K Means Clustering Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering Implementation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this excercise we will try to cluster iris dataset using K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XYZ\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.DataFrame(data=iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_scaled = scaler.fit_transform(iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(iris_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = kmeans.labels_\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "       1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.01457897,  0.85326268, -1.30498732, -1.25489349],\n",
       "       [ 1.13597027,  0.08842168,  0.99615451,  1.01752612],\n",
       "       [-0.05021989, -0.88337647,  0.34773781,  0.2815273 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clusted_df = iris\n",
    "Clusted_df['Labels'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>Labels</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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",
       "      <td>0</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   \n",
       "\n",
       "   Labels  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clusted_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check Silhouette Score to validate our Clustering Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5910709491012561"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A positive value or close to 1 value indicate a good cluster. A negative or values closer to -1 indicate bad clusters.\n",
    "silhouette_score(iris,labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's do hyperparameter tuning for the value of K "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "inertia = []\n",
    "for n_cluster in range(1 , 20):\n",
    "    kmeans = KMeans(n_clusters = n_cluster ,init='k-means++', n_init = 10 ,max_iter=300, \n",
    "                        tol=0.0001,  random_state= 111  , algorithm='elkan')\n",
    "    kmeans.fit(iris_scaled)\n",
    "    inertia.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1 , figsize = (15 ,6))\n",
    "# plt.plot(np.arange(1 , 20) , inertia , 'o')\n",
    "plt.plot(np.arange(1 , 20) , inertia , color='green', marker='o', linestyle='dashed',linewidth=2, markersize=12,markerfacecolor='purple')\n",
    "plt.xlabel('Number of Clusters') , plt.ylabel('Inertia or WSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elbow curve is also suggesting 3 clusters. So our initial model is good. No need to create another model."
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}