Untitled7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('C:/Users/himanshu ranjan/Downloads/Clustering_ex.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Variable_1</th>\n",
       "      <th>Variable_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variable_1  Variable_2\n",
       "0         1.0         1.0\n",
       "1         1.5         2.0\n",
       "2         3.0         4.0\n",
       "3         5.0         7.0\n",
       "4         3.5         5.0\n",
       "5         4.5         5.0\n",
       "6         3.5         4.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "fig = plt.figure(figsize = (5,5))\n",
    "x = data['Variable_1']\n",
    "y = data['Variable_2']\n",
    "n = range(1,8)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x, y, marker='o', c='red', alpha=0.5)\n",
    "plt.grid()\n",
    "plt.xlabel('variable_1')\n",
    "plt.ylabel(\"variable_2\")\n",
    "for i, txt in enumerate(n):\n",
    "    ax.annotate(txt, (x[i],y[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "Kmeans = KMeans(n_clusters=2)\n",
    "Kmeans.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = Kmeans.predict(data)\n",
    "centroids = Kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.25, 1.5 ],\n",
       "       [3.9 , 5.1 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "fig = plt.figure(figsize =(5, 5))\n",
    "colmap = {1:'r', 2:'b'}\n",
    "colors = map(lambda x: colmap[x+1],labels)\n",
    "colors1 = list(colors)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x, y, color = colors1, alpha = 0.5, edgecolor= 'k')\n",
    "for idx, centroid in enumerate((centroids)):\n",
    "    plt.scatter(*centroid, color = colmap[idx+1])\n",
    "    \n",
    "for i, txt in enumerate(n):\n",
    "    ax.annotate(txt, (x[i],y[i]))\n",
    "plt.grid()\n",
    "plt.xlim(0, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}