K-means_Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "625b0599",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "477301ef",
   "metadata": {},
   "outputs": [
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     "data": {
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       "<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": [
    "# loading the data\n",
    "data = pd.read_csv(r\"C:\\Users\\amit9\\Downloads\\archive\\Mall_Customers.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ec229e",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bfb019c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting 'Male' to 0 and Female to 1 in gender column\n",
    "data['Gender'] = data['Gender'].map({\"Male\" : 0,\n",
    "                                    \"Female\" : 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b04af19",
   "metadata": {},
   "outputs": [
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       "<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$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1       0   19                  15                      39\n",
       "1           2       0   21                  15                      81\n",
       "2           3       1   20                  16                       6\n",
       "3           4       1   23                  16                      77\n",
       "4           5       1   31                  17                      40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if it worked or not\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f6a00d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting the important features/ inputs\n",
    "#X = data.drop('CustomerID', axis = 1)\n",
    "X = data[['Annual Income (k$)', 'Spending Score (1-100)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4517be3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# object of MinMaxScaler\n",
    "mn = MinMaxScaler()\n",
    "\n",
    "# scaling the input data\n",
    "X = mn.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e22be9c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.3877551 ],\n",
       "       [0.        , 0.81632653],\n",
       "       [0.00819672, 0.05102041],\n",
       "       [0.00819672, 0.7755102 ],\n",
       "       [0.01639344, 0.39795918]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a88c510",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c582098",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# calculating the inertia for the number of clusters/ categories from 1 to 10\n",
    "inertia_lst = []\n",
    "\n",
    "# running a loop for the number of clusters\n",
    "for i in range(1,11,1):\n",
    "    # creating model\n",
    "    clf = KMeans(n_clusters = i, n_init = 15)\n",
    "    # training the model\n",
    "    clf.fit(X)\n",
    "    # calculating the inertia and storing it in inertia_lst list\n",
    "    inertia_lst.append(clf.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6f7d11d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# elbow method - it is simply where there is a bend in the plot are the possible number of optimal clusters one can make in this data\n",
    "\n",
    "# creating a line plot where axis represents: x - number of clusters, y - inertia\n",
    "plt.plot(np.arange(1,11,1), inertia_lst)\n",
    "plt.xticks(np.arange(1,11,1))\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "\n",
    "plt.title(\"Elbow Method\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f879b6ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the model with the task of dividing in 2 categories\n",
    "clf_2 = KMeans(n_clusters=2, n_init= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd2afd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the model with the task of dividing in 4 categories\n",
    "clf_4 = KMeans(n_clusters=4, n_init= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b5db658",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, n_init=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2, n_init=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=2, n_init=10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the \"clf_2\" model\n",
    "clf_2.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2080c98",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amit9\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4, n_init=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, n_init=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4, n_init=10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the \"clf_4\" model\n",
    "clf_4.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cc28d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 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, 0, 1, 0, 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, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the output (i.e. the category in which each row is divided) for \"clf_2\" model\n",
    "clf_2.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b99fed21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 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, 3, 2, 3, 2, 3, 2,\n",
       "       3, 2, 3, 2, 3, 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, 3, 2, 3, 2, 3, 2,\n",
       "       3, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the output (i.e. the category in which each row is divided) for \"clf_4\" model\n",
    "clf_4.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3faec06f",
   "metadata": {},
   "source": [
    "# Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5518186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33340205479521"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating accuracy of clf_2 model\n",
    "acc_2 = silhouette_score(X, clf_2.labels_)\n",
    "acc_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b5b81bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49620078745146784"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating accuracy of clf_4 model\n",
    "acc_4 = silhouette_score(X, clf_4.labels_)\n",
    "acc_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3cc1b0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Spending Score (1-100)'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reprenting the data clustering using a scatter plot\n",
    "import seaborn as sns\n",
    "sns.scatterplot(data = data, x = 'Gender', y = 'Spending Score (1-100)', hue = clf_4.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e0d7666",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}