week 6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 6 - Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”.\n",
    "\n",
    "Clustering is crucial because it determines the intrinsic grouping among the present unlabeled data. They make some assumptions about data points to constitute their similarity. Each hypothesis will construct different but equally valid clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the difference between Clustering and Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classification is the result of supervised learning which means that there is a known label that you want the system to generate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clustering is the result of unsupervised learning which means that you’ve seen lots of examples, but don’t have labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/cluster_v_class.jpg\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K_Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-means clustering is a type of unsupervised learning, which is used with unlabeled dataset. The goal of this algorithm is to find K groups in the data. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity. The results of the K-means clustering algorithm are:\n",
    "\n",
    "The centroids of the K clusters, which can be used to label new data\n",
    "Labels for the training data (each data point is assigned to a single cluster)\n",
    "K-means works by defining spherical clusters that are separable in a way so that the mean value converges towards the cluster center. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "STEP 1 : Select the number of clusters  k  that you think is the optimal number.  \n",
    "STEP 2 : Initialize  k  points as \"centroids\" randomly within the space of our data.  \n",
    "STEP 3 : Attribute each observation to its closest centroid.  \n",
    "STEP 4 : Update the centroids to the center of all the attributed set of observations.  \n",
    "STEP 5 : Repeat steps 3 and 4 a fixed number of times or until all of the centroids are stable(i.e. no longer change in step4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/Kmeans_animation.gif\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now after reading this algorithm you must be thinking <u>how do I select the number of clusters k</u> and <u>if the initialization of clusters centroid is not appropriate wouldn't it lead the K-Means to give an arbitrarily bad clusters</u>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. To select the number of clusters k there's this <b>elbow curve</b> and <b>Silhouette score</b> method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Elbow curve method is basically a graph method ,where  the x-axis will represent the number of clusters and the y-axis will be an evaluation metric-inertia(inertia calculates the sum of distances of all the points within a cluster from the centroid of that cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Silhouette score is a way to measure how close each point in a cluster is to the points in its neighboring clusters. Its a neat way to find out the optimum value for k during k-means clustering. It is calculated for each instance and the formula goes like this:  \n",
    "<b>Silhouette Coefficient = (x-y)/ max(x,y)</b>  \n",
    "where, y is the mean intra cluster distance and x depicts mean nearest cluster distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note</b> - We will be using both of these methods later in this week which help you understand them better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. To correctly initialize the centroid of clusters, we have K-Means++ .It specifies a procedure to initialize the cluster centers before moving forward with the standard k-means clustering algorithm.  \n",
    "You can read more about it [here](https://en.wikipedia.org/wiki/K-means%2B%2B#:~:text=In%20data%20mining%2C%20k%2Dmeans,the%20k%2Dmeans%20clustering%20algorithm.)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/kmeans++.gif\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Okay now let's move on to some implementation part"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First we will be performing clustering on a smaller dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score\n",
    "#%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('data/Mall_Customers.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
       "count  200.000000  200.000000          200.000000              200.000000\n",
       "mean   100.500000   38.850000           60.560000               50.200000\n",
       "std     57.879185   13.969007           26.264721               25.823522\n",
       "min      1.000000   18.000000           15.000000                1.000000\n",
       "25%     50.750000   28.750000           41.500000               34.750000\n",
       "50%    100.500000   36.000000           61.500000               50.000000\n",
       "75%    150.250000   49.000000           78.000000               73.000000\n",
       "max    200.000000   70.000000          137.000000               99.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                 int64\n",
       "Gender                    object\n",
       "Age                        int64\n",
       "Annual Income (k$)         int64\n",
       "Spending Score (1-100)     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                0\n",
       "Gender                    0\n",
       "Age                       0\n",
       "Annual Income (k$)        0\n",
       "Spending Score (1-100)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data visulaization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "n=0\n",
    "for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:\n",
    "    n+=1\n",
    "    plt.subplot(1,3,n)\n",
    "    sns.distplot(dataset[x] , bins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure( figsize = (7 , 4))\n",
    "sns.countplot(x='Gender', data=dataset);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's apply clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will be applying clustering on the basis of Annual Income (k$)\t and Spending Score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.iloc[:, [3, 4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "inertia=[]\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "    kmeans.fit(x)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),inertia)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Innertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we have used the <b>elbow method</b> to find the suitable number of clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, from this graph we choose the cluster value where this decrease in inertia value becomes constant that value can be chosen as the right cluster value for our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So from this graph we see that 5 is the point where the curve changes drastically , it starts to become constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "kmeans = KMeans(n_clusters = 5, init = 'k-means++')\n",
    "y_kmeans = kmeans.fit_predict(x)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(x[y_kmeans == 3, 0], x[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(x[y_kmeans == 4, 0], x[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:, 1], s = 50, c = 'yellow' , label = 'centeroid')\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Annual Income (k$)')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 1 - High spending High income  \n",
    "Cluster 2 - Medium spending Medium income  \n",
    "Cluster 3 - Low spending Low income  \n",
    "Cluster 4 - High spending Low income  \n",
    "Cluster 5 - Low spending High income"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's apply clustering on Age and Annual Income (k$)  ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = dataset.iloc[:, [2,3]].values       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "inertia=[]\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "    kmeans.fit(x1)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),inertia)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Innertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will be taking 6 as the optimum number of clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "kmeans = KMeans(n_clusters = 6, init = 'k-means++')\n",
    "y_kmeans = kmeans.fit_predict(x1)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(x1[y_kmeans == 0, 0], x1[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(x1[y_kmeans == 1, 0], x1[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(x1[y_kmeans == 2, 0], x1[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(x1[y_kmeans == 3, 0], x1[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(x1[y_kmeans == 4, 0], x1[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
    "plt.scatter(x1[y_kmeans == 5, 0], x1[y_kmeans == 5, 1], s = 100, c = 'black', label = 'Cluster 6')\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:, 1], s = 200, c = 'yellow' , label = 'centeroid')\n",
    "plt.title('Clusters of customers')\n",
    "plt.ylabel('Annual Income (k$)')\n",
    "plt.xlabel('Age')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 1 - Average young age earners      \n",
    "Cluster 2 - Low old age earners  \n",
    "Cluster 3 - Average medium age earners  \n",
    "Cluster 4 - Low young age earners  \n",
    "Cluster 5 - Average old age earners  \n",
    "Cluster 6 - High medium age earners "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's apply clustering on Age and Spending Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = dataset.iloc[:, [2,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "inertia=[]\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "    kmeans.fit(x2)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),inertia)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Innertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will be taking 4 as the optimum number of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "kmeans = KMeans(n_clusters = 4, init = 'k-means++')\n",
    "y_kmeans = kmeans.fit_predict(x2)\n",
    "\n",
    "# Visualising the clusters\n",
    "plt.scatter(x2[y_kmeans == 0, 0], x2[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "plt.scatter(x2[y_kmeans == 1, 0], x2[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "plt.scatter(x2[y_kmeans == 2, 0], x2[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "plt.scatter(x2[y_kmeans == 3, 0], x2[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:, 1], s = 200, c = 'yellow' , label = 'centeroid')\n",
    "plt.title('Clusters of customers')\n",
    "plt.ylabel('Spending score')\n",
    "plt.xlabel('Age')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 1 - Young average spender  \n",
    "Cluster 2 - Young high spender  \n",
    "Cluster 3 - Low spender  \n",
    "Cluster 4 - Old average spender  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Till now we have visualized the clusters in 2d as we only had 2 features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now let's take all those 3 features and create a 3d plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = dataset.iloc[:, [2,3,4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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    "inertia=[]\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "    kmeans.fit(x3)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),inertia)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Innertia')\n",
    "plt.show()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as we can see from the graph we can use 5 or 6 both the values but here I am taking 6 as the optimum no of clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters = 6, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "kmeans.fit(x3)\n",
    "labels = kmeans.labels_\n",
    "centroids = kmeans.cluster_centers_"
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  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('08b7b041-268e-4aaa-b60d-b925e022f1e1');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "py.offline.init_notebook_mode(connected = True)\n",
    "dataset['label'] =  labels\n",
    "trace1 = go.Scatter3d(\n",
    "    x= dataset['Age'],\n",
    "    y= dataset['Spending Score (1-100)'],\n",
    "    z= dataset['Annual Income (k$)'],\n",
    "    mode='markers',\n",
    "     marker=dict(\n",
    "        color = dataset['label'], \n",
    "        size= 20,\n",
    "        line=dict(\n",
    "            color= dataset['label'],\n",
    "            width= 12\n",
    "        ),\n",
    "        opacity=0.8\n",
    "     )\n",
    ")\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    title= 'Clusters',\n",
    "    scene = dict(\n",
    "            xaxis = dict(title  = 'Age'),\n",
    "            yaxis = dict(title  = 'Spending Score'),\n",
    "            zaxis = dict(title  = 'Annual Income')\n",
    "        )\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.offline.iplot(fig)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>NOTE</b> : Please run the notebook by yourself to see the interactive 3D plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Till now we have applied clustering on a small dataset which had low no of features so they were easy to visualize on graph but this is not case in real life , in real life we have hundreds of features for which the clusters cannot be visualized for every feature ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So to solve this problem we use dimension reduction techniques like <b>PCA(Principal Component Analysis)</b> . Which can reduce the total no of feature to our desired no of features. We will read about these dimension reduction techniques in depth in the next week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now let's see a small example so that it can become clear to you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clustering on a large dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Ash_Alcanity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_Phenols</th>\n",
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       "      <th>Color_Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Alcohol  Malic_Acid   Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
       "0    14.23        1.71  2.43          15.6        127           2.80   \n",
       "1    13.20        1.78  2.14          11.2        100           2.65   \n",
       "2    13.16        2.36  2.67          18.6        101           2.80   \n",
       "3    14.37        1.95  2.50          16.8        113           3.85   \n",
       "4    13.24        2.59  2.87          21.0        118           2.80   \n",
       "\n",
       "   Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity   Hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   OD280  Proline  \n",
       "0   3.92     1065  \n",
       "1   3.40     1050  \n",
       "2   3.17     1185  \n",
       "3   3.45     1480  \n",
       "4   2.93      735  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('data/wine-clustering.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Alcohol  Malic_Acid         Ash  Ash_Alcanity   Magnesium  \\\n",
       "count  178.000000  178.000000  178.000000    178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517     19.494944   99.741573   \n",
       "std      0.811827    1.117146    0.274344      3.339564   14.282484   \n",
       "min     11.030000    0.740000    1.360000     10.600000   70.000000   \n",
       "25%     12.362500    1.602500    2.210000     17.200000   88.000000   \n",
       "50%     13.050000    1.865000    2.360000     19.500000   98.000000   \n",
       "75%     13.677500    3.082500    2.557500     21.500000  107.000000   \n",
       "max     14.830000    5.800000    3.230000     30.000000  162.000000   \n",
       "\n",
       "       Total_Phenols  Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  \\\n",
       "count     178.000000  178.000000            178.000000       178.000000   \n",
       "mean        2.295112    2.029270              0.361854         1.590899   \n",
       "std         0.625851    0.998859              0.124453         0.572359   \n",
       "min         0.980000    0.340000              0.130000         0.410000   \n",
       "25%         1.742500    1.205000              0.270000         1.250000   \n",
       "50%         2.355000    2.135000              0.340000         1.555000   \n",
       "75%         2.800000    2.875000              0.437500         1.950000   \n",
       "max         3.880000    5.080000              0.660000         3.580000   \n",
       "\n",
       "       Color_Intensity         Hue       OD280      Proline  \n",
       "count       178.000000  178.000000  178.000000   178.000000  \n",
       "mean          5.058090    0.957449    2.611685   746.893258  \n",
       "std           2.318286    0.228572    0.709990   314.907474  \n",
       "min           1.280000    0.480000    1.270000   278.000000  \n",
       "25%           3.220000    0.782500    1.937500   500.500000  \n",
       "50%           4.690000    0.965000    2.780000   673.500000  \n",
       "75%           6.200000    1.120000    3.170000   985.000000  \n",
       "max          13.000000    1.710000    4.000000  1680.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alcohol                 float64\n",
       "Malic_Acid              float64\n",
       "Ash                     float64\n",
       "Ash_Alcanity            float64\n",
       "Magnesium                 int64\n",
       "Total_Phenols           float64\n",
       "Flavanoids              float64\n",
       "Nonflavanoid_Phenols    float64\n",
       "Proanthocyanins         float64\n",
       "Color_Intensity         float64\n",
       "Hue                     float64\n",
       "OD280                   float64\n",
       "Proline                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alcohol                 0\n",
       "Malic_Acid              0\n",
       "Ash                     0\n",
       "Ash_Alcanity            0\n",
       "Magnesium               0\n",
       "Total_Phenols           0\n",
       "Flavanoids              0\n",
       "Nonflavanoid_Phenols    0\n",
       "Proanthocyanins         0\n",
       "Color_Intensity         0\n",
       "Hue                     0\n",
       "OD280                   0\n",
       "Proline                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1296 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,18))\n",
    "n=0\n",
    "for x in dataset.columns:\n",
    "    n+=1\n",
    "    plt.subplot(5,3,n)\n",
    "    sns.distplot(dataset[x] , bins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Ash_Alcanity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid_Phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.518613</td>\n",
       "      <td>-0.562250</td>\n",
       "      <td>0.232053</td>\n",
       "      <td>-1.169593</td>\n",
       "      <td>1.913905</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>1.034819</td>\n",
       "      <td>-0.659563</td>\n",
       "      <td>1.224884</td>\n",
       "      <td>0.251717</td>\n",
       "      <td>0.362177</td>\n",
       "      <td>1.847920</td>\n",
       "      <td>1.013009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.246290</td>\n",
       "      <td>-0.499413</td>\n",
       "      <td>-0.827996</td>\n",
       "      <td>-2.490847</td>\n",
       "      <td>0.018145</td>\n",
       "      <td>0.568648</td>\n",
       "      <td>0.733629</td>\n",
       "      <td>-0.820719</td>\n",
       "      <td>-0.544721</td>\n",
       "      <td>-0.293321</td>\n",
       "      <td>0.406051</td>\n",
       "      <td>1.113449</td>\n",
       "      <td>0.965242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.196879</td>\n",
       "      <td>0.021231</td>\n",
       "      <td>1.109334</td>\n",
       "      <td>-0.268738</td>\n",
       "      <td>0.088358</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>1.215533</td>\n",
       "      <td>-0.498407</td>\n",
       "      <td>2.135968</td>\n",
       "      <td>0.269020</td>\n",
       "      <td>0.318304</td>\n",
       "      <td>0.788587</td>\n",
       "      <td>1.395148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.691550</td>\n",
       "      <td>-0.346811</td>\n",
       "      <td>0.487926</td>\n",
       "      <td>-0.809251</td>\n",
       "      <td>0.930918</td>\n",
       "      <td>2.491446</td>\n",
       "      <td>1.466525</td>\n",
       "      <td>-0.981875</td>\n",
       "      <td>1.032155</td>\n",
       "      <td>1.186068</td>\n",
       "      <td>-0.427544</td>\n",
       "      <td>1.184071</td>\n",
       "      <td>2.334574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.295700</td>\n",
       "      <td>0.227694</td>\n",
       "      <td>1.840403</td>\n",
       "      <td>0.451946</td>\n",
       "      <td>1.281985</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>0.663351</td>\n",
       "      <td>0.226796</td>\n",
       "      <td>0.401404</td>\n",
       "      <td>-0.319276</td>\n",
       "      <td>0.362177</td>\n",
       "      <td>0.449601</td>\n",
       "      <td>-0.037874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alcohol  Malic_Acid       Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
       "0  1.518613   -0.562250  0.232053     -1.169593   1.913905       0.808997   \n",
       "1  0.246290   -0.499413 -0.827996     -2.490847   0.018145       0.568648   \n",
       "2  0.196879    0.021231  1.109334     -0.268738   0.088358       0.808997   \n",
       "3  1.691550   -0.346811  0.487926     -0.809251   0.930918       2.491446   \n",
       "4  0.295700    0.227694  1.840403      0.451946   1.281985       0.808997   \n",
       "\n",
       "   Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity  \\\n",
       "0    1.034819             -0.659563         1.224884         0.251717   \n",
       "1    0.733629             -0.820719        -0.544721        -0.293321   \n",
       "2    1.215533             -0.498407         2.135968         0.269020   \n",
       "3    1.466525             -0.981875         1.032155         1.186068   \n",
       "4    0.663351              0.226796         0.401404        -0.319276   \n",
       "\n",
       "        Hue     OD280   Proline  \n",
       "0  0.362177  1.847920  1.013009  \n",
       "1  0.406051  1.113449  0.965242  \n",
       "2  0.318304  0.788587  1.395148  \n",
       "3 -0.427544  1.184071  2.334574  \n",
       "4  0.362177  0.449601 -0.037874  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.columns]=StandardScaler().fit_transform(dataset)\n",
    "dataset=pd.DataFrame(dataset)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative variance explained by 2 principal components: 55.41%\n"
     ]
    }
   ],
   "source": [
    "pca_2 = PCA(n_components=2)\n",
    "pca_2_result = pca_2.fit_transform(dataset)\n",
    "\n",
    "print ('Cumulative variance explained by 2 principal components: {:.2%}'.format(np.sum(pca_2.explained_variance_ratio_)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "inertia = []\n",
    "for i in range(1,11):\n",
    "    kmeans = KMeans(n_clusters=i,\n",
    "               init='k-means++',\n",
    "               n_init=15,\n",
    "               max_iter=500,\n",
    "               random_state=17)\n",
    "    kmeans.fit(dataset)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "plt.plot(range(1,11),inertia)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Innertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So here we can see 3 is the optimum no of clusters that can be formed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Silhouette score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the optimal no of clusters we have another method called Silhouette method. Usually silhouette method is more accurate than the elbow method    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try to compute the silhouette score. Here, the bigger score the better the clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette = {}\n",
    "for i in range(2,10):\n",
    "    kmeans = KMeans(n_clusters=i,\n",
    "               init='k-means++',\n",
    "               n_init=15,\n",
    "               max_iter=500,\n",
    "               random_state=17)\n",
    "    kmeans.fit(dataset)\n",
    "    silhouette[i] = silhouette_score(dataset, kmeans.labels_, metric='euclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(range(len(silhouette)), list(silhouette.values()), align='center',width=0.5)\n",
    "plt.xticks(range(len(silhouette)), list(silhouette.keys()))\n",
    "plt.grid()\n",
    "plt.title('Silhouette Score',fontweight='bold')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this method also tells us that 3 is the optimal no of clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read more about these methods [here](https://gdcoder.com/silhouette-analysis-vs-elbow-method-vs-davies-bouldin-index-selecting-the-optimal-number-of-clusters-for-kmeans-clustering/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    65\n",
       "0    62\n",
       "2    51\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3,init='k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
    "labels = kmeans.fit_predict(dataset)\n",
    "pd.Series(labels).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = kmeans.cluster_centers_\n",
    "pca_centroids = pca_2.transform(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "plt.scatter(x=pca_2_result[:, 0], y=pca_2_result[:, 1], c=labels, cmap='autumn')\n",
    "plt.scatter(pca_centroids[:, 0], pca_centroids[:, 1],s=200 ,color='black')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('Clustered Data (PCA visualization)',fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to see Hierarchical Clustering .  \n",
    "This type of clustering basically works on the basis of minimize the distance only i.e it is a distance based clustering method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hierarchical clustering starts by assigning all data points as their own cluster. As the name suggests it builds the hierarchy and in the next step, it combines the two nearest data point and merges it together to one cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hierarchical Clustering is of 2 types :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <b>Agglomerative Hierarchical Clustering</b> - In this technique, initially each data point is considered as an individual cluster. At each iteration, the similar clusters merge with other clusters until one cluster "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. <b>Divisive Hierarchical Clustering</b> - In Divisive is a top-down clustering method where we assign all of the observations to a single cluster and then partition the cluster to two least similar clusters. Finally, we proceed recursively on each cluster until there is one cluster for each observation. So this clustering approach is exactly opposite to Agglomerative clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/hierarchial_approach.jfif\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main difference between the two methods of hierarchical Clustering is only the approach we take but <b>Divisive clustering</b> is rarely done in practice so in this week we are only going to study <b>Agglomerative Hierarchical Clustering</b>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Agglomerative Hierarchical Clustering algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1 : Assign each data point to its own cluster.  \n",
    "Step 2 : Find closest pair of cluster using euclidean distance and merge them in to single cluster.  \n",
    "Step 3 : Calculate distance between two nearest clusters and combine until all items are clustered in to a single cluster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of this clustering method is represented on a <b>Dendrogram</b> which tells us how many cluster will be there.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/hierarch.gif\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to understand this better in the next part."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy.cluster.hierarchy import dendrogram,linkage\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be using the same <b>Wine dataset</b> that I have already used in k-means and I will be also be assuming that I have already done Data exploration and visualization part on the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Ash_Alcanity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid_Phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.518613</td>\n",
       "      <td>-0.562250</td>\n",
       "      <td>0.232053</td>\n",
       "      <td>-1.169593</td>\n",
       "      <td>1.913905</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>1.034819</td>\n",
       "      <td>-0.659563</td>\n",
       "      <td>1.224884</td>\n",
       "      <td>0.251717</td>\n",
       "      <td>0.362177</td>\n",
       "      <td>1.847920</td>\n",
       "      <td>1.013009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.246290</td>\n",
       "      <td>-0.499413</td>\n",
       "      <td>-0.827996</td>\n",
       "      <td>-2.490847</td>\n",
       "      <td>0.018145</td>\n",
       "      <td>0.568648</td>\n",
       "      <td>0.733629</td>\n",
       "      <td>-0.820719</td>\n",
       "      <td>-0.544721</td>\n",
       "      <td>-0.293321</td>\n",
       "      <td>0.406051</td>\n",
       "      <td>1.113449</td>\n",
       "      <td>0.965242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.196879</td>\n",
       "      <td>0.021231</td>\n",
       "      <td>1.109334</td>\n",
       "      <td>-0.268738</td>\n",
       "      <td>0.088358</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>1.215533</td>\n",
       "      <td>-0.498407</td>\n",
       "      <td>2.135968</td>\n",
       "      <td>0.269020</td>\n",
       "      <td>0.318304</td>\n",
       "      <td>0.788587</td>\n",
       "      <td>1.395148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.691550</td>\n",
       "      <td>-0.346811</td>\n",
       "      <td>0.487926</td>\n",
       "      <td>-0.809251</td>\n",
       "      <td>0.930918</td>\n",
       "      <td>2.491446</td>\n",
       "      <td>1.466525</td>\n",
       "      <td>-0.981875</td>\n",
       "      <td>1.032155</td>\n",
       "      <td>1.186068</td>\n",
       "      <td>-0.427544</td>\n",
       "      <td>1.184071</td>\n",
       "      <td>2.334574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.295700</td>\n",
       "      <td>0.227694</td>\n",
       "      <td>1.840403</td>\n",
       "      <td>0.451946</td>\n",
       "      <td>1.281985</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>0.663351</td>\n",
       "      <td>0.226796</td>\n",
       "      <td>0.401404</td>\n",
       "      <td>-0.319276</td>\n",
       "      <td>0.362177</td>\n",
       "      <td>0.449601</td>\n",
       "      <td>-0.037874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alcohol  Malic_Acid       Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
       "0  1.518613   -0.562250  0.232053     -1.169593   1.913905       0.808997   \n",
       "1  0.246290   -0.499413 -0.827996     -2.490847   0.018145       0.568648   \n",
       "2  0.196879    0.021231  1.109334     -0.268738   0.088358       0.808997   \n",
       "3  1.691550   -0.346811  0.487926     -0.809251   0.930918       2.491446   \n",
       "4  0.295700    0.227694  1.840403      0.451946   1.281985       0.808997   \n",
       "\n",
       "   Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity  \\\n",
       "0    1.034819             -0.659563         1.224884         0.251717   \n",
       "1    0.733629             -0.820719        -0.544721        -0.293321   \n",
       "2    1.215533             -0.498407         2.135968         0.269020   \n",
       "3    1.466525             -0.981875         1.032155         1.186068   \n",
       "4    0.663351              0.226796         0.401404        -0.319276   \n",
       "\n",
       "        Hue     OD280   Proline  \n",
       "0  0.362177  1.847920  1.013009  \n",
       "1  0.406051  1.113449  0.965242  \n",
       "2  0.318304  0.788587  1.395148  \n",
       "3 -0.427544  1.184071  2.334574  \n",
       "4  0.362177  0.449601 -0.037874  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_sca=StandardScaler()\n",
    "df_cluster=dataset.copy()\n",
    "df_cluster[df_cluster.columns]=std_sca.fit_transform(df_cluster)\n",
    "df_cluster.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move further , we should know a little about <b>Linkage methods</b>.  \n",
    "Linkage methods are a way to measure the distance between clusters in order to decide the rules for clustering.  \n",
    "They are Complete-linkage, Single-linkage, Average-linkage, Centroid-linkage, average, weighted, median and ward.\n",
    "If you want to know more about Linkage you can read [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z=linkage(df_cluster,method=\"ward\")\n",
    "plt.figure(figsize=(15,10))\n",
    "dendrogram(Z,leaf_rotation=90,p=5,color_threshold=20,leaf_font_size=10,truncate_mode='level')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what you call a <b>Dendrogram</b>.  \n",
    "A Dendrogram is basically a type of tree diagram showing hierarchical relationships between different sets of data.\n",
    "A Dendrogram contains the memory of hierarchical clustering algorithm, so just by looking at the Dendrgram you can tell how the cluster is formed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So to find the number of clusters formed you have to look at the longest vertical line which is not intercepted by a horizontal line.After we find out the longest vertical line we have to split it horizontally and the no of vertical lines it touches tells you how many clusters will be there.  \n",
    "So in our case a horizontal line can be drawn on the first blue line from the left which will cut 3 vertical lines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z=linkage(df_cluster,method=\"ward\")\n",
    "plt.figure(figsize=(15,10))\n",
    "dendrogram(Z,leaf_rotation=90,p=5,color_threshold=20,leaf_font_size=10,truncate_mode='level')\n",
    "plt.axhline(y=20, color='r', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><b>OR</b></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the <b>Silhouette score</b> that we used above to find the no of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "                        connectivity=None, distance_threshold=None,\n",
       "                        linkage='ward', memory=None, n_clusters=3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "hc = AgglomerativeClustering(n_clusters = 3, affinity = 'euclidean', linkage = 'ward')\n",
    "hc.fit(df_cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 0, 0, 0, 2,\n",
       "       2, 0, 1, 0, 1, 2, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hc.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Alcohol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Ash_Alcanity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid_Phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_Intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.518613</td>\n",
       "      <td>-0.562250</td>\n",
       "      <td>0.232053</td>\n",
       "      <td>-1.169593</td>\n",
       "      <td>1.913905</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>1.034819</td>\n",
       "      <td>-0.659563</td>\n",
       "      <td>1.224884</td>\n",
       "      <td>0.251717</td>\n",
       "      <td>0.362177</td>\n",
       "      <td>1.847920</td>\n",
       "      <td>1.013009</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.246290</td>\n",
       "      <td>-0.499413</td>\n",
       "      <td>-0.827996</td>\n",
       "      <td>-2.490847</td>\n",
       "      <td>0.018145</td>\n",
       "      <td>0.568648</td>\n",
       "      <td>0.733629</td>\n",
       "      <td>-0.820719</td>\n",
       "      <td>-0.544721</td>\n",
       "      <td>-0.293321</td>\n",
       "      <td>0.406051</td>\n",
       "      <td>1.113449</td>\n",
       "      <td>0.965242</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.196879</td>\n",
       "      <td>0.021231</td>\n",
       "      <td>1.109334</td>\n",
       "      <td>-0.268738</td>\n",
       "      <td>0.088358</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>1.215533</td>\n",
       "      <td>-0.498407</td>\n",
       "      <td>2.135968</td>\n",
       "      <td>0.269020</td>\n",
       "      <td>0.318304</td>\n",
       "      <td>0.788587</td>\n",
       "      <td>1.395148</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.691550</td>\n",
       "      <td>-0.346811</td>\n",
       "      <td>0.487926</td>\n",
       "      <td>-0.809251</td>\n",
       "      <td>0.930918</td>\n",
       "      <td>2.491446</td>\n",
       "      <td>1.466525</td>\n",
       "      <td>-0.981875</td>\n",
       "      <td>1.032155</td>\n",
       "      <td>1.186068</td>\n",
       "      <td>-0.427544</td>\n",
       "      <td>1.184071</td>\n",
       "      <td>2.334574</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.295700</td>\n",
       "      <td>0.227694</td>\n",
       "      <td>1.840403</td>\n",
       "      <td>0.451946</td>\n",
       "      <td>1.281985</td>\n",
       "      <td>0.808997</td>\n",
       "      <td>0.663351</td>\n",
       "      <td>0.226796</td>\n",
       "      <td>0.401404</td>\n",
       "      <td>-0.319276</td>\n",
       "      <td>0.362177</td>\n",
       "      <td>0.449601</td>\n",
       "      <td>-0.037874</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Alcohol  Malic_Acid       Ash  Ash_Alcanity  Magnesium  Total_Phenols  \\\n",
       "0  1.518613   -0.562250  0.232053     -1.169593   1.913905       0.808997   \n",
       "1  0.246290   -0.499413 -0.827996     -2.490847   0.018145       0.568648   \n",
       "2  0.196879    0.021231  1.109334     -0.268738   0.088358       0.808997   \n",
       "3  1.691550   -0.346811  0.487926     -0.809251   0.930918       2.491446   \n",
       "4  0.295700    0.227694  1.840403      0.451946   1.281985       0.808997   \n",
       "\n",
       "   Flavanoids  Nonflavanoid_Phenols  Proanthocyanins  Color_Intensity  \\\n",
       "0    1.034819             -0.659563         1.224884         0.251717   \n",
       "1    0.733629             -0.820719        -0.544721        -0.293321   \n",
       "2    1.215533             -0.498407         2.135968         0.269020   \n",
       "3    1.466525             -0.981875         1.032155         1.186068   \n",
       "4    0.663351              0.226796         0.401404        -0.319276   \n",
       "\n",
       "        Hue     OD280   Proline  cluster  \n",
       "0  0.362177  1.847920  1.013009        2  \n",
       "1  0.406051  1.113449  0.965242        2  \n",
       "2  0.318304  0.788587  1.395148        2  \n",
       "3 -0.427544  1.184071  2.334574        2  \n",
       "4  0.362177  0.449601 -0.037874        2  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters_agg=pd.concat([dataset, pd.DataFrame({'cluster':hc.labels_})], axis=1)\n",
    "clusters_agg.head()"
   ]
  }
 ],
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