\n",
       "       [-3.46334793e+01, -1.50700505e+01],\n",
       "       [-9.94983869e+00, -3.30231964e+01],\n",
       "       [-3.38859638e+01, -1.43425872e+01],\n",
       "       [ 1.33424018e+01, -4.87081562e+01],\n",
       "       [-5.82987123e+01,  4.58075927e+00],\n",
       "       [ 3.30504018e+00, -4.01269208e+01],\n",
       "       [-5.52099698e+01,  6.08913003e+00],\n",
       "       [ 2.05718194e+01, -4.89990472e+01],\n",
       "       [-4.65941080e+01, -1.73866592e-01],\n",
       "       [ 1.13589353e+01, -4.22931022e+01],\n",
       "       [-4.23246581e+01, -2.00398803e+00],\n",
       "       [ 3.95883646e+00, -3.57147310e+01],\n",
       "       [-3.23509213e+01, -5.52987430e+00],\n",
       "       [ 9.18649452e+00, -3.57470830e+01],\n",
       "       [-3.01685636e+01, -6.08872992e+00],\n",
       "       [ 2.23811793e+01, -4.41713774e+01],\n",
       "       [-2.55813646e+01, -8.08067622e+00],\n",
       "       [-2.71889068e+00, -2.46549242e+01],\n",
       "       [-3.20583109e+01, -3.35840237e+00],\n",
       "       [ 1.74791473e+00, -2.78673350e+01],\n",
       "       [-1.04367385e+01, -1.79147281e+01],\n",
       "       [-1.23783039e+01, -1.63356191e+01],\n",
       "       [-1.66862348e+01, -1.32041208e+01],\n",
       "       [-1.70641832e+01, -1.29421904e+01],\n",
       "       [-1.14284717e+01, -1.47072011e+01],\n",
       "       [-2.11691252e+00, -2.13939530e+01],\n",
       "       [-5.86630212e+00, -1.74040262e+01],\n",
       "       [-6.44160070e+00, -1.71804820e+01],\n",
       "       [-1.65307804e+01, -9.75046514e+00],\n",
       "       [-1.91078439e+01, -7.84770642e+00],\n",
       "       [-1.22011311e+01, -1.16806921e+01],\n",
       "       [-1.87466527e+01, -7.02766414e+00],\n",
       "       [-5.70247177e+00, -1.37810191e+01],\n",
       "       [-1.45457835e+01, -7.50635593e+00],\n",
       "       [-9.89837985e+00, -1.10190833e+01],\n",
       "       [-1.04669216e+00, -1.71243951e+01],\n",
       "       [-1.18868022e+01, -8.30794885e+00],\n",
       "       [-3.92816213e+00, -1.40278916e+01],\n",
       "       [-1.13282609e+01, -7.44952035e+00],\n",
       "       [ 3.46355009e+00, -1.79342631e+01],\n",
       "       [-8.33477343e+00, -9.49491083e+00],\n",
       "       [-1.46311214e+01, -5.07295359e+00],\n",
       "       [ 3.27457589e+00, -1.78032979e+01],\n",
       "       [-8.61404674e+00, -9.21378732e+00],\n",
       "       [-8.91845330e+00, -8.02004253e+00],\n",
       "       [-1.47900012e+01, -3.57136564e+00],\n",
       "       [-1.11560493e+01, -5.07783734e+00],\n",
       "       [-5.65407378e+00, -9.09523241e+00],\n",
       "       [-1.01845040e+01, -8.27472473e-01],\n",
       "       [ 1.55362009e+00, -9.16671924e+00],\n",
       "       [-2.82288621e+00, -6.10446680e+00],\n",
       "       [-5.80799772e+00, -3.88972491e+00],\n",
       "       [ 5.48549687e-01, -8.41178768e+00],\n",
       "       [-1.22247445e+01,  7.32443405e-01],\n",
       "       [-6.66256961e+00, -3.38505720e+00],\n",
       "       [ 7.19261956e-02, -8.16905039e+00],\n",
       "       [-1.64122766e+01,  3.66373064e+00],\n",
       "       [-1.00858289e+01, -8.08279367e-01],\n",
       "       [ 4.85648060e+00, -1.15432860e+01],\n",
       "       [-8.89178433e+00, -1.69417612e+00],\n",
       "       [ 9.83277363e-01, -5.23925550e+00],\n",
       "       [ 4.86143645e+00, -7.83936645e+00],\n",
       "       [ 7.11236959e+00, -8.32897619e+00],\n",
       "       [-6.91516865e+00,  1.80125719e+00],\n",
       "       [-2.65409471e+00, -1.98215661e-01],\n",
       "       [-4.20933575e+00,  1.28831433e+00],\n",
       "       [-3.00194870e+00,  1.43433750e+00],\n",
       "       [-8.56412363e+00,  5.55183811e+00],\n",
       "       [-5.48033702e+00,  3.35628932e+00],\n",
       "       [ 3.89142160e+00, -3.43056812e+00],\n",
       "       [-4.38496751e+00,  2.45119946e+00],\n",
       "       [ 1.75250600e+00, -1.88984535e+00],\n",
       "       [-7.91528320e+00,  6.26010829e+00],\n",
       "       [ 2.87796991e+00, -1.42431246e+00],\n",
       "       [-3.38828369e+00,  4.36826753e+00],\n",
       "       [-2.79963737e+00,  3.75596774e+00],\n",
       "       [ 2.44478856e+00, -1.99708008e-01],\n",
       "       [ 7.04874475e+00, -3.27362700e+00],\n",
       "       [ 3.48833465e+00, -8.35340915e-01],\n",
       "       [-2.22433878e+00,  3.53242354e+00],\n",
       "       [-3.85156474e+00,  5.64292475e+00],\n",
       "       [-4.72786035e+00,  6.36699664e+00],\n",
       "       [-9.73148866e+00,  9.92225023e+00],\n",
       "       [-5.42355775e+00,  6.79075191e+00],\n",
       "       [-2.09059754e+00,  4.36413238e+00],\n",
       "       [ 8.17420867e+00, -2.80809411e+00],\n",
       "       [-4.25961814e+00,  7.37558345e+00],\n",
       "       [ 2.47487766e+00,  2.59159027e+00],\n",
       "       [ 4.82448589e+00,  2.12117363e+00],\n",
       "       [ 6.20750469e+00,  1.10431167e+00],\n",
       "       [-7.60933562e+00,  1.08841757e+01],\n",
       "       [ 7.61224722e+00, -1.31954477e-01],\n",
       "       [-4.16436318e+00,  1.09293447e+01],\n",
       "       [ 7.02856205e+00,  2.76358934e+00],\n",
       "       [ 1.05889721e+01,  3.25303258e-01],\n",
       "       [-4.06568809e+00,  1.09485378e+01],\n",
       "       [ 1.08815826e+01,  2.49677519e+00],\n",
       "       [ 3.70084414e+01, -1.65733382e+01],\n",
       "       [-8.11111356e+00,  1.77222363e+01],\n",
       "       [ 2.81049250e+01, -8.77789396e+00],\n",
       "       [-6.58597753e+00,  1.77064346e+01],\n",
       "       [ 4.11407352e+01, -1.71212759e+01],\n",
       "       [-2.84734808e+01,  3.35758038e+01],\n",
       "       [ 2.57987536e+01, -5.90493465e+00],\n",
       "       [-2.77777834e+01,  3.31520486e+01],\n",
       "       [ 2.56097794e+01, -5.77396944e+00],\n",
       "       [-3.38179741e+00,  1.67313503e+01],\n",
       "       [ 2.45662280e+01, -3.71766101e+00],\n",
       "       [-2.46421840e+01,  3.35283939e+01],\n",
       "       [ 3.88947579e+01, -1.29277466e+01],\n",
       "       [-2.76055718e+01,  3.55237316e+01],\n",
       "       [ 2.65378878e+01, -3.92614725e+00],\n",
       "       [-1.99345862e+01,  3.13362365e+01],\n",
       "       [ 2.57736097e+01, -2.15096233e+00],\n",
       "       [-3.04569473e+01,  3.99908578e+01],\n",
       "       [ 4.34350999e+01, -1.37876674e+01],\n",
       "       [ 3.12182308e+00,  1.69142673e+01],\n",
       "       [ 3.93077619e+01, -9.53581020e+00],\n",
       "       [-1.77305154e+01,  3.33993277e+01],\n",
       "       [ 4.85122647e+01, -1.49904311e+01],\n",
       "       [-3.21300591e+00,  2.26376014e+01],\n",
       "       [ 2.96784484e+01, -1.26655792e+00],\n",
       "       [-1.09826771e+01,  2.96472544e+01],\n",
       "       [ 4.24650851e+01, -9.37886909e+00],\n",
       "       [-1.66134274e+01,  3.36955092e+01],\n",
       "       [ 3.99482210e+01, -7.57621591e+00],\n",
       "       [-1.44444121e+01,  3.21047336e+01],\n",
       "       [ 3.07052372e+01, -8.20218136e-01],\n",
       "       [-1.81553207e+01,  3.47932836e+01],\n",
       "       [ 4.30019079e+01, -9.72171193e+00],\n",
       "       [-2.80555263e+01,  4.20923353e+01],\n",
       "       [ 3.37890238e+01, -3.01576692e+00],\n",
       "       [-2.74886037e+01,  4.16994397e+01],\n",
       "       [ 2.98590413e+01, -1.46199015e-01],\n",
       "       [-4.33351397e+00,  2.58759881e+01],\n",
       "       [ 3.84966216e+01, -5.20792430e+00],\n",
       "       [-1.97440816e+01,  3.98644345e+01],\n",
       "       [ 4.71559202e+01, -9.06837826e+00],\n",
       "       [-6.74179118e+00,  3.51056735e+01],\n",
       "       [ 3.44176762e+01,  5.15039513e+00],\n",
       "       [-9.35733558e+00,  3.83098091e+01],\n",
       "       [ 5.12931699e+01, -5.91239638e+00],\n",
       "       [-2.13287384e+00,  3.43149985e+01],\n",
       "       [ 2.69188971e+01,  1.31302488e+01],\n",
       "       [-1.38927217e+01,  4.28736494e+01],\n",
       "       [ 3.71067519e+01,  5.71942496e+00],\n",
       "       [-1.54948144e+01,  4.40715294e+01],\n",
       "       [ 4.89568988e+01, -2.98938430e+00],\n",
       "       [-1.55717711e+01,  4.52536076e+01],\n",
       "       [ 4.59634060e+01,  4.76681701e-01],\n",
       "       [-1.51336232e+01,  4.48915717e+01],\n",
       "       [ 3.31683881e+01,  9.84031698e+00],\n",
       "       [-1.31653889e+01,  4.96383291e+01],\n",
       "       [ 5.11057261e+01,  2.87773203e+00],\n",
       "       [ 7.49454467e+00,  3.96601531e+01],\n",
       "       [ 5.08832268e+01,  8.01399365e+00],\n",
       "       [-6.97952266e+00,  5.14037465e+01],\n",
       "       [ 5.36107834e+01,  7.28164661e+00],\n",
       "       [ 1.34179054e+01,  3.77833703e+01],\n",
       "       [ 6.10844188e+01,  3.05576531e+00],\n",
       "       [ 3.48575325e-01,  4.97113731e+01],\n",
       "       [ 3.98457506e+01,  2.10540802e+01],\n",
       "       [-1.51945356e+00,  5.36429720e+01],\n",
       "       [ 5.28731796e+01,  1.39620224e+01],\n",
       "       [ 4.51934486e+00,  4.92827341e+01],\n",
       "       [ 4.10531324e+01,  2.26207789e+01],\n",
       "       [-1.19521829e+00,  6.58442298e+01],\n",
       "       [ 6.30976204e+01,  1.88642286e+01],\n",
       "       [ 6.56760207e+00,  6.87450641e+01],\n",
       "       [ 5.83530721e+01,  3.10169261e+01],\n",
       "       [ 1.99093546e+01,  6.64464251e+01],\n",
       "       [ 5.85218583e+01,  3.83438527e+01],\n",
       "       [ 2.09811054e+01,  7.93751463e+01],\n",
       "       [ 7.24488265e+01,  4.18088331e+01]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = PCA(n_components=2).fit_transform(data)\n",
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6512e6af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(values[:,0],values[:,1])\n",
    "plt.title('Principal Component Analysis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3121e348",
   "metadata": {},
   "source": [
    "***The features are reduced to 2 components by Feature reduction using PCA (Principal Component Analysis) algorithm.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e4c648",
   "metadata": {},
   "source": [
    "## Clustering Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ced0ce",
   "metadata": {},
   "source": [
    "## KMeans Algorithm with PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73940c4d",
   "metadata": {},
   "source": [
    "### Finding Optimum K value for the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1e7086",
   "metadata": {},
   "source": [
    "### Elbow method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2214695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow curve')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distorsions = []\n",
    "for k in range(2,10):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(values)\n",
    "    distorsions.append(kmeans.inertia_)\n",
    "    \n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(2, 10), distorsions)\n",
    "plt.grid(True)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Distorsions')\n",
    "plt.title('Elbow curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff136112",
   "metadata": {},
   "source": [
    "**We can observe that from k=3 there is an elbow shape with distorsions decreasing.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d4960d4",
   "metadata": {},
   "source": [
    "### Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5d1bab46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 and The average silhouette_score is : 0.342494700011012\n",
      "For n_clusters = 3 and The average silhouette_score is : 0.4510525443868883\n",
      "For n_clusters = 4 and The average silhouette_score is : 0.4991495294931792\n",
      "For n_clusters = 5 and The average silhouette_score is : 0.5526250183600322\n",
      "For n_clusters = 6 and The average silhouette_score is : 0.5364302947370859\n",
      "For n_clusters = 7 and The average silhouette_score is : 0.5232200976140171\n",
      "For n_clusters = 8 and The average silhouette_score is : 0.44900520464470306\n",
      "For n_clusters = 9 and The average silhouette_score is : 0.45288244094337426\n",
      "For n_clusters = 10 and The average silhouette_score is : 0.453460946296433\n"
     ]
    }
   ],
   "source": [
    "for n_clusters in range(2,11):\n",
    "    clusterer = KMeans(n_clusters=n_clusters)\n",
    "    cluster_labels = clusterer.fit_predict(values)\n",
    "    silhouette_avg = silhouette_score(values, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\"and The average silhouette_score is :\", silhouette_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc9d227f",
   "metadata": {},
   "source": [
    "***From the silhouette scores we can see that k=5 has a good silhouette score.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081fe049",
   "metadata": {},
   "source": [
    "**Interpretation**: On considering both elbow curve and silhoutte scores we can set cluster size (k) as 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15af6d4c",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0b9a1693",
   "metadata": {},
   "outputs": [],
   "source": [
    "KMeans_model = KMeans(n_clusters=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43eb035",
   "metadata": {},
   "source": [
    "### Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "271953fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KMeans_model.fit(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361ff3a4",
   "metadata": {},
   "source": [
    "### Model Predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3a9a101f",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_pred = KMeans_model.predict(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6e07a887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -4.41034605,  -3.10054946],\n",
       "       [-10.22346234,  42.33151065],\n",
       "       [ 41.55109062,   1.86988377],\n",
       "       [  4.98429835, -46.67348852],\n",
       "       [-44.38700009, -10.04934895]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster centroids\n",
    "\n",
    "cluster_centroid = KMeans_model.cluster_centers_\n",
    "cluster_centroid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ceed2c",
   "metadata": {},
   "source": [
    "### Silhouette Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6805b251",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score of KMeans model = 55.0 %\n"
     ]
    }
   ],
   "source": [
    "# Calculating Silhouette score\n",
    "kmeans_score = np.round(silhouette_score(values,k_pred)*100)\n",
    "kmeans_score\n",
    "\n",
    "print(f'Silhouette score of KMeans model = {kmeans_score} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb903366",
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Kmeans_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Kmeans_pred\n",
       "0   19                  15                      39            4\n",
       "1   21                  15                      81            3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the predictions along with the data in new dataframe\n",
    "df_pred = pd.DataFrame(data,columns=['Age','Annual Income (k$)','Spending Score (1-100)'])\n",
    "df_pred['Kmeans_pred'] = k_pred\n",
    "df_pred.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593a853d",
   "metadata": {},
   "source": [
    "### Visualizing the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ca611d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(data[k_pred == 0, 1], data[k_pred == 0, 2], s = 50, c = 'red', label = 'Standard customers')\n",
    "plt.scatter(data[k_pred == 1, 1], data[k_pred == 1, 2], s = 50, c = 'blue', label = 'Careful customers')\n",
    "plt.scatter(data[k_pred == 2, 1], data[k_pred == 2, 2], s = 50, c = 'green', label = 'Target customers')\n",
    "plt.scatter(data[k_pred == 3, 1], data[k_pred == 3, 2], s = 50, c = 'cyan', label = 'Careless customers')\n",
    "plt.scatter(data[k_pred == 4, 1], data[k_pred == 4, 2], s = 50, c = 'magenta', label = 'Sensible customers')\n",
    "plt.title('Clusters of customers using Kmeans with PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e9d92f",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The green cluster indicates the target customers whose Annual income and spending scores are high.\n",
    "- The red cluster indicates the Standard customers whose Annual Income and Spending scores is moderate.\n",
    "- The blue cluster indicates Careful customers whose Annual Income is high but Spending Score is low.\n",
    "- The cyan cluster indicates the Careless customers whose Annual income is low but Spending score is high.\n",
    "- The magenta cluster indicates the Sensible customers whose Annual Income and Spending Scores are low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f44017",
   "metadata": {},
   "source": [
    "## KMeans without Dimension reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06f0e73",
   "metadata": {},
   "source": [
    "### Elbow method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6309b435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Elbow curve')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distorsions = []\n",
    "for k in range(2,10):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(data)\n",
    "    distorsions.append(kmeans.inertia_)\n",
    "    \n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(2, 10), distorsions)\n",
    "plt.grid(True)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Distorsions')\n",
    "plt.title('Elbow curve')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3525858",
   "metadata": {},
   "source": [
    "***From k=3 there is an elbow curve decrease of distorsions.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731e9d63",
   "metadata": {},
   "source": [
    "### Silhouette Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3daa5611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2 and The average silhouette_score is : 0.293166070535953\n",
      "For n_clusters = 3 and The average silhouette_score is : 0.3839349967742105\n",
      "For n_clusters = 4 and The average silhouette_score is : 0.40546302077733304\n",
      "For n_clusters = 5 and The average silhouette_score is : 0.44428597560893024\n",
      "For n_clusters = 6 and The average silhouette_score is : 0.4523443947724053\n",
      "For n_clusters = 7 and The average silhouette_score is : 0.44125523526699084\n",
      "For n_clusters = 8 and The average silhouette_score is : 0.42790288922594905\n",
      "For n_clusters = 9 and The average silhouette_score is : 0.4134438886098646\n",
      "For n_clusters = 10 and The average silhouette_score is : 0.3814902498722627\n"
     ]
    }
   ],
   "source": [
    "for n_clusters in range(2,11):\n",
    "    clusterer = KMeans(n_clusters=n_clusters)\n",
    "    cluster_labels = clusterer.fit_predict(data)\n",
    "    silhouette_avg = silhouette_score(data, cluster_labels)\n",
    "    print(\"For n_clusters =\", n_clusters,\"and The average silhouette_score is :\", silhouette_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c63bca",
   "metadata": {},
   "source": [
    "***The Silhouette score is high when clusters are set as 5 and 6.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d790f44c",
   "metadata": {},
   "source": [
    "**Interpretation**: On comparing both elbow and silhouette score for the model we can can set n clusters as 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e433f088",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6063b1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_model1 = KMeans(n_clusters=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2332f315",
   "metadata": {},
   "source": [
    "### Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7dd971ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=5)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_model1.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff123de",
   "metadata": {},
   "source": [
    "### Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "99e7c346",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4,\n",
       "       2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4,\n",
       "       2, 4, 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, 3, 1, 0, 1, 3, 1, 3, 1,\n",
       "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 0, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
       "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
       "       3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1, 3, 1,\n",
       "       3, 1])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_pred1 = kmeans_model1.predict(data)\n",
    "k_pred1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7badaf",
   "metadata": {},
   "source": [
    "### Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "45a913dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score of KMeans model = 44.0 %\n"
     ]
    }
   ],
   "source": [
    "kmeans_score1 = np.round(silhouette_score(data,k_pred1)*100)\n",
    "kmeans_score1\n",
    "\n",
    "print(f'Silhouette score of KMeans model = {kmeans_score1} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75567ae9",
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Kmeans_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Kmeans_pred\n",
       "0   19                  15                      39            2\n",
       "1   21                  15                      81            4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding predicted values to dataframe\n",
    "df_pred1 = pd.DataFrame(data,columns=['Age','Annual Income (k$)','Spending Score (1-100)'])\n",
    "df_pred1['Kmeans_pred'] = k_pred1\n",
    "df_pred1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6f3cac",
   "metadata": {},
   "source": [
    "### Visualizing the clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3ccf8dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K7/oux+VW+ycRo4CjnIMSkWvKW/4D77V8ARenNgWfEXp+z/d7Pb1rdYP3HtyBq1S8g0tV8a2w7a7z3mvf4D53pxKRU43on7/Qcbt6j0d6z13vXZtVuJQaewW4Lt/x9vVUxPJ3iPhe8pbfQdj73lt2Dq4SVItPHrKAr21kHrLnaZyHLO770NuuGPgLu7tDVyfz3oiybczvdNz3TD1waIzP0icEGBlst6bdQkOQjTHGpJm4uQY/BH6tqr/KdnmMMbnDKmTGGJMGXpzX/biYty+Bw4GbcUH9parqN0rPGNNCWQyZMcakxy7cqLsHcbFN23GpOy60ypgxJpK1kBljjDHGZJmlvTDGGGOMyTKrkBljjDHGZFlex5B17NhRu3btmu1iGGOMMcbEtXjx4i9VtZPfuryukHXt2pVFi3xnjjDGGGOMySki8nG0ddZlaYwxxhiTZVYhM8YYY4zJMquQGWOMMcZkmVXIjDHGGGOyLG0VMhF5TETWi8iysGX7iMgrIrLSu987bN14EflQRFaIyLB0lcsYY4wxJteks4VsCnBGxLJxwGuq2h14zXuMiBwDfA8o9Z7zsIi0SmPZjDHGGGNyRtoqZKo6D9gUsfhs4Anv7yeAc8KWP6WqO1V1FfAhcFy6ypYx1cBkYKx3X53d4hhjjDEmN2U6D9n+oUl1VXWdiOznLe8C/CNsuzXesvw1HxgO1OOmFC4GbgRmASdmsVzGGGOMyTm5EtQvPst8Zz0XkStEZJGILNqwYUOai5WkalxlrBpXGcO7Dy3flqVyGWOMMSYnZbpC9oWIdAbw7td7y9cAB4dtdxCw1m8HqvqIqlaoakWnTr6zD2RfFa5lzE+9tz4o6/Y0xhhjmr1MV8heAEZ6f48Eng9b/j0RaSMihwHdgbcyXLbUWcnulrFI23ERckHMx3XcjgHu8e67eMuNMcYY02ykLYZMRP4CDAE6isgaYAIwEXhaRC4DPgEuBFDV5SLyNPAeUAdco6q70lW2tOuOixnzq5QVA90C7CO82zMktL/huPbDDk0oozEmrupqqKqClSuhe3eorISSkmyXqvmy621aMlH1DdXKCxUVFZqTk4tX41qy/LoXSwhWmZqMaxGLVqmbBFyWfBGNMbHNnw/Dh0N9PWzfDsXFUFAAs2bBiTYwJ+XsepuWQEQWq2qF37pcCepvXkpwoylLcJUnvPvQ8iAtW6nq9jTGJKy62lUOqqtd5QDcfWj5NhuYk1J2vY2xCln6nIhrCZuES387yXsc9JdeqNvTT9BuT2NMUqqqXEuNn/p6t96kjl1vYzKfh6xl6UDy3YqVuLxlfgq89caYtFi5cndLTaTt2+FDa6FOKbvexliFLHeFujcjk8sWELzb05hmLl1B4N27uxgmv0pCcTF0y3ILdXMLfs/1621MJlhQf67bhstb9iGum7ISq4wZQ3qDwKuroUsXdx+ppATWroUOWfocNsfg91y+3sakUqygfquQGWPyTib+gedixac5V1xy8Xobk2qxKmTWZZlK1bjWrJW4oPxKXNejMSalggSBX9bEtDAnnugqOFVVLoapWzfXNZjNCk8mzjtbcvF6G5NJViFLFZtM3JiMyVQQeIcOuVXBae7B77l2vY3JJKuQpYJl1Tcmo1IVBJ6J4PhUHsOC341pviyGLBUsq74xGZWKWKpMxCyl+hjNOYbMmJbAMvWnm2XVNyajSkpcpaakxFVywN2HlserlGQiM3w6jtHU8zbG5C7rskyFVEwmnu9SPaDBBkiYOJoSBJ6J4Ph0HcPvvIcPh5kz4a9/bR55yYxpiaxClgotPat+qgc02AAJE1CyQeCZCI5P5zHCz3v+fOjRY89u0RtvtHQRxuQb67JMhVRMJp6vwgc0hP75bA9bnmi3TKr3Z4yPUHC8n1QFx2fiGDYptzHNh1XIUqWpk4nnqypcS5afem99Nvdn8lZ1NUyeDGPHunu/QPZkVVa64Ho/BQVufT4cwyblzk/pfG+b/GVdlqnUlMnE81WqBzTYAAmD/+jEVHbDhYLgo42ATEVwfCaO0dzzkjVH6X5vm/xlFTLTNKke0GADJFq88G64kFClY/jw1KV2SHZQQCJ5xdKdfT6Tecma24Tm2ZCp97bJT5aHzDRNNdCFPZPihpSQeFLcVO/P5J3Jk2HMmOiVjEmTspfNPdfmW8xUXrJcO+98lcvvbZMZlofMpE+qBzS05AESBsjdbrhcDKDPRF6yXDzvfJWr722TG6zL0jRdaEBDFS7Gqxsu1Uey/wxSvT+TV3J1eqBcndg73d2iuXre+ShX39smN1iFzKRGqgc0tMQBEgZwlYkbo+T1S9XoxGTkcutGOiflzuXzzje5+t42ucEqZMaYnJLK0YmpDEQ/+ODY6w86KLn95rp0tuq0tIECmRh5a/KXVciMMTknFd1wll4gNdLVqtNSX590dzGb/GUVMmNMTmpKN1w60gt89FHs9f/5T2L7yxfpaNVp6ekf0tnFbPKXVciMTeRtmp1kA9FjdaFt2hT7mBs3Nq3MuSzVrTo2UMCYxqxC1tLZRN6mGUomED1eF9ree8c+5r77Nr3cuSyVrTo2UMCYxqxC1pKFT+QdEvqSHI4lYTV5K9FA9CBdaD17Qtu2sGNH4322bQvHHJO68jdVrgbLh8r1zjvQpg3s3Nl4G0v/YFoqSwzbktlE3qaZSnRi7yBdaJWVUFjov01hYe6kLJg/32XvHzMG7rnH3Xfp4pbnSrleftm/MgaW/sG0XFYha8lsIm/TTCWawT5IF1omsuI3Va5m1fcrV6Rcu5bGZJp1WbZkNpG3acYSCUQP2sWZ6ykLcjVYPla5iopg6FA4//zUXMtc7a41Jh6rkLVklbgAfj8F3npj8ljQQPREcm3lcsqCXA2Wj1Wumhro3Ts117Sl5jYzzYN1WbZkNpG3MUB+dEcGEWrp85PNYPlMlCtXu2uNCUpUNdtlSFpFRYUuWrQo28XIf9uwibyNwf3TztXuyCCqq13gfPho0ZCSkuwlXM1EuSZPdgMGonU7T5qUuy2bpuUQkcWqWuG3zrosjU3kbYwnl7sjg8jVuRIzUa5c7a41JiirkBljTBPkWhB5rg48SHe50jkJumnecuUzbF2WxhiTJL8g8lCrjwWRZ1audtea3Jbpz3CsLksL6m+JqoHJwFjv3ucLzBgTmwWR55bmMjDDZE6ufYaty7KlsbkrjUmJXM351ZLlanetyU259hm2CllLYnNXGpMyFkSem/J9YIbJnFz7DFuXZSzNrWsvnXNXNrdrZUwcuZrzyxgTTK59hq1CFs18oAswBrjHu+/iLc9X6Zq7sjleK2PiSHQCc2NMbsm1z3BWKmQi8hMRWS4iy0TkLyLSVkT2EZFXRGSld793NsoG7Nm1F6rAbA9bnq/BuqG5K/0kO3dlc71WxsRhQeTG5Ldc+wxnPIZMRLoA1wPHqOo3IvI08D3gGOA1VZ0oIuOAcbgOsMwL0rWXjzEK6Zi7srleK2MCsCByY/JbLn2GsxXU3xpoJyK1QHtcOPl4YIi3/glgDtmqkKWray/bQnNURo6yLCD5uSub67UyJiALIjcmv+XKZzjjFTJV/UxE7gM+Ab4BZqvqbBHZX1XXedusE5H9Ml22BqGuPb+KRrJde+C68apwlZjuuBapTGcDPhFX/U3V3JXpulbGGGNSJley0ZvoMp6p34sNm46rBmwGngGmAQ+q6rfDtvtKVRvFkYnIFcAVAIcccki/jz/+OPWFrMYFpfuNFCwhufQQfvm/Qi1T+Zz/Kx3XyhhjTMrYjBK5I9cy9Z8KrFLVDapaCzwLHA98ISKdAbz79X5PVtVHVLVCVSs6deqUnhKGuvZK2B0EXxy2PNEKRnMOfE/1tTImx1VXw+TJMHasu/ebqseYXJFr2ehNdNmIIfsEGCAi7XFdlkOBRbgqykhgonf/fBbKtlsqu/aae+B7qrtBjclRfi0NN95oLQ0md+VaNnoTXTZiyBaKyDRgCVAH/BN4BPfv+2kRuQxXabsw02VrpAOpqSi1hMD3VF0rY3JUeEtDSKjFYfhwm7za5KZcy0ZvosvKKEtVnQBMiFi8E9dalruSDcq3wHdjmizbQcnW0mDyUSgbvV+lzGaUyC2WqT+opmSjrwSijZ1Qksv/ZUwLMn8+dOkCY8bAPfe4+y5d3PJMsZYGk49yLRu9ic4qZEHECso/FZdsNd78jbEqZMaYqHIlKDnX5r0zJohcy0ZvorMKWRCxgvJ3Ag8Qu8WsiuhXuoCmTeptTDMXpKswEyorIVqWIFVraTC5K5SNftIkGDfO3a9dawNRck22MvXnl1hB+SGh9cNpnHurJQT1G5MmudRVGKtCZkwuy5Vs9CY6ayELItak3JFCaSyCPt+C+p1qXLfvWOJ3/5oWJVZXYbt2MHcu9O8PI0e6X/3pUlUVOxYnnS118XKfWW40Y/JfxjP1p1JFRYUuWrQo/QeKlY3ezzjg7oDPt2z2zXcWA5MS1dUugD9oJeOhh+Dqq1NfjrFj3YCCaMaNg7vvjr4+WfGyrFsWdmPyR65l6s8/ftnoo/Fr8bJs9tE151kMTEr4BSW3axd9+2uugc8/T82xw1uePv8c2rf33y5dQf3xBjSsW5cbAx6MAWupbSqLIQsqPBv9cuAPwA6f7QrwT2Nh2ez9NfdZDExKhIKSq6pczNjcubBgQfTtx42DKVOadszIlqf27eHrr/23TVf6gHgDGsaNs9xoJjfYLBZNZxWyRIRnoz+P6N1s0SpZls2+MRvwYAIKD0ru3z/2titWNO1Yfln5wytjoUSb4d2D6UgfEG9Aw4oVuTPgwbRcNotFaliFLFnZavGKnC1gOK4SmOjsAYkcI5l9Bt2HzWJgknDUUfDWW9HX9+jh7pPN7h+rZapdO+jVy63v0QMmToTOnRM/hyDiZVnv0QOWLbMs7Ca7bBaL1LCg/nwSGfzeFtdtGrpPRTB8KgLsE9mHDXgwSVi71gX6R7NunWsdSjbYPV4Af2Eh1NamP4A+1oCGkhLXQtajR/T11jJhMiFbA17ykQX1Z0sqUzn4Bb/viLhvajB8KgLsE92HDXgwSTjwQDea0s9DD7mKUrRg91NPdbEtsYKOY6XaAFcZC99nugLo42VZ79zZsrCb7LNZLFLDWsjSJdWpHCbjZgOIl6AW71iTSDxeLdYxgu4z2X1swwY8mIR9/rn79R1qKZo4EQ44wFW2xoyJHl8FsVu3Ek21UVzssp+nq1tm27bdAxq6dXNdr+GVrXjrjUmneC251lK7W6wWssAxZCJSDOxQ1V0pK1lzFd5KFBIrk38QQWYLCD9WMsG8qQiwT3YfNuDBJOGAA/xHU8YKhg+JFXQcamEK7/Js3Rrq6qLvK50B9PGyrFsWdpNNfp+XdA94aY6iVshEpAD4HnAxcCxu1sY2IrIB187ziKquzEgp8006UjnECn6PlGwwfCoC7C1I32RRKIj/nXegTRvYuTP+c6IFHUem2li7FqZPtwB6Y/xEfl6spTZxUbssRWQu8CrwPLBMVeu95fsAJwMXATNU9ckMlbWRnO2yHAvECHBslMk/iERmC0g2GD4VAfYWpG+yJDIPUiKCBB1bt4wxpqmSDeo/VVV/parvhipjAKq6SVWnq+r5NJ610UB65q70C35vG3Hf1GD4VATYW5C+yQK/jPZBBW3dihdgb5UxY0xTRO2yVNVaABHZH9fmocBaVf0ichsToRK4Mcq6aJn8g/DLfTYCmEnqguFTkV/NZiUwGRYrD1JREQweDG++CTt8ZtdIJMu+dcsYY9IlVgxZH9wEQd8CPvMWHyQim4GrVXVJ+ouXp0KtQYlm8g/CL/g91cG8qQiwtyB9k0GxgvhraqBfP7j99tQEHVsAvTEmHWKNsnwcuFJVF4YvFJEB3rre6SxY3rNWImMyJl5G+27dWlbrVrIzFDQHLfncTX6LFdS/UlW7R1n3oapmfUxRzgb1G2MyygLud/Ob5DmdswnkkpZ87iY/JJuH7EURmQn8CfjUW3Yw8APgpdQW0Rhjkmd5kJyWPMlzSz530zzECuq/XkS+A5yNC+oXYA3wkKrOylD5jDEmkJbUJRlNc5/kOVZ3ZHM/d9P8xczUr6ovAi9mqCzGGNMkLT3gPtbghnTPJpBuft2RN964uzuyOZ+7aRlijbJsjRsndw5haS9wiWL/11JeGGNMbgkyuCEfBemObK7nblqOWIlh/wyUA7/AJXAY4f3dG8hadn5jjDH+Kitd3JyfRPKt5Zog3ZHN9dxNyxGry7KvqvaIWLYG+IeI/DuNZTLGGJOE5jq4IUh3ZHM9d9NyxKqQfSUiFwLTw+axLAAuBL7KROFahGpcrrKVuCmXKnGJZY0xJgnNcXBD0O7I5njupuWIlYesK/DfwCnsroB9G3gDGKeqqzJQvpjyPg/ZfKJn87ecOcYYA1ieOdN8JJWHTFVX4826KCL74ipvX6alhC1RNa4yFv4FE/r1Nxw3fMK+YHazlkRjWizrjjQtQcy0FyGqujH8sYicpqqvpKdILUQVrmXMT723vgUP39+DX0vijVhLojEtiHVHmuYuUIXMx/8Ch6SyIC3OSna3iEXajpv/0lhLojGmQUvPM2eat1h5yF6ItgrYNz3FaUG641p6/CplxbjJyI21JBpjclZtbS1r1qxhx44d2S6KyTFt27bloIMOorCwMPBzYrWQnQRcAmyLWC7AcYkXz+yhEtft5qfAW2+sJdEYk7PWrFlDSUkJXbt2RUSyXRyTI1SVjRs3smbNGg477LDAz4tVIfsH8LWqzo1cISIrkiijCVeCi4GKNsrSuuEca0k0Jq5Yczya9NmxY4dVxkwjIsK+++7Lhg0bEnperFGW34mxblBCRzH+TsTFQFXhWnq64VrGrDK2m7UkGhNTvDkeTXpZZcz4SeZ9EWvqJL8DnJnwEUxsHXAxUHd791YZ21OoJbEE1yKGdx9abtfLtGDhczyGkqZu3757+bbIgBPT7Pz617+mtLSUXr16UV5ezsKFCwH47W9/y9dff52y43Tt2pUvv0w+89WUKVO49tprU1aecEuXLmXWrFlp2XcmJVQhA36ZllIYE0uoJXESMM67X4ulvDAtXpA5Hk0Oqa6GyZNh7Fh375fpNgELFizgb3/7G0uWLOHdd9/l1Vdf5eCDDwZSXyFL1K5duzJ2rGxUyOrq6lK+z0QrZNY2a7LDWhKNaSTIHI8mR8yf76YbGDMG7rnH3Xfp4pYnad26dXTs2JE2bdoA0LFjRw488EB+97vfsXbtWk4++WROPvlkAK666ioqKiooLS1lwoQJDfvo2rUrEyZMoG/fvpSVlfHBBx8AsHHjRk4//XT69OnDlVdeSfisPueccw79+vWjtLSURx55pGF5hw4duP322+nfvz8LFizg8ccf58gjj2Tw4MH8v//3/3zPYdu2bYwePZqysjJ69erF9OnTG/YVMm3aNEaNGgXAM888Q8+ePenduzeDBg2ipqaG22+/naqqKsrLy6mqqmLTpk2cc8459OrViwEDBvDuu+8CcMcddzBy5EhOP/10unbtyrPPPsvNN99MWVkZZ5xxBrW1tQAsXryYwYMH069fP4YNG8a6desAGDJkCLfccguDBw9m0qRJjcrSZKoa+AYcl8j26b7169dPTRK2quqjqnqzd781u8UJJB/LbEyaPfqoanGxKjS+FRerTp6c7RI2b++9916wDbduVS0p8X+hSkpUq6uTOn51dbX27t1bu3fvrldddZXOmTOnYd2hhx6qGzZsaHi8ceNGVVWtq6vTwYMH6zvvvNOw3e9+9ztVVX3ooYf0sssuU1XV6667Tn/xi1+oqurf/vY3BRr2F9rX119/raWlpfrll1+qqiqgVVVVqqq6du1aPfjgg3X9+vW6c+dOPf744/Waa65pdA4333yz3nDDDQ2PN23apKqqxcXFDcueeeYZHTlypKqq9uzZU9esWaOqql999ZWqqj7++ON77Pvaa6/VO+64Q1VVX3vtNe3du7eqqk6YMEFPOOEEramp0aVLl2q7du101qxZqqp6zjnn6IwZM7SmpkYHDhyo69evV1XVp556SkePHq2qqoMHD9arrrqq4Th+ZQnn9/4AFmmUOk1CLWSq+ha4TP1NrwqarJgPdAHGAPd491285bkqH8tsTAZUVrrpg/wUFLj1JgekqW+5Q4cOLF68mEceeYROnTpRWVnJlClTfLd9+umn6du3L3369GH58uW89957DevOO+88APr168fq1asBmDdvHpdccgkAI0aMYO+9927Y/ne/+x29e/dmwIABfPrpp6xcuRKAVq1acf755wOwcOFChgwZQqdOnSgqKqIyypvx1Vdf5Zprrml4HH4cPyeccAKjRo3i0UcfjdotOn/+fC699FIATjnlFDZu3MiWLVsA+M53vkNhYSFlZWXs2rWLM844A4CysjJWr17NihUrWLZsGaeddhrl5eXceeedrFmzpmHf4ecRpCyJsEz9LUk+Zr3PxzIbkyE2x2OeSGPfcqtWrRgyZAhDhgyhrKyMJ554oqF7L2TVqlXcd999vP322+y9996MGjVqj2S2oS7PVq1a7REb5TdScM6cObz66qssWLCA9u3bM2TIkIZ9tW3bllatWsV8fiRV9d0ufFl4Wf/4xz+ycOFCZs6cSXl5OUuXLvXdZ7T9hc61oKCAwsLChuUFBQXU1dWhqpSWlrJgwQLf8hYXFzf87VeWffdNPm9+1BYyEXkhyu2vNDFTv4h8W0SmicgHIvK+iAwUkX1E5BURWendx64mm8QFyXqfa5pS5sgA2rVrUxpQmzdSHEhscktojsdJk2DcOHe/dq2lvMgp3bu7mrKf4mI3MWcSVqxY0dA6BS64/dBDDwWgpKSEau+zvnXrVoqLi/nWt77FF198wYsvvhh334MGDWLq1KkAvPjii3z11VcAbNmyhb333pv27dvzwQcf8I9//MP3+f3792fOnDls3LiR2tpannnmGd/tTj/9dB588MGGx6Hj7L///rz//vvU19czY8aMhvUfffQR/fv355e//CUdO3bk008/3eNcI8s+Z84cOnbsyF577RX3nAF69OjBhg0bGipktbW1LF++3Hdbv7I0RbYy9U8CXlLVC0SkCGgP3AK8pqoTRWQcbjzd2CYex4TLx6z3yZY5MjlT27Zw+eXufseOlpOsyZJUtQg2x2OOq6x0nzs/Tehb3rZtG9dddx2bN2+mdevWdOvWrSHI/oorruA73/kOnTt35o033qBPnz6UlpZy+OGHc8IJJ8Td94QJE/j+979P3759GTx4MIcc4jrFzjjjDP74xz/Sq1cvevTowYABA3yf37lzZ+644w4GDhxI586d6du3r2+33s9//nOuueYaevbsSatWrZgwYQLnnXceEydO5Mwzz+Tggw+mZ8+ebPNyuNx0002sXLkSVWXo0KH07t2bQw45hIkTJ1JeXs748eO54447GD16NL169aJ9+/Y88cQTga9pUVER06ZN4/rrr2fLli3U1dUxZswYSktLG23rV5amEL+mPQAReRG4R1Xf8Fk3T5NMDisiewHvAIdr2MG97P9DVHWdiHQG5qhqj1j7qqio0EWLFiVTjJZpMi7+KlrW+0nk3ryQyZS5utqNXgrSElRS4poTgvbtRKZEHzQIfv1r+OADOOoouPtuOPDAYPtKt1jXoannHSQVvKWPN83c+++/z9FHHx1sY78fR6G+Zftx1Cz5vT9EZLGqVvhtn41M/YcDG4DHRaQ3sBi4AdhfVdd5+18nIvs14RjGTz5mvU+mzLECaCOFAmqDNC9EfqEWFUFNze71b70Ff/oTPPQQXH11sOOnU5BA4mTOO0grm7XMGbOnUN9yVZWLGevWzf1IsUA/44laIRMR0WjNZwlsE+WYfYHrVHWhiITSfQYiIlcAVwANTagmoHycPzOZMscKoI0UNKA2PCV6SHhlLNw118B558EBBwQrQ7qkIpDY77xD+xw+3L+VLZnnGNMSWN+yiSFW2os3ROQ6Edmj1iMiRSJyiog8AYxM4phrgDWqutB7PA1XQfvC66rEu1/v92RVfURVK1S1olOnTkkcvoXLx6z3iZY5VgBtpKABtYm0uoGLrs6WUBD/O++AN6KokVScd7Th+qka4m+DEYwxLUisoP4zgB8CfxGRw4DNQFugFTAbeEBVlyZ6QFX9XEQ+FZEeqroCGAq8591GAhO9++cT3bcJKJT1Pp8kUuZYAbSRggbUJtLqBrBiRfBtUymyqzCaVJx3tFa2VLTMWZenMaaFiRVDtgN4GHhYRAqBjsA3qro5Bce9DpjqjbD8DzAa11r3tIhcBnwCXJiC45iWyC85U2h0Zfgoy0SSNYVa3YJWynrEHI+SmKDB8X5dhZFSed7RWtmCPifaeVmXpzGmBYo6yjIf2ChLE9O2bXsG0I4YATNnJhdQm8jITYB161ITQ5bIyKzJk938eH4VoaIiGDoUzj8/decdbaRmkOcsXRr9vD74IPp5FBe7RFsWh2NyQEKjLE2Lk7JRlsbkPb8A2mT/kfu1ukWOsgx56KHUVMYSbSmK1VVYUwO9eyd+/smkgo/3HNXY5/WjH9mM2cbEsXHjRoYOHQrA559/TqtWrQjFVb/11lsUFRWl7FibN2/m//7v/7g6jaPH77rrLm655Za07T8fWIXMmFgiu9VWrHCVilAr25Ah8KtfueU9esDEiXtWxpqSiyvRtBXJdC8GceKJ7vzGjdvzPDt3jv2caEP8J0+OfV4bN6bnPIzJslSm5tt3330bpg2644476NChAz/72c/iPq+uro7WrRP7179582YefvjhZlUhS+Y6pF20WcfDb8ChwKne3+2AkiDPS/etX79+jWZSNyZl3nxTtaREtbhYFdx9SYlbnonn33yze16027hxe26/davbv9+2JSWq1dWJnX+qziPR8/rJT9JzHsak2HvvvRd421R/jMJNmDBB7733Xn3kkUe0oqJCe/Xqpeedd55u375dVVVHjhypP/nJT3TIkCF644036ocffqj9+/fXiooKve2227S4uLhhX/fcc49WVFRoWVmZ3n777aqqWllZqW3bttXevXvrz372s0bHf+KJJ7SsrEx79eqll1xyScMxn3nmmYZtQsdYu3atnnTSSdq7d28tLS3VefPm6dixY7WgoEB79+6tF110kaqq/uY3v9HS0lItLS3VBx54QFVVV61apT169NDLLrtMS0tL9aKLLtJXXnlFjz/+eO3WrZsuXLhQVVW3bdumo0eP1oqKCi0vL9fnnntOVVUff/xxveCCC/TMM8/Uk08+2bcsqeT3/gAWabS6VrQVDRvA5cDbwEfe4+64KY6sQmaya+tW1Ucfdf/gH33UPU7lvpOpFITKdMMNqm3bNq1S8eiju7+9I2/FxaqTJzd+zptvqnbooFpU5LYrKnKPI7/1g167dFTyfv/72BWyBx9M738vY1IkaIUsXb+VQkIVsi+//LJh2a233qq/+93vVNVVjkaMGKF1dXWqqjpixAj9v//7P1VV/cMf/tBQWXr55Zf18ssv1/r6et21a5eOGDFC586dq6tWrdLS0lLfYy9btkyPPPJI3bBhg6qqbty4seGYfhWy++67T++8805VVa2rq9Ot3ndPeKVw0aJF2rNnT922bZtWV1frMccco0uWLNFVq1Zpq1at9N1339Vdu3Zp3759dfTo0VpfX6/PPfecnn322aqqOn78eP3zn/+sqqpfffWVdu/eXbdt26aPP/64dunSpaGM0cqSKolWyIK0112Dm7tyodeittKy6JusS3dahKoqqK31X1db65/lPmjKiaBZ8isr4YYb/Nepxk5bIbLnfaxyxrp2yVyHVLCs5qYZSdWkGfEsW7aMn//852zevJlt27YxbNiwhnUXXnghrVq1AmDBggU899xzAFx00UUNXZ2zZ89m9uzZ9OnTB3BzZa5cuTJmEvbXX3+dCy64gI4dOwKwzz77xCzjscceyw9/+ENqa2s555xzKC8vb7TN/PnzOffccyn28kmed955vPnmm5x11lkcdthhlJWVAVBaWsrQoUMREcrKyli9enXDebzwwgvcd999AOzYsYNPPvkEgNNOO62hjEHKkklBKmQ7VbVGvC92EWkN5O/QTJP/MpEWYdkylx7Dz44d8N578csUTSKB6Rrlo+a3PFQGbxJeAHbudLfQddE4AfWR12758sSuQxCffhp7/Zo17t6ymptmIhWp+YIYNWoUzz33HL1792bKlCnMmTOnYV1xgGTZqsr48eO58sor91gequhEe474/PBr3bo19V4tVFWp8QZADRo0iHnz5jFz5kwuvfRSbrrpJn7wgx802mc0bcKSXRcUFDQ8LigooK6uruH506dPp0dE+qGFCxfucR2ClCWTYmXqD5krIrcA7UTkNOAZ4K/pLZYxMSSbCT6RzO9ffRW7DBs3Bi9TpESy5BdE+YgWFDQ+zyDXJdFrt2lT7DLOnp14Fv1YMylY0L5phjL1lq+urqZz587U1tYyderUqNsNGDCA6dOnA/DUU081LB82bBiPPfYY27wfdZ999hnr16+npKSE6iif8aFDh/L000+z0ftO3OR9Z3Tt2pXFixcD8Pzzz1PrtbR//PHH7Lffflx++eVcdtllLFmyBIDCwsKGbQYNGsRzzz3H119/zfbt25kxYwYnnXRS4OswbNgwfv/73zdU7P75z3/6bhetLNkSpEI2FjcZ+L+AK3EzCP48nYUyJqZkfm7On+9yY40ZA/fc4+67dHHL/cRpdmfffYOXKVK6suQH2T7Rfe69d+wyLlsW/1pGqqyMXdEMcm2MySOZesv/6le/on///px22mkcddRRUbf77W9/y/33389xxx3HunXr+Na3vgXA6aefzkUXXcTAgQMpKyvjggsuoLq6mn333ZcTTjiBnj17ctNNN+2xr9LSUm699VYGDx5M7969udGbJeXyyy9n7ty5HHfccXu0TM2ZM4fy8nL69OnD9OnTucELy7jiiivo1asXF198MX379mXUqFEcd9xx9O/fnx/96EcN3ahB3HbbbdTW1tKrVy969uzJbbfd5rtdtLJkTbTgMq9mWQAsi7VNNm8W1N9CJRrsnkxE7aOPRg/Kb9u28TFilSm8bIkEpgcJfk/0uiR67WJdh6ZEJ1vQvmkGcmWUZaK2b9+u9fX1qqr6l7/8Rc8666zMF6IFSDSoP2YLmarWA+9ETjBuTFYl+nMzmS7OykooLPR/TmFh42PEKlObNi5oftIkF6OVrrkYg1yXRK9drOsQKZGJw0NB+5Mmufxm6b42xmRZLr3lFy9eTHl5Ob169eLhhx/mN7/5TeYLYRoJEtTfGVguIm8BDX0dqnpW2kplTCyJZo9Pposz0WPE2z6Zb914kb7/+U9yZW7qeUWTaHSyBe2bFiZX3vInnXQS77zzTraLYSIEqZD9Iu2lMCZRQdIihNJiv/OOa6XaubPxfmJF1PodY/hwNx/mX//aONV2qlM1JDqwIGgZmnJe06fD668nfi2NMcbEFGhycRHZHzjWe/iWqq5Pa6kCssnFTVRBc4JFmyA7yD6b2gIWz09+Ar/9bfT1N94IqehqSOS8qqvd1FBff914P+3bwxdfWK4w02LY5OImlpRPLi4i/wXcC8wBBPi9iNykqtOaXlxj0iBITrB4E2SH76uqyo0m/J//2TMnV6iid+qpcPXVcMwxTZucLtIRR8Ref/jhTT9GMjndEsmNZowxJpAgXZa3AseGWsVEpBPwKmAVMpObYgXxFxXB0KFw/vnxuxODtrLt3AkPPJD62QIyIdEU4kFyo+VCkIwxxuSZIHnICiK6KDcGfJ4x2REriL+mBnr3dpWGeC1joZajoPnFtm/3z5afrESD+pORjlxnxpiM+fWvf01paSm9evWivLychQsXpnT/xx9/POBydp155pm+23Tt2pUvv/wypceNZ8qUKaxduzajx0y3IC1kL4nIy8BfvMeVwIvpK5IxTRRKi+1XcQgPPA91R65c2TiQPZHM+5FSNTldMkH9iQp6rZLZPtb1NaYFqt5ZTdXyKlZuXEn3fbtTWVpJSZvkPxMLFizgb3/7G0uWLKFNmzZ8+eWXDVMUpcrf//73lO4vVaZMmULPnj058MADM3K8hlxh0XoIUiDunlX1JuB/gF5Ab+ARVb05bSUypqmC5NqKl7k/kcz7kVLVUpTobAHJqKyM3WXpl5csVgxZaPtEZ0Ywppmb/8l8utzfhTEvjeGev9/DmJfG0OX+Lsz/JPnPxLp16+jYsWPDfI4dO3ZsqKAsXryYwYMH069fP4YNG8a6desAGDJkCGPHjuW4447jyCOP5M033wRg+fLlHHfccQ35yVauXAlAh7CehK1bt3LuuedyzDHH8OMf/7hhrspwTz75ZMN+rrzySnbt2tVom7fffpvjjz+e3r17c9xxx1FdXc2UKVO49tprG7Y588wzmTNnDrt27WLUqFH07NmTsrIyHnjgAaZNm8aiRYu4+OKLKS8v55tvvuG1116jT58+lJWV8cMf/pCd3kjwrl27cssttzBw4EAqKipYsmQJw4YN44gjjuCPf/xjw/Huvfdejj32WHr16sWECRMAN4fn0UcfzdVXX03fvn359NNPG5UlpaJljA3dgMOAtmGP2wFd4z0vEzfL1G+iipUWO0jm/iCZ94Nk0d+61e3r5pvd/datwc8h0dkCkrF1q2r79v7HKCpS/clP9iz31q2q7dr5b9+unbt2ycyMYEweCpqpf+uOrVpyV4lyB41uJXeVaPXO5D4T1dXV2rt3b+3evbteddVVOmfOHFVVramp0YEDB+r69etVVfWpp57S0aNHq6rq4MGD9cYbb1RV1ZkzZ+rQoUNVVfXaa6/VJ598UlVVd+7cqV9//bWqqhYXF6uq6htvvKFt2rTRjz76SOvq6vTUU0/VZ555RlVVDz30UN2wYYO+9957euaZZ2pNTY2qql511VX6xBNP7FHmnTt36mGHHaZvvfWWqqpu2bJFa2tr9fHHH9drrrmmYbsRI0boG2+8oYsWLdJTTz21YflXX33VcB5vv/22qqp+8803etBBB+mKFStUVfXSSy/VBx54oKFsDz/8sKqqjhkzRsvKynTr1q26fv167dSpk6qqvvzyy3r55ZdrfX297tq1S0eMGKFz587VVatWqYjoggULVFWjliWalGbq9zwDhFeDd3nLjMldsdJiBwlkj5d5/+ST45ehqa1Eic4WkIxY16Kmxg1WCC93kKD+ZCd/N6aZqlpeRb36fybqtZ6qZcl9Jjp06MDixYt55JFH6NSpE5WVlUyZMoUVK1awbNkyTjvtNMrLy7nzzjtZs2ZNw/POO+88APr168fq1asBGDhwIHfddRf//d//zccff0y7du0aHe+4447j8MMPp1WrVnz/+99nfsR32WuvvcbixYs59thjKS8v57XXXuM/EbGuK1asoHPnzhx7rMuktddee9G6dfToqcMPP5z//Oc/XHfddbz00kvstddejbZZsWIFhx12GEceeSQAI0eOZN68eQ3rzzrL5bEvKyujf//+lJSU0KlTJ9q2bcvmzZuZPXs2s2fPpk+fPvTt25cPPvigoYXw0EMPZcCAAYHL0hRBYshaq2pDp7Sq1ohIUUpLYUw6REuLHSQwPV7W+7/+Fd54I/qxP/oIxo9PLJ1EpERnC0jG8uV7pvLwE17uH/0o/rWLNSrVAv9NC7Ry40q21/p/JrbXbufDTcl/Jlq1asWQIUMYMmQIZWVlPPHEE/Tr14/S0lIWLFjg+5xQF2erVq2oq6sD4KKLLqJ///7MnDmTYcOGMXnyZE455ZQ9niciMR+rKiNHjuTuu++OWl5VbfQ8gNatW+/RBbrD+17ae++9eeedd3j55Zd56KGHePrpp3nsscca7TOW0PkWFBQ0/B16XFdXh6oyfvx4rrzyyj2et3r16oZJ0YOWpSmCtJBtEJGGaZJE5Gwgs8MpjEmlUGC6n/DA9FitbPH2sXFjalqJTjwRVqxwaTr693f3K1akLq3Gpk3Bt925E2bPdi2EfkLXLuj1NaaF6L5vd4oL/T8TxYXFdNsnuc/EihUrGlpyAJYuXcqhhx5Kjx492LBhQ0OFrLa2luXLl8fc13/+8x8OP/xwrr/+es466yzefffdRtu89dZbrFq1ivr6eqqqqjgx4nto6NChTJs2jfXrXWKGTZs28fHHH++xzVFHHcXatWt5++23Aaiurqauro6uXbuydOlS6uvr+fTTT3nrrbcA+PLLL6mvr+f888/nV7/6FUuWLAGgpKSEau8H71FHHcXq1av50Pux9+c//5nBgwcHu4jAsGHDeOyxx9jmjY7/7LPPGs4hXLSypEqQFrIfA1NF5EFcYthPgR+ktBTGZFJlpcsX5idygu1orWyVlXD99f77qK93AfepaCWKzIW2bBnMmJG6XGd77x1825oa16IWTWgQgCrccIP/NuGB/8a0EJWlldz4sv93ToEUUNkzuc/Etm3buO6669i8eTOtW7emW7duPPLIIxQVFTFt2jSuv/56tmzZQl1dHWPGjKG0tDTqvqqqqnjyyScpLCzkgAMO4Pbbb2+0zcCBAxk3bhz/+te/GDRoEOeee+4e64855hjuvPNOTj/9dOrr6yksLOShhx7i0EMPbdimqKiIqqoqrrvuOr755hvatWvHq6++ygknnMBhhx1GWVkZPXv2pG/fvoCrHI0ePbqh9SzU+jZq1Ch+/OMf065dOxYsWMDjjz/OhRdeSF1dHcceeyw//vGPA1/H008/nffff5+BAwcCriv4ySefpFWrVntsF60sqRJo6iQAEengbR8j/Xlm2dRJLVxT0io0dRqkeFMI/fd/u1a1aOkhJk2KnxajutrFbvnNOJDIlE+xTJ4M110Xv9syiNDUSaqw//7wzTeNt2nXDtavt+mVTLOQyNRJ8z+Zz/Cpw6nXerbXbqe4sJgCKWDWxbM48ZA8SSRtEpKyqZNE5LvAu6oaam+8EThfRD4GblDVVSkqszGJ86tQJZIlv6kTgccLXBeJn3ojyDFqa/3X1damJtdZrJa+RIm4MqlaNn9jIpx4yIms/elaqpZV8eGmD+m2Tzcqe1bSoch+nBgnVpflr4EBACJyJnAJ8H2gD/BHYFjaS2eMn2TmX/QTrTsyiGXLorcq7djhsug3NSA/VsD9jh3w3nvJlT2ST4BtUiyo35iYOhR14LK+9mPE+ItVIVNVDfXHnAf8r6ouBhaLyNXpL5oxUSQ6/2I0Qbo8o20TJIt+U1vh4gXcB83UH29GgmgVsqIiOPJI9zwvyWJMRUWwdCnst5/rvvTrzrWgfmOM8RWrQiZe3NjXwFDg4bB1bdNaKmNiScV8ikG6PGNtEzSLflNa4eIF3AfJ1B/vPOPN+3naafDxx8EqZDU18NJL0StjELy71hhjWphYaS9+CywFFgHvq+oiABHpA6xLe8mMiaapaRX8Jg6PnBg83jZHHAFto/wuadsWjjkmuXMLF+88Dj889vog5xnvWpaWuspbSUn07SKFV8ZCzyku3p1XzQL6jTGmkagtZKr6mDep+H7AO2GrPgdGp7tgxkSVSNqKcKGuu2nTXGuOn1CXp2r8oP3CQv8Yr1Rl0W+qoDMSxLuWHTrs7nqdPh1efz1Yi1n79nDBBdC5c+LdtcYY08LETAyrqp+p6j9Vd8/5oKrrVPWT9BfNmChCLS3hrTbxWmDCpzF6+eXoFYpQl2e8btE1axIvQ6I+/TT2+rCpUHwlMiNBvPMIdb2WlQWrjIFrKevcGe6+2z3XKmPGpNznn3/O9773PY444giOOeYYhg8fzr///e+U7Pt3v/sdRx99NBdffHHM7Tqk8bN91113pW3fuSZIYlhjck8iAfN+ozKjCXV5qrq/o+UR69at6UH78YS6E2OVIRXPT+Q8Yu0zmTIa04JUA1XASqA7UAkEzJzoS1U599xzGTlyJE899RTgsvV/8cUXDfM6xnu+qlIQJU3Nww8/zIsvvshhhx3WhFI2zV133cUtt9ySsePV1dXFnFszraLNOp4Pt379+sWcad0YVVV99FHV4mJVV82KfSspUa2uVt261f0da5t027pVtX17/zK0bx+/DOk4h1j7zNZ1MiZL3nvvvcDbvqmqJaparO4fWLH3+M0mHP+1117Tk046yXdddXW1nnLKKdqnTx/t2bOnPvfcc6qqumrVKj3qqKP0qquu0vLycl29erXec889WlFRoWVlZXr77berquqVV16phYWF2rNnT73//vt1woQJeu+99zbsv7S0VFetWqWqqsXFxb5leOKJJ7SsrEx79eqll1xyiaqqjhw5Up955pmGbULPXbt2rZ500knau3dvLS0t1Xnz5unYsWO1oKBAe/furRdddJGqqv7mN7/R0tJSLS0t1QceeKDhnHr06KGXXXaZlpaW6kUXXaSvvPKKHn/88dqtWzdduHChqqpu27ZNR48erRUVFVpeXt5wTR5//HG94IIL9Mwzz9STTz7ZtyzJ8Ht/AIs0Sp0mbqUH2MfnVhjveZm4WYXMBHLzzfErD8XFrgLxZtjX45tvumWhypzfNum0datqu3b+5W3XLlhlJx3nELnPtm33vM/0dTImS4JWyLaqq3z5/SMrUdVkf7ZMmjRJx4wZ47uutrZWt2zZoqqqGzZs0COOOELr6+t11apVKiK6YMECVVV9+eWX9fLLL9f6+nrdtWuXjhgxQufOnauqqoceeqhu2LBBVTXhCtmyZcv0yCOPbHj+xo0bVTV6hey+++7TO++8U1VV6+rqdOvWrY32vWjRIu3Zs6du27ZNq6ur9ZhjjtElS5boqlWrtFWrVvruu+/qrl27tG/fvjp69Gitr6/X5557Ts8++2xVVR0/frz++c9/VlXVr776Srt3767btm3Txx9/XLt06dJQxmhlSVSiFbIg7XJLgIOBr3BzWX4bWCci64HL1eUmMyZ3xepmKyqCoUPdpN2R3XTp7pKMp6qq6Rnv03EOoQnPx41z9z16wG23wdy5ex5D1U3NFC3PW1OmvjImj1QBUYbXUO+tT3W6WFXllltuYd68eRQUFPDZZ5/xxRdfAHDooYcyYMAAAGbPns3s2bPp06cP4ObHXLlyJYMGDWrS8V9//XUuuOACOnbsCMA+cVIFHXvssfzwhz+ktraWc845h/Ly8kbbzJ8/n3PPPZdiL971vPPO48033+Sss85qmAcToLS0lKFDhyIilJWVsXr16oZzfeGFF7jvvvsA2LFjB5984kLiTzvttIYyBilLOgSpkL0EzFDVlwFE5HTgDOBpXG6y/ukrnjEpEGskYZs28PTT0SsoTckj1lSpyLcGqT+HWBOeh44TL/9ZU6e+MiaPrASiRV1uB5Kdu6K0tJRp06b5rps6dSobNmxg8eLFFBYW0rVrV3Z4o8KLw1LYqCrjx4/nyiuvjHms1q1bN0yqDTTsKxpVRXySTofvR1Wp8Ua8Dxo0iHnz5jFz5kwuvfRSbrrpJn7wgx802mc0bdq0afi7oKCg4XFBQQF1dXUNz58+fTo9evTY47kLFy7c45oEKUs6xBxl6akIVcYAVHU2MEhV/wG0if40Y9Js7VoYORL693f3a9f6b5fMqMxkVVe7VqGxY919kIEE0TQ131oiIsu9dq3/4zFjXLJYv9xmp57qKlUPPgjf+U70/Gfr1sXPj2ZMM9IdiJbFrxhI9pN8yimnsHPnTh599NGGZW+//TZz585ly5Yt7LfffhQWFvLGG2/w8ccf++5j2LBhPPbYY2zzPnefffYZ69evb7Rd165dWbJkCQBLlixh1arY01kPHTqUp59+mo3ejCKbvJlHunbtyuLFrmPt+eefp9abr/fjjz9mv/324/LLL+eyyy5rOFZhYWHDNoMGDeK5557j66+/Zvv27cyYMYOTTjop2MXyzvX3v/99Q8Xun//8p+920cqSbkFayDaJyFjgKe9xJfCViLQieiusMen18MNwzTW7H7/1FvzpT/DQQ3C1z8xemeh+THWrT2Ul3HCD/zrV1OU6iyx327Zw+eXufseOxo+j2bkTHnjAdQPHyvM2blxqpr4yJk9UAlHa6Cnw1idDRJgxYwZjxoxh4sSJtG3blq5du/Lb3/6W0tJSvvvd71JRUUF5eTlHHXWU7z5OP/103n//fQYOHAi4FBZPPvkk++233x7bnX/++fzpT3+ivLycY489Nu4oztLSUm699VYGDx5Mq1at6NOnD1OmTOHyyy/n7LPP5rjjjmPo0KENLVNz5szh3nvvpbCwkA4dOvCnP/0JgCuuuIJevXrRt29fpk6dyqhRozjuuOMA+NGPfkSfPn0auiTjue222xgzZgy9evVCVenatSt/+9vfGm0XrSzpJrGaAAFEpCMwATgRF0M2H/gFsAU4RFWzNlNwRUWFLlq0KFuHN9mydq3LKRbNunVwwAGZKw+41p0uXfxbxEpKgk94HrnP/feHb75pvK5dO1i/vukVyljlTpf+/WHhwujrx41zucuMyXHvv/8+Rx99dKBt5wPDca0Y23EtYwXALNw/V9P8+L0/RGSxqlb4bR+3y1JVv1TV61S1j6qWq+q1qrpBVWuyWRkzLdj48bHXjxvnvzyV3YmRgmTFT2af8YL6kxW6FhdeGL01Kx2Ki90ggEx1xRqTI04E1gKTgHHe/VqsMmZ2i9tlKSJHAj8DuoZvr6qnpK9YxsTwwQex169Y0XhZuoPIUxWAn+59QuNrkUkFBTBxohsEEG19Lkw7ZUwadCD1oylN8xEkhuwZ4I/AZGBXqg7sxaAtAj5T1TNFZB/c6N+uwGrgv1T1q1QdLx2qd1ZTtbyKlRtX0n3f7lSWVlLSxobtp91RR7mYsWgiRtD4ZuoPVUSGD0+uOzFSU7Pq+zn44NjrDzoo8X0mMmtBKrRp42LLiotdZWvWLDed0qxZLvC/psbdiorcLduTj1sqDmNMlgSpkNWp6h/ScOwbgPeBvbzH44DXVHWiiIzzHo9Nw3FTYv4n8xk+dTj1Ws/22u0UFxZz48s3MuviWZx4iDVCp9Xdd7sA/mgmTtzzcZDuxKYGkSc74XmmxboWyTj5ZFiwwD/Yv0MH91qsWRN9EEVoWLzP8PiMs1QcxpgsCpL24q8icrWIdBaRfUK3phxURA4CRuBa3ULOBp7w/n4COKcpx0in6p3VDJ86nOqaarbXuhaR7bXbqa5xy7fV2LD9tDrwQDea0s9DDzUO6E9X11+4dKTWaOrk4n5iXYtk9O8Pr7zif94vvuhGwkZOLh6e3iI0UfnOne5xttJehLccWioOY0wWBGkhG+nd3xS2TIHDm3Dc3wI3s+e8qvur6joAVV0nIvv5PTGTonVJVi2vol79WxnqtZ6qZVVc1tciBdLq6qvhvPP2zBY/caL/6Mp0dCf6SXVqjXSUO5HJwYuK4MgjXSUuVHHyK0Oi552JFstE5WKZWqBUT75tTD6JWyFT1ZRO8y4iZwLrVXWxiAxJ4vlXAFcAHHLIIaks2h5idUmu3LiyoWUs0vba7Xy4yQafZsQBB8CUKfG3y2R3Yiqz4qej3LH2GammxiWB/fhj/wpZeBkSOe9MtFgmKhfL1ML4pYW4EUsLEc/nn3/OmDFjePvtt2nTpk1DHrJ4ecKC6NChQ0PC2FyzdOlS1q5dy/Dhw7NdlJSJ2mUpIqd49+f53ZpwzBOAs0RkNS7Z7Cki8iTwhYh09o7ZGWicKhhQ1UdUtUJVKzp16tSEYkQXr0vy4G8dTHGh/7D94sJiuu1jw/ZzSiYz9adSOsrtt89oiouhtDT1ZcjkDARB5WKZWpBqXGWsmt1TDG0PW56bVYIkVOMCdcZ6900cW6OqnHvuuQwZMoSPPvqI9957j7vuuqthzsogz69PZUxpBi1dupRZs2Zl9JihKZjSJVYM2WDv/rs+tzOTPaCqjlfVg1S1K/A94HVVvQR4gd3doyOB55M9RlPF65IUhALxv3QFUkBlzxwJ4Da7hbrVJk1y3ZyTJrnHuR6snY5yh+/zJz9xGfj9hFrAUl2GysrY+dWyMQAiF8vUggSZfDvvzQe6AGOAe7z7Lt7yJL3xxhsUFhby4x//uGFZeXk5J510Etu2bWPo0KH07duXsrIynn/e/UtdvXo1Rx99NFdffTV9+/bl008/5d577+XYY4+lV69eTJgwwfdYftts376dESNG0Lt3b3r27EmVlxtx3LhxHHPMMfTq1Yuf/exnjfa1bds2Ro8eTVlZGb169WL69OmAa5ELmTZtGqNGjQLgmWeeoWfPnvTu3ZtBgwZRU1PD7bffTlVVFeXl5VRVVbFp0ybOOeccevXqxYABA3j33XcBuOOOOxg5ciSnn346Xbt25dlnn+Xmm2+mrKyMM844o2FapsWLFzN48GD69evHsGHDWLduHQBDhgzhlltuYfDgwUyaNKlRWVIpapelqk7w7ken9IjRTQSeFpHLgE+ACzN03EbidUmu2bqGWRfPatSlWSAFzLp4Fh2KcrTFpaXL5kThTZGOcofv87zzGo8uDKWoCH1BprIModa1eMfMpFwsUwuSrsm3c0Z4E2BI6ISH4zLEJvEWW7ZsGf369fNd17ZtW2bMmMFee+3Fl19+yYABAzjrrLMAWLFiBY8//jgPP/wws2fPZuXKlbz11luoKmeddRbz5s3bo7IRbZsNGzZw4IEHMnPmTAC2bNnCpk2bmDFjBh988AEiwubNmxuV7Ve/+hXf+ta3+Ne//gXAV1/FznD1y1/+kpdffpkuXbqwefNmioqK+OUvf8miRYt48MEHAbjuuuvo06cPzz33HK+//jo/+MEPWLp0KQAfffQRb7zxBu+99x4DBw5k+vTp3HPPPZx77rnMnDmTESNGcN111/H888/TqVMnqqqquPXWW3nssccA2Lx5M3PnzgWgrKxsj7KkUtQKmYjEDDRR1fubenBVnQPM8f7eCAxt6j5Tofu+3SkuLPatlIW6JE885ETW/nQtVcuq+HDTh3TbpxuVPSutMmbyTybm+cyFY+ZjmVqI0OTbfpWypky+nTOCNAGm+DeXqnLLLbcwb948CgoK+Oyzzxq6Mg899FAGDBgAuMrW7Nmz6dOnD+Bar1auXNmoQua3zUknncTPfvYzxo4dy5lnnslJJ51EXV0dbdu25Uc/+hEjRozgzDMbd6i9+uqrPPXUUw2P995775jncsIJJzBq1Cj+67/+i/PO84+Ymj9/fkNL2ymnnMLGjRvZsmULAN/5zncoLCykrKyMXbt2ccYZZwCucrV69WpWrFjBsmXLOO200wDYtWsXnTt3bth3ZVgLeZCyJCtWUH9ocEsP4FhclyK4Lst5KS1FjqksreTGl/3ro+Fdkh2KOthoStM8ZKP1MBdbLHOxTC1AuibfzhlpagIsLS1l2rRpvuumTp3Khg0bWLx4MYWFhXTt2pUdXr7A4rB4SVVl/PjxXHnllVGPE2ubxYsXM2vWLMaPH8/pp5/O7bffzltvvcVrr73GU089xYMPPsjrr7/eaH/ik3swfNmOsNyGf/zjH1m4cCEzZ86kvLy8oeUrcp/R9temTRsACgoKKCwsbFheUFBAXV0dqkppaSkLFizwPf/w6+VXln333df3eYmKGkOmqr9Q1V8AHYG+qvpTVf0p0A9IIkV4/ihpU8Ksi2dRUlTSELxfXFhMSVGJdUlmSfXOaiYvmczYV8YyeclkqndmcDJs02KlOAbbRFGCG01ZgmsRw7sPLc/7b9xQE6CfJjQBnnLKKezcuZNHH320Ydnbb7/N3Llz2bJlC/vttx+FhYW88cYbfPzxx777GDZsGI899ljDaMrPPvuM9evXB9pm7dq1tG/fnksuuYSf/exnLFmyhG3btrFlyxaGDx/Ob3/7W9/K0+mnn97Q1Qi7uyz3339/3n//ferr65kRNr3aRx99RP/+/fnlL39Jx44d+fTTTykpKaE6bMaRQYMGMXXqVADmzJlDx44d2WuvvQiiR48ebNiwoaFCVltby/Lly3239StLqgTJQ3YIED77cA1ueqNmzbokc4fNimCywdIwZFZo8u0qXINRN1zLWLP4xk1TE6CIMGPGDMaMGcPEiRNp27ZtQ9qL0tJSvvvd71JRUUF5eTlHHXWU7z5OP/103n//fQYOHAi4wPonn3yS/fbbL+42H374ITfddFNDy9Mf/vAHqqurOfvss9mxYweqygMPPNDomD//+c+55ppr6NmzJ61atWLChAmcd955TJw4kTPPPJODDz6Ynj17NlQAb7rpJlauXImqMnToUHr37s0hhxzCxIkTKS8vZ/z48dxxxx2MHj2aXr160b59e5544olGx42mqKiIadOmcf3117Nlyxbq6uoYM2YMpaWljbb1K0uqiF8z3x4biNwK/BcwA5cQ9lzgaVW9K2WlSFJFRYUuWrQo28UwaVS9s5ou93ehuqZx20RJUQlrf7rWKskm5apxA+D8WsRKSDoG2zQz77//PkcffXSwjf1q+AVYDb8Z83t/iMhiVa3w2z5IYthfi8hL7H7LjFbVfza5pAZIfILyljKheeg8p703jZpdNb7b2KwIJl2TgScSg23Z5U0gzboJ0KRCkC5LgKXAutD2InKIqn6SrkK1FIl2xbWUrrvI84zGZkVo4dI4GXjQGGzr1jQJ6UDKR1Oa5iPu5OIich3wBfAK8DdgpndvmiDRCcpbyoTmfucZjc2K0IJFmQy8WpXJTz3F2JqaJgXhB4nBbjHZ5Y0xGRG3QgbcAPRQ1VJV7aWqZaraK90Fa+6CTFDelO3zVazzjGSzIrRgPpOBzz/hBLp89hljJk7knqKiJiVCryT6l2MoBrtFZJc3ccWLwzYtUzLviyAVsk+BLQnv2cSU6ATlLWVC81jnGWIpSEzkZODVHTowfNYsqvfai+1eItemtFYFScPQ7LPLm7jatm3Lxo0brVJm9qCqbNy4kbbRpqWLIkgM2X+AOSIyE9gZdsAmZ+pvyYLMBtCU7fNVrPMsalXE0K5DOf+Y8y0FSUsXmgzcq5RVVVZSH2UuymQToceLwW722eVNXAcddBBr1qxhw4YN2S6KyTFt27bloIMSS9kaJO2F70yjXtLYrMrntBeJpnNoKekfWsp5miaqroYuXdw9MPbuu7ln3Liom48D7k51EbDUGMaYxDQ17cUvvJ0Uq2rsviQTWGg2gKATlCe6fSy5nDojledpmrGIycC7f/ghxdu2NXRXhktXa1Wo+zJaail7pzZmKUKMiS5IC9lA4H+BDqp6iIj0Bq5U1aszUcBY8rmFLGRbzbaEZgNIdPtIfqkzQpWdXEqd0dTzNC3Etm1QVUX1J5/Q5dZbqS4qarRJulurtmGppYKwvKjGxG4hC1IhWwhcALygqn28ZctUtWfKS5qg5lAhyyTrDjTNmf3Dz13WvWuME6tCFmSUJaoaOXvmriaXymRcrJQSO3ft5MKnL7SJu03eCgXhT8LFjE3yHltlLPssRYgx8QUZZfmpiBwPqIgUAdcD76e3WCYdYqWUqNlVw0sfvcSbn7zZLLP/m5bBEqHnJksRYkx8QVrIfgxcg2tx/gwo9x6bPBNKKRFLc8z+b4zJriAzHxjT0sWtkKnql6p6sarur6qdVPUSVd2YicKZ1KosraRAAvVSN6vs/8aY7Aoy84ExLV2QuSwPF5G/isgGEVkvIs+LyOGZKJxJrVBKiZKikkAtZc0l+78xJruCzHxgTEsXJIbs/4CHgHO9x98D/gL0T1ehzG6pzhl24iEnsvana6laVsX096bz+urX2blrZ6PtmlP2f9M8WA6r/BZv5gNjWrpAaS9UtX/Esn+o6oC0liyA5p72It05wywNhskXltLCGNMcNDUP2URgM/AUoLgfNW1wrWao6qZUFjYRzblClqnKUr4kis3l2QVMelkOK2Pyg7Vix9ekqZPYHW95ZcTyH+IqaBZPlgaxcoaFAu4v69v0Af7hXZi5mhXfr9JoqTlajiA5rCzVhTHZ5deKfSPWip2IIHNZHpaJgpg9xcoZluqA+w5FHVJSuUuH6p0uBUd4S2HougyfOty6VVsAy2FlTG6rxlXGwluxQ5/Z4VgrdlBRR1mKyLEickDY4x94Iyx/JyL7ZKZ4LVesnGEtKeA+SEuhad4sh1V81cBkYKx3b3NtmEyymRhSI1bai/8BagBEZBAwEfgTsAV4JP1Fa9li5QwrkAIqe7aMzD2ZbCk0uclyWMU2HxdjNwa4x7vv4i03JhOsFTs1YnVZtgoL2K8EHlHV6cB0EVma9pK1cKGcYdEC7ptbN120oP1QS6FfpawltRS2ZKFcVdFGWTavT4K/aMHS1lVkckGoFduvUmat2MFFHWUpIsuAclWtE5EPgCtUdV5onar2zGA5fTXnUZYh22q25XTAfSrEGunZe//elprDALCNlpnDKlbKjw9wLWLR/hFOwgY8mPSzkdDBJTvK8i/AXBH5EvgGeNPbWTdct6XJgFwOuE+FIEH7Laml0ETXEicOj9cC9iOsq8hkn7Vip0bUCpmq/lpEXgM6A7N1d1NaAXBdJgpnmr+g6T1yPTWHMekQL1h6I9ZVZHKDzcTQdDHTXqjqP3yW/Tt9xTEtTdCg/ebeUmiMn3jB0vtiAx5M7miJrdipFHdycWPSydJ7GBNdvJQfpbSsSbstvYdpzuJOnZTLWkJQf3Nn82kaE13QYOmWMODB5jM1zUGsoH5rITNZFUrvUVJU0tBSVlxYTElRiQXtmxYv1NIVrwUs1FV0t3ff3D414YMbQl2428OWb8tSuYxJpSBzWRqTVvkwn6Yx2WLB0onNZ2oTXJt8ZRUykxMsaN+Y6Fp6sHTQTPA2wbXJZ1YhM8aYBFgLTOYFyQRvsxaYfGcxZMYYE5DNG5kdQeYztQmuTb6zCpkxxgRggeXZE2Rwg01wbfKddVkaY0wAiQSWm9SLN7jBJrg2+c4qZMYYE4C1wGRfrMENlbgAfj82a4HJB9ZlaYwxAcTLmm8tMNkVNGebMbkq4y1kInIw8CfgAFxL/yOqOklE9sG1RncFVgP/papfZbp8xhjjJ5daYGykpz/L2WbyWTa6LOuAn6rqEhEpARaLyCvAKOA1VZ0oIuOAcbgpy4wxJutKgInANT7rJpK5f/qWayu2lp6zzeSvjHdZquo6VV3i/V0NvI8bOX428IS32RPAOZkumzHGRFON+5XoZxyZGWVpIz2Nab6yGkMmIl2BPsBCYH9VXQeu0gbsl8WiGWPMHnIhz1UulMEYkx5Zq5CJSAdgOjBGVbcm8LwrRGSRiCzasGFD+gpojDFhcmGUZS6UwRiTHlmpkIlIIa4yNlVVn/UWfyEinb31nYH1fs9V1UdUtUJVKzp16pSZAhtjWrxcGGWZC2UwprmpBibjgtYns+f0W5mU8QqZiAjwv8D7qnp/2KoXgJHe3yOB5zNdNmOMiSbI9D0toQzGNCe5NB1aNkZZngBcCvxLRJZ6y27BDVR6WkQuAz4BLsxC2UyKVO+spmp5FSs3rqT7vt2pLK2kpI0NzDf5K5TP6jtALbATaAMUkrk8V6EyRI6yLMhgGXKdpQQxQeXahPQZr5Cp6nxAoqwemsmymPSY/8l8hk8dTr3Ws712O8WFxdz48o3MungWJx5iA/NN/tOI+0yyXFvRWUoQk4hcmw7Npk4yKVW9s5rhU4dTXbP7N8f2WvebY/jU4az96Vo6FNm/DpN//FJL1Hi3TP+atlxbjeVaa4fJfbk2SMamTjIpVbW8inr1/81Rr/VULbOB+SY/WcqJ3GavT2y5ErieS3JtkIxVyExKrdy4sqFFLNL22u18uMkG5pv8lGu/ps2e7PWJLpcC13NJrg2SsS7LDGoJge7d9+1OcWGxb6WsuLCYbvvYwHyTnw6Os/6gjJQimHwJbE9lOUOtHX6VspacEsS6cqPLtUEyopqNsNTUqKio0EWLFmW7GIH4BboXSEGzC3Sv3llNl/u77BFDFlJSVGIxZCZvPQhcF2e93zyXmeYX2B76B5NL3zSpLmc1rtXHryuuhJZb8ZiMaxGLVlGdhMUjbiNzg2REZLGqVvitsy7LDAgPdA+1HG2v3U51jVu+rab5zEBX0qaEWRfPoqSohOJC1ztfXFhMSZFbbpUxk68+jbN+TUZKEVu+zHWZjnKGWjtK2B0XVBy2vKV+81hXbnyhQTJ3e/fZeq9Yl2Uahboop703jZpdNb7bhALdL+ubud8o6eg6jdznimtXMGvlLD7c9CHd9ulGZc9Kq4yZvJYPXWLpHMafyu7FdJXTUoI0lg/vW+NYhSxNIrsoo8l0oHs6coTF2mcmK5rGpFMlLqeVn1zJkp+u1pBU5/dKZ6uNpQTZUz68b41jXZZp4NdFGU0mA93T0XXakrpjTcuWy11ioZQG7+BmD/CTbGtIOroXcy3dQHOWy+9bsyerkKVBrFxckeq1nsqemfmNko4cYZZ3zLQkoS6xScA4734t2Q2WD09p8DJuSic/SnKtIenI75Vr6Qaau1x835rGrMsyDWLl4ork5lrPjHTkCLO8Y6alyaUuMb+UBtEkO54+Hd2LuZZuoCXIpfet8WctZGkQysUVhCAZa0WKVa5ku07TsU9jckWuZzeP1XoVqYDkWrPS1b1orTbG7MkqZGlQWVpJgQS7tJlsRYpVrgIpSKrrNB37NCYX5EN281itV5GSbc1KZ/dirqQbMCYXWIUsDfxycUWTyVakdOQIs7xjJpvS1YKVL/m8YrVeRUq2NcuCwo3JDMvUn0bbarZRtayK5euX84fFf2BH3Y5G22Qje32oXKnMEZaOfRoTSzoz0udLdvNY2ekjNTVbfSazmRvTXMXK1G8VsgxpKVMnGZMJ6Z4mZyyumzKacbhutlwQWTFtC+wIu8/VqZOMaYliVchslGWGnHjIiaz96VprRTImBZLN9B4023w+ZTc/EViBqySuAHoAtwFzsdYsY/KJVcgyqENRB8tcb0wKJJOKIZFs8/mU3TzyvJYBM3DnZd82xuQPC+o3xuSdRFMxJBqkny+B7Pky+MAYE59VyIwxeaeS2F2WkS1YyWSbz4c8WenIom+MyQ7rsjTG5KVoc1z4LU8223yuZzdP5yTdxpjMshYyY0zeqSJ2hSyyZai5TmbdXM+rucv1GSBMdliFzBiTdxJtGWquk1k31/NqzvJhBgiTHVYhM8bknaAtQ6GWiDuBq3BdkLkcpJ+ofBl8YBwbhGFisRgyY0zeCZKWwi/NhQDXevfNJT9XaPCBZdHPfcnmzzMtg1XIjDF5J9QCFG3qJGV3S0RIqEXiYZqeyT/X5PrgA+PYIAwTi1XIElC9s5qq5VWs3LiS7vt2p7K0kpI2JYHXG2NSJ1bL0GSsJcLknnyaAcJkns1lGVC8uShtrkpjckc+zUVpWo50z8Fqcp/NZdlE1TurGT51ONU1uz9G22vdb5zhU4ez4toVMdev/enatM1Zaa1yxjQWtCUi6NyWTZGJY5j8EK+r3SpjLZtVyAKoWl5Fvfp3gNRrPeNeHRdzfdWyqrTMYenXKnfjyzdaq5xp8SqBG6KsU6IH/Ueb2zJZmTiGyS82CMNEYxWyAFZuXNnQ4hVpe+12VmxcEXP9h5tSH6oZr9Uuna1yxuSDaMEYyp7pB0JCn+DhpKbrKBPHMPnJBmEYP5aHLIDu+3anuNA/61FxYTE99u0Rc323fVIfqhmv1a5qWfKz2FXvrGbyksmMfWUsk5dMpnpn/DzSyTzHmHSpInbC1HGkfw5Im2fSGJMIayELoLK0khtf9s96VCAFTDx1IjM+mBF1fWXP1OfLjtdql2yrXDLdoNZ1anJNvPQCK+KsT0WbtqU4MMYkwlrIAihpU8Ksi2dRUlTS0BJWXFhMSZFb3rmks+/6DoUduOrYq/jV3F+lvNUoXqtdMq1y4d2gocre9trtVNe45dtqGueRTuY5xqRbvEz+PeKsT0Wbts0zaYxJhKW9SMC2mm1ULaviw00f0m2fblT2rNwjTit8vary0KKHUNW0pMGo3llNl/u77BFDFlJSVJJUDNnkJZMZ89IY35a34sJiJp0xqdHghGSeY0y6xUsvsAJXKUtn+gFLcWCMiWRpL1KkQ1GHmJWL0PpQZSm8dSjVAfehVrtouc+S2X8y3aDp6jo1pinipRfoHGd9KipKluLAGJMIq5ClQZCA+1S0Gp14yIms/enamK12iQh1g0Zr7fLrBk3mOcZkQrz0AplIP2ApDowxQVmFLA0y2WoUr9UuEfEGL/gNTkjmOcZkSrz0AplIP2ApDowxQVhQfxqkI+A+E+INXvBreUvmOcbks7XASKC/d782u8UxxjQTFtSfBukIuM+keIMXUvUcY/LNw8A1PssfAq7OcFmMMfknVlC/VcjSxCYbN6Z5WYsbNRnNOuCADJXFGJOf8mqUpYicAUwCWgGTVXVilouUlFQH3Btjsmt8nPXjgCkZKIcxpnnKqQqZiLTCtf6fBqwB3haRF1T1veyWLDmpDLg3xmTXB3HWr8hIKYwxzVWuBfUfB3yoqv9R1RrgKeDsLJfJGGM4Ks76HhkphTGmucq1ClkX4NOwx2uIHbZhjDEZcXec9XkZW2GMyRm5ViETn2V7jDoQkStEZJGILNqwYUOGimWMaekOxMVT+HkIC+g3xjRNrlXI1gAHhz0+iIg0P6r6iKpWqGpFp06dMlo4Y0zLdjVuNOVIYIB3vw5LeWGMabqcCuoH3ga6i8hhwGfA94CLslskY4zZ7QBsNKUxJvVyqkKmqnUici3wMi7txWOqujzLxTLGGGOMSaucqpABqOosYFa2y2GMMcYYkym5FkNmjDHGGNPiWIXMGGOMMSbLrEJmjDHGGJNlViEzxhhjjMkyq5AZY4wxxmSZqGr8rXKUiGwAPs52OZLQEfgy24VoRux6po5dy9Sxa5k6di1Tx65l6iRzLQ9VVd+s9nldIctXIrJIVSuyXY7mwq5n6ti1TB27lqlj1zJ17FqmTqqvpXVZGmOMMcZkmVXIjDHGGGOyzCpk2fFItgvQzNj1TB27lqlj1zJ17Fqmjl3L1EnptbQYMmOMMcaYLLMWMmOMMcaYLLMKWZqJyMEi8oaIvC8iy0XkBm/5PiLyiois9O73znZZ84WItBKRf4rI37zHdi2TICLfFpFpIvKB9/4caNcyOSLyE+/zvUxE/iIibe1aBicij4nIehFZFrYs6vUTkfEi8qGIrBCRYdkpdW6Kci3v9T7n74rIDBH5dtg6u5ZR+F3LsHU/ExEVkY5hy5p0La1Cln51wE9V9WhgAHCNiBwDjANeU9XuwGveYxPMDcD7YY/tWiZnEvCSqh4F9MZdU7uWCRKRLsD1QIWq9gRaAd/DrmUipgBnRCzzvX7e9+f3gFLvOQ+LSKvMFTXnTaHxtXwF6KmqvYB/A+PBrmUAU2h8LRGRg4HTgE/CljX5WlqFLM1UdZ2qLvH+rsb90+sCnA084W32BHBOVgqYZ0TkIGAEMDlssV3LBInIXsAg4H8BVLVGVTdj1zJZrYF2ItIaaA+sxa5lYKo6D9gUsTja9TsbeEpVd6rqKuBD4LhMlDMf+F1LVZ2tqnXew38AB3l/27WMIcr7EuAB4GYgPAi/ydfSKmQZJCJdgT7AQmB/VV0HrtIG7JfFouWT3+I+CPVhy+xaJu5wYAPwuNf9O1lEirFrmTBV/Qy4D/dreR2wRVVnY9eyqaJdvy7Ap2HbrfGWmWB+CLzo/W3XMkEichbwmaq+E7GqydfSKmQZIiIdgOnAGFXdmu3y5CMRORNYr6qLs12WZqA10Bf4g6r2AbZjXWpJ8WKbzgYOAw4EikXkkuyWqlkTn2WWLiAAEbkVF0YzNbTIZzO7llGISHvgVuB2v9U+yxK6llYhywARKcRVxqaq6rPe4i9EpLO3vjOwPlvlyyMnAGeJyGrgKeAUEXkSu5bJWAOsUdWF3uNpuAqaXcvEnQqsUtUNqloLPAscj13Lpop2/dYAB4dtdxCui9jEICIjgTOBi3V3viu7lok5AvfD6x3v/9BBwBIROYAUXEurkKWZiAguTud9Vb0/bNULwEjv75HA85kuW75R1fGqepCqdsUFT76uqpdg1zJhqvo58KmI9PAWDQXew65lMj4BBohIe+/zPhQXK2rXsmmiXb8XgO+JSBsROQzoDryVhfLlDRE5AxgLnKWqX4etsmuZAFX9l6rup6pdvf9Da4C+3vdpk69l65SX2EQ6AbgU+JeILPWW3QJMBJ4WkctwX+gXZqd4zYJdy+RcB0wVkSLgP8Bo3I80u5YJUNWFIjINWILrDvonLoN3B+xaBiIifwGGAB1FZA0wgSifa1VdLiJP435A1AHXqOqurBQ8B0W5luOBNsAr7jcD/1DVH9u1jM3vWqrq//ptm4praZn6jTHGGGOyzLosjTHGGGOyzCpkxhhjjDFZZhUyY4wxxpgsswqZMcYYY0yWWYXMGGOMMSbLrEJmjEkLETlXRFREjsrCsVeLSMegy3OFiPQRkcne33eIyM9ibHtHxONOIvJSmotojEkTq5AZY9Ll+8B8XBJfE8wtwO9jbSAix4jIPOAqEVkiIt8HUNUNwDoROSED5TTGpJhVyIwxKefN3XoCcBlhFTIRGSIic0Rkmoh8ICJTvez2odarX3iVjH+FWtYiW4pEZJmIdPX+fk5EFovIchG5IoHydRWR90XkUe+5s0Wknbeum4i8KiLveGU5Qpx7vWP/S0Qqw85nrog8LSL/FpGJInKxiLzlbXeEt10nEZkuIm97t0aVJhEpAXr5TFqMiFwuIi96ZbwD+BPwB+8avx226XPAxUGvgzEmd1iFzBiTDucAL6nqv4FNItI3bF0fYAxwDHA4rlIR8qWq9sVVNqJ214X5oar2AyqA60Vk3wTK2B14SFVLgc3A+d7yqd7y3rg5KdcB5wHlQG/c3JX3huZZ9JbdAJThZuU4UlWPAybjZkMAmAQ8oKrHeseZ7FOeCmBZ5EIRuRb4LnCOqn4D1AD7AQWq+o2qfhi2+SLgpASugTEmR1iFzBiTDt/HTQCPd//9sHVvqeoaVa0HlgJdw9Y9690vjlgezfUi8g7wD9zEvt0TKOMqVV0afjyvlaqLqs4AUNUd3tx/JwJ/UdVdqvoFMBc41nvu26q6TlV3Ah8Bs73l/wo7h1OBB73p014A9vKOFa4zsCFi2aXAd4Dzvf2Dm5OwDLhWRP4qIr3Dtl8PHJjANTDG5Aiby9IYk1JeK9UpQE8RUaAVoCJys7fJzrDNd7Hn99BOn+V17Pnjsa13nCG4is5AVf1aROaE1gUUWY52gETZNtryyP3Uhz2uZ/c5FHjl/CbGfr6hcfmX4VrmDgJWAajqZ8D3ReSXuO7KZ4EjvO3bevsxxuQZayEzxqTaBcCfVPVQVe2qqgfjKhMnJrm/1UBfAK/r8zBv+beAr7zK2FHAgKYVG1R1K7BGRM7xjtdGRNoD84BKEWklIp2AQcBbCex6NnBt6IGIlPts8z7QLWLZP4ErgRdE5EDvuaXeunpcy15x2PZH4tPtaYzJfVYhM8ak2veBGRHLpgMXJbm/6cA+XnffVcC/veUvAa1F5F3gV7huy1S4FNcV+i7wd+AA3Pm8C7wDvA7crKqfJ7DP64EKEXlXRN4Dfhy5gap+AHwrsitTVefj4ulmeik7zhORfwA/xFX0rg/b/GRgZgLlMsbkCFHVbJfBGGMMICI/AapV1S/oP3LbO1T1johl84CzVfWrNBXRGJMm1kJmjDG54w/sGZMWy5zwB15X6v1WGTMmP1kLmTHGGGNMllkLmTHGGGNMllmFzBhjjDEmy6xCZowxxhiTZVYhM8YYY4zJMquQGWOMMcZkmVXIjDHGGGOy7P8DCfGD409MlakAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(data[k_pred1 == 0, 1], data[k_pred1 == 0, 2], s = 50, c = 'red', label = 'Standard customers')\n",
    "plt.scatter(data[k_pred1 == 1, 1], data[k_pred1 == 1, 2], s = 50, c = 'blue', label = 'Target customers')\n",
    "plt.scatter(data[k_pred1 == 2, 1], data[k_pred1 == 2, 2], s = 50, c = 'green', label = 'Sensible customers')\n",
    "plt.scatter(data[k_pred1 == 3, 1], data[k_pred1 == 3, 2], s = 50, c = 'cyan', label = 'Careful customers')\n",
    "plt.scatter(data[k_pred1 == 4, 1], data[k_pred1 == 4, 2], s = 50, c = 'magenta', label = 'Careless customers')\n",
    "plt.title('Clusters of customers using Kmeans without PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e044d2",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The blue clusters are the target customers whose Annual Income and Spending Scores are high.\n",
    "- The red clusters are the Standard customers whose Annual Income and Spending scores are moderate.\n",
    "- The cyan cluster indicates the Careful customers whose Annual Income is high but Spending Score is low.\n",
    "- The magenta cluster indicates the Careless customers whose Annual Income is low but Spending Score is high.\n",
    "- The green cluster indicates the Sensible customers whose Annual Income and Spending scores are low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2116ae7e",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "- Using KMeans model with dimension reduction and without dimension reduction, the clusters formed are good and interpretable.\n",
    "- The Silhouette score of Kmeans model with dimension reduction is 55%.\n",
    "- The Silhouette score of Kmeans model without dimension reduction is 44%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c4f812",
   "metadata": {},
   "source": [
    "## Hierarchical Clustering "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb35d27",
   "metadata": {},
   "source": [
    "## Agglomerative Model with PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a361f029",
   "metadata": {},
   "source": [
    "### Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8c0a59c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "dendrogrm = sch.dendrogram(sch.linkage(values, method = 'ward'))\n",
    "plt.axhline(y=150, color='r', linestyle='--')\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='x', which='major', labelsize=10)\n",
    "plt.title('Customer Dendrogram',fontsize=30)\n",
    "plt.xlabel('Customers',fontsize=20)\n",
    "plt.ylabel('Euclidean distance', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55e9efd",
   "metadata": {},
   "source": [
    "**Interpretation**: From the dendrogram we can set cluster size as 5."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724cbb21",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a8dcbad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_model = AgglomerativeClustering(n_clusters=5, affinity='euclidean',linkage='ward')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3603e400",
   "metadata": {},
   "source": [
    "### Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ce52920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_model.fit(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975a54d1",
   "metadata": {},
   "source": [
    "### Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cdf81564",
   "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, 2,\n",
       "       4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 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, 1, 2, 1, 2, 1, 0, 1, 0, 1,\n",
       "       2, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 1, 0, 1, 2, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 2, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_pred = agg_model.fit_predict(values)\n",
    "agg_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7f51dd06",
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Kmeans_pred</th>\n",
       "      <th>agg_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Kmeans_pred  agg_pred\n",
       "0   19                  15                      39            4         4\n",
       "1   21                  15                      81            3         3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the predicted values to the dataframe\n",
    "df_pred['agg_pred'] = agg_pred\n",
    "df_pred.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd6d401",
   "metadata": {},
   "source": [
    "### Silhouette Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "410b3ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score of Agglomerative model = 55.0 %\n"
     ]
    }
   ],
   "source": [
    "agg_score = np.round(silhouette_score(values,agg_pred)*100)\n",
    "agg_score\n",
    "\n",
    "print(f'Silhouette score of Agglomerative model = {agg_score} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0aeabb4",
   "metadata": {},
   "source": [
    "### Visualization of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71f70f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(data[agg_pred == 0, 1], data[agg_pred == 0, 2], s = 50, c = 'red', label = 'Careful customers')\n",
    "plt.scatter(data[agg_pred == 1, 1], data[agg_pred == 1, 2], s = 50, c = 'blue', label = 'Target customers')\n",
    "plt.scatter(data[agg_pred == 2, 1], data[agg_pred == 2, 2], s = 50, c = 'green', label = 'Standard customers')\n",
    "plt.scatter(data[agg_pred == 3, 1], data[agg_pred == 3, 2], s = 50, c = 'cyan', label = 'Careless customers')\n",
    "plt.scatter(data[agg_pred == 4, 1], data[agg_pred == 4, 2], s = 50, c = 'magenta', label = 'Sensible customers')\n",
    "plt.title('Clusters of customers using Agglomerative with PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a197b8",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The blue cluster indicates the Target customers whose Annual income and Spending Scores are high.\n",
    "- The green cluster indicates the Standard customers whose Annual income and Spending scores are moderate.\n",
    "- The cyan cluster indicates the careless customers whose Annual Income is low but Spending score is high.\n",
    "- The magenta cluster indicates the sensible customers whose Annual income and Spending scores are low.\n",
    "- The red cluster indicates the careful customers whose Annual Income is high but spending score is low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a900493",
   "metadata": {},
   "source": [
    "## Agglomerative clustering without Dimension reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4c1ce4",
   "metadata": {},
   "source": [
    "### Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "097e3c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "dendrogrm = sch.dendrogram(sch.linkage(data, method = 'ward'))\n",
    "plt.axhline(y=200, color='r', linestyle='--')\n",
    "ax = plt.gca()\n",
    "ax.tick_params(axis='x', which='major', labelsize=10)\n",
    "plt.title('Customer Dendrogram',fontsize=30)\n",
    "plt.xlabel('Customers',fontsize=20)\n",
    "plt.ylabel('Euclidean distance', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45219257",
   "metadata": {},
   "source": [
    "**Interpretation**: From the dendogram we can set cluster size as 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bef2f508",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6899bc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_model1 = AgglomerativeClustering(n_clusters=5, affinity='euclidean',linkage='ward')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94fa657b",
   "metadata": {},
   "source": [
    "### Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9777cfe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=5)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_model1.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930fd001",
   "metadata": {},
   "source": [
    "### Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "09003a02",
   "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, 0, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 0,\n",
       "       4, 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, 2, 0, 2, 1, 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], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_pred1 = agg_model1.fit_predict(data)\n",
    "agg_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f425f5a4",
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Kmeans_pred</th>\n",
       "      <th>agg_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Kmeans_pred  agg_pred\n",
       "0   19                  15                      39            2         4\n",
       "1   21                  15                      81            4         3"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the predicted values to dataframe\n",
    "df_pred1['agg_pred'] = agg_pred1\n",
    "df_pred1.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3186821d",
   "metadata": {},
   "source": [
    "### Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7a8c5dd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score of Agglomerative model = 44.0 %\n"
     ]
    }
   ],
   "source": [
    "agg_score1 = np.round(silhouette_score(data,agg_pred1)*100)\n",
    "agg_score1\n",
    "\n",
    "print(f'Silhouette score of Agglomerative model = {agg_score1} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3ab838",
   "metadata": {},
   "source": [
    "### Visualization of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f9f9e834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(data[agg_pred1 == 0, 1], data[agg_pred1 == 0, 2], s = 50, c = 'red', label = 'Standard')\n",
    "plt.scatter(data[agg_pred1 == 1, 1], data[agg_pred1 == 1, 2], s = 50, c = 'blue', label = 'Careful')\n",
    "plt.scatter(data[agg_pred1 == 2, 1], data[agg_pred1 == 2, 2], s = 50, c = 'green', label = 'Target customers')\n",
    "plt.scatter(data[agg_pred1 == 3, 1], data[agg_pred1 == 3, 2], s = 50, c = 'cyan', label = 'Careless')\n",
    "plt.scatter(data[agg_pred1 == 4, 1], data[agg_pred1 == 4, 2], s = 50, c = 'magenta', label = 'Sensible')\n",
    "plt.title('Clusters of customers using Agglomerative without PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "787714dc",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The green cluster indicates the Target customers whose Annual income and Spending Scores are high.\n",
    "- The red cluster indicates the Standard customers whose Annual income and Spending scores are moderate.\n",
    "- The cyan cluster indicates the careless customers whose Annual Income is low but Spending score is high.\n",
    "- The magenta cluster indicates the sensible customers whose Annual income and Spending scores are low.\n",
    "- The blue cluster indicates the careful customers whose Annual Income is high but spending score is low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d1dfaf",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "- The clusters formed using Agglomerative clustering model with dimension reduction are very well seggregated and well interpretable.\n",
    "- The Silhouette Score of Agglomerative model with dimension reduction is 55%.\n",
    "- The Silhouette score of Agglomerative model without dimension reduction is 44%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cac349",
   "metadata": {},
   "source": [
    "## DBSCAN Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4076603c",
   "metadata": {},
   "source": [
    "## DBSCAN Algorithm with PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61248696",
   "metadata": {},
   "source": [
    "### K Distance graph for optimal eps value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d02f9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "neigh = NearestNeighbors(n_neighbors=2).fit(values)\n",
    "distances, indices = neigh.kneighbors(values)\n",
    "\n",
    " # Plotting K-distance Graph\n",
    "distances = np.sort(distances, axis=0)\n",
    "distances = distances[:,1]\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(distances,color='teal')\n",
    "plt.axhline(y=9, color='r', linestyle='--')\n",
    "plt.title('K-distance Graph',fontsize=20)\n",
    "plt.xlabel('Data Points sorted by distance',fontsize=14)\n",
    "plt.ylabel('Epsilon',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcd849f",
   "metadata": {},
   "source": [
    "**We can set eps value as 9 for the model.** "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc8f1045",
   "metadata": {},
   "source": [
    "### Finding Optimal min samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "06e14a07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eps= 9 | min_samples=  2 | obtained clustering:  [(1, 5), (2, 139), (3, 12), (4, 3), (5, 33), (6, 2), (7, 2), (8, 2), (9, 2)]\n",
      "eps= 9 | min_samples=  5 | obtained clustering:  [(1, 24), (2, 15), (3, 12), (4, 117), (5, 32)]\n",
      "eps= 9 | min_samples=  8 | obtained clustering:  [(1, 50), (2, 11), (3, 8), (4, 86), (5, 24), (6, 21)]\n",
      "eps= 9 | min_samples=  11 | obtained clustering:  [(1, 79), (2, 82), (3, 16), (4, 23)]\n",
      "eps= 9 | min_samples=  14 | obtained clustering:  [(1, 119), (2, 81)]\n",
      "eps= 9 | min_samples=  17 | obtained clustering:  [(1, 120), (2, 80)]\n"
     ]
    }
   ],
   "source": [
    "eps=9\n",
    "for min_samples in range(2,20,3):\n",
    "    clusterer = DBSCAN(eps=eps, min_samples=min_samples)\n",
    "    cluster_labels = clusterer.fit_predict(values)\n",
    "\n",
    "    x= cluster_labels +2\n",
    "    y = np.bincount(x) \n",
    "    ii = np.nonzero(y)[0]  \n",
    "    results = list(zip(ii,y[ii]) )\n",
    "    print('eps=',eps,'| min_samples= ', min_samples, '| obtained clustering: ', results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e373e009",
   "metadata": {},
   "source": [
    "**We can set min samples as 5 for the model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4206f8",
   "metadata": {},
   "source": [
    "### Model Building & Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bb19ea03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=9)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan_model = DBSCAN(eps=9,min_samples=5)\n",
    "dbscan_model.fit(values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed85d16",
   "metadata": {},
   "source": [
    "### Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c22ff58c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0,  1,  0, -1,  0,  1, -1,  1,  0,  1, -1,  1,  0,  1,  0,  2,\n",
       "        0,  2, -1,  2,  0,  1,  0,  1,  0,  2, -1,  2,  0,  1,  0,  1, -1,\n",
       "        1,  0,  1,  0,  2,  0,  2, -1,  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,  2,  2,\n",
       "        2,  2,  2,  2,  2,  2,  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,  2,  2,\n",
       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
       "        2,  2,  2,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,\n",
       "        2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,\n",
       "        3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2,  3,  2, -1,\n",
       "        2,  3,  2,  3,  2,  3,  2,  3,  2,  3, -1,  3,  2,  3, -1,  3, -1,\n",
       "       -1, -1,  3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan_pred = dbscan_model.fit_predict(values)\n",
    "dbscan_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d69564",
   "metadata": {},
   "source": [
    "### Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "03dd7f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score of DBSCAN model with dimension reduction = 36.0 %\n"
     ]
    }
   ],
   "source": [
    "dbscan_score = np.round(silhouette_score(values,dbscan_pred)*100)\n",
    "\n",
    "print(f'Silhouette score of DBSCAN model with dimension reduction = {dbscan_score} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e980e2dd",
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Kmeans_pred</th>\n",
       "      <th>agg_pred</th>\n",
       "      <th>dbscan_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Kmeans_pred  agg_pred  \\\n",
       "0   19                  15                      39            4         4   \n",
       "1   21                  15                      81            3         3   \n",
       "\n",
       "   dbscan_pred  \n",
       "0           -1  \n",
       "1            0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding predicted value to dataframe\n",
    "df_pred['dbscan_pred']=dbscan_pred\n",
    "df_pred.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "330c3c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 2    117\n",
       " 3     32\n",
       "-1     24\n",
       " 0     15\n",
       " 1     12\n",
       "Name: dbscan_pred, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction counts\n",
    "df_pred['dbscan_pred'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54975432",
   "metadata": {},
   "source": [
    "### Visualization of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "706243bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(data[dbscan_pred == -1, 1], data[dbscan_pred == -1, 2], s = 50, c = 'red', label = 'Outliers')\n",
    "plt.scatter(data[dbscan_pred == 0, 1], data[dbscan_pred == 0, 2], s = 50, c = 'blue', label = 'Careless customers')\n",
    "plt.scatter(data[dbscan_pred == 1, 1], data[dbscan_pred == 1, 2], s = 50, c = 'green', label = 'Sensible customers')\n",
    "plt.scatter(data[dbscan_pred == 2, 1], data[dbscan_pred == 2, 2], s = 50, c = 'cyan', label = 'Standard customers')\n",
    "plt.scatter(data[dbscan_pred == 3, 1], data[dbscan_pred == 3, 2], s = 50, c = 'magenta', label = 'Target customers')\n",
    "plt.title('Clusters of customers using DBSCAN with PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3857dbde",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The red dots indicate the Outliers in the customers in which some customers can be considered as target customers as they have high income and spending score.\n",
    "- The blue cluster indicates the Careless customers whose Annual Income is low but their spending score is high.\n",
    "- The cyan cluster indicates the Standard customers in which some customers have high annual income but low spending score and vice versa.\n",
    "- The green cluster indicates the sensible customers whose annual income and spending score is low.\n",
    "- The magenta cluster indicates out target customers whose annual income and spending score is high."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab8d03a",
   "metadata": {},
   "source": [
    "## DBSCAN Algorithm without Dimension Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c82a651",
   "metadata": {},
   "source": [
    "### K-Distance graph for optimal eps value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b0580954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neigh = NearestNeighbors(n_neighbors=2).fit(data)\n",
    "distances, indices = neigh.kneighbors(data)\n",
    "\n",
    " # Plotting K-distance Graph\n",
    "distances = np.sort(distances, axis=0)\n",
    "distances = distances[:,1]\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(distances,color='teal')\n",
    "plt.axhline(y=15, color='r', linestyle='--')\n",
    "plt.title('K-distance Graph',fontsize=20)\n",
    "plt.xlabel('Data Points sorted by distance',fontsize=14)\n",
    "plt.ylabel('Epsilon',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a25ace8",
   "metadata": {},
   "source": [
    "**We can set eps value as 15 for our model**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2db5151",
   "metadata": {},
   "source": [
    "### Finding Optimal min samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c1334d3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eps= 15 | min_samples=  2 | obtained clustering:  [(2, 200)]\n",
      "eps= 15 | min_samples=  5 | obtained clustering:  [(1, 6), (2, 194)]\n",
      "eps= 15 | min_samples=  8 | obtained clustering:  [(1, 10), (2, 190)]\n",
      "eps= 15 | min_samples=  11 | obtained clustering:  [(1, 18), (2, 171), (3, 11)]\n",
      "eps= 15 | min_samples=  14 | obtained clustering:  [(1, 31), (2, 22), (3, 116), (4, 31)]\n",
      "eps= 15 | min_samples=  17 | obtained clustering:  [(1, 51), (2, 118), (3, 31)]\n"
     ]
    }
   ],
   "source": [
    "eps=15\n",
    "for min_samples in range(2,20,3):\n",
    "    clusterer = DBSCAN(eps=eps, min_samples=min_samples)\n",
    "    cluster_labels = clusterer.fit_predict(values)\n",
    "\n",
    "    x= cluster_labels +2\n",
    "    y = np.bincount(x) \n",
    "    ii = np.nonzero(y)[0]  \n",
    "    results = list(zip(ii,y[ii]) )\n",
    "    print('eps=',eps,'| min_samples= ', min_samples, '| obtained clustering: ', results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab3dd9c",
   "metadata": {},
   "source": [
    "**We can set optimal min samples as 5 for our model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1912704",
   "metadata": {},
   "source": [
    "### Model Building & Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ef0b02fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(eps=15)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan_model1 = DBSCAN(eps=15,min_samples=5)\n",
    "dbscan_model1.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f96da2",
   "metadata": {},
   "source": [
    "### Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6c8b8fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  0, -1,  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,\n",
       "        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,\n",
       "        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,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,\n",
       "        0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,\n",
       "        1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,\n",
       "        0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1,  0,  1, -1,  1,  0,\n",
       "        1,  0,  1, -1, -1, -1,  1, -1, -1, -1, -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan_pred1 = dbscan_model1.fit_predict(data)\n",
    "dbscan_pred1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a7482a",
   "metadata": {},
   "source": [
    "### Silhouette score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "30aecd96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette score of dbscan model without dimension reduction = 28.0 %\n"
     ]
    }
   ],
   "source": [
    "dbscan_score1 = np.round(silhouette_score(data,dbscan_pred1)*100)\n",
    "\n",
    "print(f'Silhouette score of dbscan model without dimension reduction = {dbscan_score1} %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "97eeabd5",
   "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>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>Kmeans_pred</th>\n",
       "      <th>agg_pred</th>\n",
       "      <th>dbscan_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  Kmeans_pred  agg_pred  \\\n",
       "0   19                  15                      39            2         4   \n",
       "1   21                  15                      81            4         3   \n",
       "\n",
       "   dbscan_pred  \n",
       "0           -1  \n",
       "1            0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding predicted values to dataframe\n",
    "df_pred1['dbscan_pred']=dbscan_pred1\n",
    "df_pred1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8cfe36cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    153\n",
       " 1     35\n",
       "-1     12\n",
       "Name: dbscan_pred, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction counts\n",
    "df_pred1['dbscan_pred'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41763e0a",
   "metadata": {},
   "source": [
    "### Visualization of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "81c54a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(data[dbscan_pred1 == -1, 1], data[dbscan_pred1 == -1, 2], s = 50, c = 'red', label = 'Outliers')\n",
    "plt.scatter(data[dbscan_pred1 == 0, 1], data[dbscan_pred1 == 0, 2], s = 50, c = 'blue', label = 'Standard customers')\n",
    "plt.scatter(data[dbscan_pred1 == 1, 1], data[dbscan_pred1 == 1, 2], s = 50, c = 'green', label = 'Target customers')\n",
    "plt.title('Clusters of customers using DBSCAN without PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c090a2b3",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The red dots indicate the outlier customers in which some customers can be considered as target customers having high income and spending score.\n",
    "- The green cluster indicates our target customers whose income ranges from moderate to high salary and high spending score.\n",
    "- The blue cluster indicates standard customers where some customers have low salary and high spending score and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57848505",
   "metadata": {},
   "source": [
    "### Interpretation:\n",
    "- The clusters formed by DBSCAN model are not so good and interpretable.\n",
    "- The Silhouette score of dbscan model with dimension reduction is 36%.\n",
    "- he Silhouette score of dbscan model without dimension reduction is 28%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3521e9c3",
   "metadata": {},
   "source": [
    "## CONCLUSION:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dde7aa72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette scores of Models:\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>With Dimension Reduction</th>\n",
       "      <th>Without Dimension Reduction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KMeans clustering</th>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hierarchical clustering</th>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBSCAN clustering</th>\n",
       "      <td>36.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         With Dimension Reduction  Without Dimension Reduction\n",
       "KMeans clustering                            55.0                         44.0\n",
       "Hierarchical clustering                      55.0                         44.0\n",
       "DBSCAN clustering                            36.0                         28.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Silhouette scores of Models:')\n",
    "pd.DataFrame([[kmeans_score,kmeans_score1],[agg_score,agg_score1],[dbscan_score,dbscan_score1]],columns=['With Dimension Reduction','Without Dimension Reduction'],index=['KMeans clustering','Hierarchical clustering','DBSCAN clustering'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e247fcd6",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- Models with Dimension reduction have good silhouette score than model without dimension reduction.\n",
    "- DBSCAN clustering has the least Silhoutte score than KMeans and Hierarchical clustering.\n",
    "- KMeans and Hierarchical clustering has about 55 % Silhouette score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2209c9f0",
   "metadata": {},
   "source": [
    "## Comparing the Cluster Formations of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2f9e06e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(2,2,tight_layout=True,figsize=(15,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(data[k_pred == 0, 1], data[k_pred == 0, 2], s = 50, c = 'red', label = 'Standard customers')\n",
    "plt.scatter(data[k_pred == 1, 1], data[k_pred == 1, 2], s = 50, c = 'blue', label = 'Careful customers')\n",
    "plt.scatter(data[k_pred == 2, 1], data[k_pred == 2, 2], s = 50, c = 'green', label = 'Target customers')\n",
    "plt.scatter(data[k_pred == 3, 1], data[k_pred == 3, 2], s = 50, c = 'cyan', label = 'Careless customers')\n",
    "plt.scatter(data[k_pred == 4, 1], data[k_pred == 4, 2], s = 50, c = 'magenta', label = 'Sensible customers')\n",
    "plt.title('Clusters of customers using Kmeans with PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.scatter(data[agg_pred == 0, 1], data[agg_pred == 0, 2], s = 50, c = 'red', label = 'Careful customers')\n",
    "plt.scatter(data[agg_pred == 1, 1], data[agg_pred == 1, 2], s = 50, c = 'blue', label = 'Target customers')\n",
    "plt.scatter(data[agg_pred == 2, 1], data[agg_pred == 2, 2], s = 50, c = 'green', label = 'Standard customers')\n",
    "plt.scatter(data[agg_pred == 3, 1], data[agg_pred == 3, 2], s = 50, c = 'cyan', label = 'Careless customers')\n",
    "plt.scatter(data[agg_pred == 4, 1], data[agg_pred == 4, 2], s = 50, c = 'magenta', label = 'Sensible customers')\n",
    "plt.title('Clusters of customers using Agglomerative with PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.scatter(data[dbscan_pred == -1, 1], data[dbscan_pred == -1, 2], s = 50, c = 'red', label = 'Outliers')\n",
    "plt.scatter(data[dbscan_pred == 0, 1], data[dbscan_pred == 0, 2], s = 50, c = 'blue', label = 'Careless customers')\n",
    "plt.scatter(data[dbscan_pred == 1, 1], data[dbscan_pred == 1, 2], s = 50, c = 'green', label = 'Sensible customers')\n",
    "plt.scatter(data[dbscan_pred == 2, 1], data[dbscan_pred == 2, 2], s = 50, c = 'cyan', label = 'Standard customers')\n",
    "plt.scatter(data[dbscan_pred == 3, 1], data[dbscan_pred == 3, 2], s = 50, c = 'magenta', label = 'Target customers')\n",
    "plt.title('Clusters of customers using DBSCAN with PCA',fontsize=16)\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5838294",
   "metadata": {},
   "source": [
    "### CONCLUSION:\n",
    "- Comparing the cluster formation and its segregations Hierarchical clustering has well segregated clusters and we can cluster mall customers in well manner.\n",
    "- KMeans Model also has good segregation of clusters but comparing to hierarchical clusters , the clusters are well segregated in agglomerative model.\n",
    "- In DBSCAN the clusters eventhough it identifies outliers, the mall customers who are outliers should also be considered to implement some innovative business plans."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e26f7e2",
   "metadata": {},
   "source": [
    "### BUSINESS IMPLEMENTATION: \n",
    "Using Agglomerative clustering model the customers can be identified according to the clusters and helps to identify those customers who would be interested in certain products, offers and services so that the stores can strategize its offerings in such a way that it targets only right customers for specific products."
   ]
  }
 ],
 "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",
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 },
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}