KMeans-Clustering1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3d3f2f3-1ad1-40e5-bd78-2457ab235845",
   "metadata": {},
   "source": [
    "## K-Means Clustering WholeSale Customers data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca77d8f-fbc0-4de0-bd3e-f66adaf578be",
   "metadata": {},
   "source": [
    "### Importing necessary libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ac597e5a-8b35-42c5-bf72-e625548307b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done..\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "print(\"Done..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "577b0c09-b691-438a-81b4-088cc7366c6a",
   "metadata": {},
   "source": [
    "### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88ffd825-7ff8-4fcd-ad46-d599da022a0f",
   "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>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data = pd.read_csv('/home/bree/Documents/wholesale+customers/Wholesale customers data.csv')\n",
    "customer_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3fe38516-a057-4452-95e3-37f99c609538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 8)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ead7fd3c-6a42-4ad7-8a56-6f0d1e970187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Channel             0\n",
       "Region              0\n",
       "Fresh               0\n",
       "Milk                0\n",
       "Grocery             0\n",
       "Frozen              0\n",
       "Detergents_Paper    0\n",
       "Delicassen          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f965cb2-6991-4c4d-9959-9316544d35b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Channel', 'Region', 'Fresh', 'Milk', 'Grocery', 'Frozen',\n",
       "       'Detergents_Paper', 'Delicassen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9fb4630e-a4a4-430a-8850-84c624e0d823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
      "0    12669   9656     7561     214              2674        1338\n",
      "1     7057   9810     9568    1762              3293        1776\n",
      "2     6353   8808     7684    2405              3516        7844\n",
      "3    13265   1196     4221    6404               507        1788\n",
      "4    22615   5410     7198    3915              1777        5185\n",
      "..     ...    ...      ...     ...               ...         ...\n",
      "435  29703  12051    16027   13135               182        2204\n",
      "436  39228   1431      764    4510                93        2346\n",
      "437  14531  15488    30243     437             14841        1867\n",
      "438  10290   1981     2232    1038               168        2125\n",
      "439   2787   1698     2510      65               477          52\n",
      "\n",
      "[440 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "X= customer_data.iloc[:,[2,3,4,5,6,7]]\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc2b251-94dd-4a4d-9750-ea4b40de6a72",
   "metadata": {},
   "source": [
    "### Choosing the number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d120d4c3-e12d-4447-bee8-5915445ded2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "\n",
    "for i in range (1,11) :\n",
    "    kmeans = KMeans(n_clusters=i,init= 'k-means++',random_state= 42)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf8abbf-b9a4-4e92-8198-01e8bbf83693",
   "metadata": {},
   "source": [
    "### Elbow plot for WCSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88298524-c61e-403a-896a-57cc3bd9ab9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2309e5b2-bd32-40f4-a85e-97445d56713a",
   "metadata": {},
   "source": [
    "### Training the K-Means Clustering Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d13e86c2-5d91-4f80-8150-cf9e11df6285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 4 4 4 1 4 4 4 4 0 0 4 1 1 1 4 0 4 1 4 1 4 1 2 1 1 4 4 0 1 1 4 1 1 4 0 1\n",
      " 0 0 3 1 1 0 0 4 0 0 2 4 0 4 4 1 0 1 4 0 0 4 4 4 2 4 0 4 2 4 1 4 4 1 1 4 1\n",
      " 4 1 4 0 4 4 4 0 0 1 4 2 2 3 4 1 4 4 2 4 0 4 4 4 4 4 0 0 4 3 4 1 0 0 4 0 4\n",
      " 0 1 4 1 4 4 4 1 4 1 4 4 4 1 3 1 1 4 1 4 4 4 4 4 4 4 4 4 4 1 1 1 4 1 0 4 4\n",
      " 4 1 1 4 1 4 4 0 0 1 4 0 0 4 4 0 4 0 4 4 4 4 0 0 4 0 4 0 3 4 4 4 4 3 0 3 4\n",
      " 4 4 4 0 0 1 4 4 0 4 1 1 0 4 4 0 0 1 4 4 0 4 4 4 0 1 2 4 4 0 0 0 1 0 4 4 0\n",
      " 4 4 4 4 1 4 4 4 4 4 1 4 1 4 4 1 4 3 1 1 1 4 0 0 4 4 1 4 4 2 4 1 4 1 4 4 3\n",
      " 3 4 4 1 4 0 0 0 1 0 4 4 4 4 1 4 4 1 4 4 1 4 4 3 1 3 1 4 1 1 1 4 4 4 0 1 4\n",
      " 1 4 4 4 1 0 4 0 0 0 0 1 4 0 4 1 0 4 4 0 4 4 4 0 4 4 1 4 1 3 4 4 1 4 4 0 1\n",
      " 2 1 1 4 4 4 4 4 0 4 0 4 4 0 1 4 0 4 0 4 0 4 4 1 0 4 4 1 4 4 4 4 4 4 4 1 4\n",
      " 1 1 4 1 4 4 0 1 4 4 1 1 1 4 0 4 4 1 4 4 4 4 4 1 4 4 0 4 4 4 4 1 1 1 1 4 1\n",
      " 0 4 4 4 4 0 4 4 4 0 4 0 4 0 1 1 1 1 4 0 1 4 4 0 4 1 4 1 1 1 0 4 4]\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5,init= 'k-means++',random_state= 42)\n",
    "\n",
    "# return a label for each data point based on their cluster\n",
    "y = kmeans.fit_predict(X)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bbe1da3-7aaa-4257-ad9d-946ef9bf674a",
   "metadata": {},
   "source": [
    "### Visualizing all the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "074a766f-db4b-4d0f-886b-4dcfa392b2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "# Cluster 1\n",
    "plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], s=50, c='green', label='Cluster 1')\n",
    "# Cluster 2\n",
    "plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], s=50, c='red', label='Cluster 2')\n",
    "# Cluster 3\n",
    "plt.scatter(X[y == 2][:, 0], X[y == 2][:, 1], s=50, c='blue', label='Cluster 3')\n",
    "# Cluster 4\n",
    "plt.scatter(X[y == 3][:, 0], X[y == 3][:, 1], s=50, c='violet', label='Cluster 4')\n",
    "# Cluster 5\n",
    "plt.scatter(X[y == 4][:, 0], X[y == 4][:, 1], s=50, c='orange', label='Cluster 5')\n",
    "\n",
    "# Plot the centroids\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=50, c='cyan', label='Centroids')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b230f6-3710-4a12-8033-8df51eb14956",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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