Task1 Unsupervised Classification Using K-Means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1 : K- Means Clustering Algorithm "
   ]
  },
  {
   "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",
       "    }\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>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": "markdown",
   "metadata": {},
   "source": [
    "How do you find the optimum number of clusters for K Means? How does one determine the value of K?"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "It is called 'The elbow method' from the above graph, the optimum clusters is where the elbow occurs. This is when the within cluster sum of squares (WCSS) doesn't decrease significantly with every iteration.\n",
    "\n",
    "From this we choose the number of clusters as ** '3**'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying kmeans to the dataset / Creating the kmeans classifier\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x159a234a808>"
      ]
     },
     "execution_count": 4,
     "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",
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
    "            s = 100, c = 'red', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
    "            s = 100, c = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
    "            s = 100, c = 'green', label = 'Iris-virginica')\n",
    "\n",
    "# Plotting the centroids of the clusters\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
    "            s = 100, c = 'yellow', label = 'Centroids')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "The end of this task through step-wise implementation. "
   ]
  }
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