kmean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import random as rd\n",
    "from copy import deepcopy\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(\"Mall_Customers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</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   Genre  Age  AnnualIncome  SpendingScore\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keeping only the desired column\n",
    "col = ['Genre', 'Age', 'AnnualIncome', 'SpendingScore']\n",
    "customer_features = df[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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       "        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>Genre</th>\n",
       "      <th>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": [
       "    Genre  Age  AnnualIncome  SpendingScore\n",
       "0    Male   19            15             39\n",
       "1    Male   21            15             81\n",
       "2  Female   20            16              6\n",
       "3  Female   23            16             77\n",
       "4  Female   31            17             40"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_features = pd.get_dummies(customer_features, \n",
    "                                     columns= ['Genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_features['cluster']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Age</th>\n",
       "      <th>AnnualIncome</th>\n",
       "      <th>SpendingScore</th>\n",
       "      <th>Genre_Female</th>\n",
       "      <th>Genre_Male</th>\n",
       "      <th>cluster</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>45</td>\n",
       "      <td>126</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>32</td>\n",
       "      <td>126</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>32</td>\n",
       "      <td>137</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>30</td>\n",
       "      <td>137</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  AnnualIncome  SpendingScore  Genre_Female  Genre_Male  cluster\n",
       "0     19            15             39             0           1        0\n",
       "1     21            15             81             0           1        0\n",
       "2     20            16              6             1           0        0\n",
       "3     23            16             77             1           0        0\n",
       "4     31            17             40             1           0        0\n",
       "..   ...           ...            ...           ...         ...      ...\n",
       "195   35           120             79             1           0        0\n",
       "196   45           126             28             1           0        0\n",
       "197   32           126             74             0           1        0\n",
       "198   32           137             18             0           1        0\n",
       "199   30           137             83             0           1        0\n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = np.mean(customer_features, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=customer_features\n",
    "X = data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 19,  15,  39,   0,   1,   0],\n",
       "       [ 21,  15,  81,   0,   1,   0],\n",
       "       [ 20,  16,   6,   1,   0,   0],\n",
       "       ...,\n",
       "       [ 32, 126,  74,   0,   1,   0],\n",
       "       [ 32, 137,  18,   0,   1,   0],\n",
       "       [ 30, 137,  83,   0,   1,   0]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "sns.scatterplot(X[:,0], X[:, 1])\n",
    "plt.xlabel('Annual Income')\n",
    "plt.ylabel('Spending Score ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kmeans(X, k):\n",
    "    diff = 1\n",
    "    #making a cluster array of zeros of size of number of datapoints\n",
    "    cluster = np.zeros(X.shape[0])\n",
    "    #this gets random values for each column k times\n",
    "    centroids = data.sample(n=k).values\n",
    "    while diff:\n",
    "        for i, row in enumerate(X):\n",
    "            #minimum distance set at positive infinity\n",
    "            mn_dist = float('inf')\n",
    "            #this loop goes over each of the centroid\n",
    "            for idx, centroid in enumerate(centroids):\n",
    "                #calculating eucludiean distance between a centeroid point and datapoint\n",
    "                d = np.sqrt((centroid[0]-row[0])**2 + (centroid[1]-row[1])**2)\n",
    "                #this condition works if euclidean distance of datapoint(d1) and centeroid(c2) is less than mn_dist than \n",
    "                #earlier centroid(c1) datapoint is assigned the respective centroid \n",
    "                if mn_dist > d:\n",
    "                    mn_dist = d\n",
    "                    cluster[i] = idx\n",
    "        #after placing points in respective centeroids the new_centroid values calculated by taking mean\n",
    "        new_centroids = pd.DataFrame(X).groupby(by=cluster).mean().values\n",
    "        #closing condition \n",
    "        if np.count_nonzero(centroids-new_centroids) == 0:\n",
    "            print(new_centroids)\n",
    "            diff = 0\n",
    "        else:\n",
    "            centroids = new_centroids\n",
    "    return centroids, cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[49 33 14  1  0  0]\n",
      " [32 87 63  0  1  0]\n",
      " [38 67 40  1  0  0]\n",
      " [66 63 48  0  1  0]\n",
      " [31 72 71  1  0  0]]\n"
     ]
    }
   ],
   "source": [
    "k=5\n",
    "centroids = data.sample(n=k).values\n",
    "print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 25.73529412  52.41176471  52.94117647   0.61764706   0.38235294\n",
      "    0.        ]\n",
      " [ 35.89830508  77.3559322   51.           0.50847458   0.49152542\n",
      "    0.        ]\n",
      " [ 37.54545455 108.18181818  52.           0.59090909   0.40909091\n",
      "    0.        ]\n",
      " [ 56.38461538  50.13461538  43.15384615   0.51923077   0.48076923\n",
      "    0.        ]\n",
      " [ 30.87878788  23.60606061  55.84848485   0.63636364   0.36363636\n",
      "    0.        ]]\n"
     ]
    }
   ],
   "source": [
    "k = 5\n",
    "centroids, cluster = kmeans(X, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "sns.scatterplot(X[:,0], X[:, 1],hue=cluster)\n",
    "sns.scatterplot(centroids[:,0], centroids[:, 1], s=100, color='blue')\n",
    "plt.xlabel('Annual Income ')\n",
    "plt.ylabel('Spending Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}