TASK2 K-means clustering..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Author : BLESSEL SUNNY\n",
    "\n",
    "\n",
    "## Prediction Using Unsupervised ML( TASK # 2)\n",
    "\n",
    "\n",
    "## TSFGRIPMAY21\n",
    "\n",
    "\n",
    "### QUESTION : Predict the optimum number of clusters and represent it visually.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "                                                               # Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "                                                                # Load the iris dataset\n",
    "iris = datasets.load_iris()\n",
    "iris_df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "iris_df.head()                                                    # See the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "# Finding the optimum number of clusters for k-means classification\n",
    "\n",
    "x = iris_df.iloc[:, [0, 1, 2, 3]].values\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', \n",
    "                    max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "                                                        # Plotting the results onto a line graph, \n",
    "                                                         # `allowing us to observe 'The elbow'\n",
    "plt.plot(range(1, 11), wcss) \n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')                                       # Within cluster sum of squares\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying kmeans to the dataset / Creating the kmeans classifier\n",
    "\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++',\n",
    "                max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x293d7d00308>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Visualising the clusters - On the first two columns\n",
    "\n",
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
    "            s = 100, c = 'orange', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
    "            s = 100, c = 'black', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
    "            s = 100, c = 'blue', label = 'Iris-virginica')\n",
    "plt.title(\" cluster\")\n",
    "\n",
    "# Plotting the centroids of the clusters\n",
    "\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
    "            s = 100, c = 'green', label = 'Centroids')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}