Hierarchical Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering\n",
    "- There are two types of Hierarchical Clustering;\n",
    "    \n",
    "    `1. Agglomerative`\n",
    "        - It is the bottom up approach.\n",
    "    \n",
    "    `2. Divisive`\n",
    "    \n",
    "    \n",
    "- **Steps for Agglomerative HC;**\n",
    "        Step 1 : Make each data point a single point cluster -> That forms N clusters.\n",
    "        Step 2 : Take the two closest data points and make them one cluster -> That forms (N - 1) clusters.\n",
    "        Step 3 : Take the two closest clusters and make them one cluster -> That forms (N - 2) clusters.\n",
    "        Step 4 : Repeat Step 3 until there is only one cluster. Than FIN.\n",
    "\n",
    "    - **Option to choose Distance between clusters**\n",
    "        1. Closest Point\n",
    "        2. Furthest Point\n",
    "        3. Average Distance\n",
    "        4. Distance between Centroids. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numpy allows us to work with array.\n",
    "import numpy as np\n",
    "\n",
    "# Maptplotlib which allows us to plot some chart.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pandas allows us to not only import the datasets but also create the matrix of features(independent) and \n",
    "# dependent variable.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The independent variable usally in the first columns of dataset and dependent variable usally in the last columns of the data sets.\n",
    "- X is Independent Variable.\n",
    "- Y is Dependent Variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('Mall_Customers.csv')\n",
    "x = dataset.iloc[:, [3,4]].values\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using the Dendrogram to find the optimal number of clusters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing Package\n",
    "import scipy.cluster.hierarchy as sch\n",
    "\n",
    "# Dendrogram\n",
    "dendrogram = sch.dendrogram(sch.linkage(x, method = 'ward'))\n",
    "\n",
    "plt.title(\"Dendrogram\")\n",
    "plt.xlabel(\"Customers\")\n",
    "plt.ylabel(\"Euclidean Distance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training the Hierarchical Clustering on the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4\n",
      " 3 4 3 4 3 4 1 4 1 1 1 1 1 1 1 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 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 2 1 2 1 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 1 2 0 2 1 2\n",
      " 0 2 0 2 0 2 0 2 0 2 0 2 1 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0\n",
      " 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2]\n"
     ]
    }
   ],
   "source": [
    "# Importing Package\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\n",
    "y_hc = hc.fit_predict(x)\n",
    "# This method not only trains your HC on the data set, but also it return exactly that dependent variable which we are \n",
    "# about to create the five different values taken by that variable.\n",
    "\n",
    "print(y_hc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualising the Clusters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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[y_hc == 0, 0], x[y_hc == 0, 1], s = 100, c = 'red', label = \"Cluster 1\")\n",
    "plt.scatter(x[y_hc == 1, 0], bx[y_hc == 1, 1], s = 100, c = 'blue', label = \"Cluster 2\")\n",
    "plt.scatter(x[y_hc == 2, 0], x[y_hc == 2, 1], s = 100, c = 'green', label = \"Cluster 3\")\n",
    "plt.scatter(x[y_hc == 3, 0], x[y_hc == 3, 1], s = 100, c = 'orange', label = \"Cluster 4\")\n",
    "plt.scatter(x[y_hc == 4, 0], x[y_hc == 4, 1], s = 100, c = 'cyan', label = \"Cluster 5\")\n",
    "\n",
    "plt.title(\"Clusters of Customer\")\n",
    "plt.xlabel(\"Annual Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The cluster 1 contains all the customers that have a high annual income and yet have a low spending score in the mall.\n",
    "- The cluster 2 contains all the customers that have an average annual income and spends normally in the mall.\n",
    "- The cluster 3 contains all the customers that have a high annual income and also who spend a lot in the mall.\n",
    "- The cluster 4 contains all the customers that have a low annual income and who spends high in the mall.\n",
    "- The cluster 5 contains all the customers that have a high annual income and who spends very low in the mall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Trying with 3 clusters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 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 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 2 0 2 0 2 1 2 1 2 0 2 1 2 1 2 1 2 1 2 0 2 1 2 0 2\n",
      " 1 2 1 2 1 2 1 2 1 2 1 2 0 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1\n",
      " 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2]\n"
     ]
    },
    {
     "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": [
    "# Importing Package\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "hc1 = AgglomerativeClustering(n_clusters = 3, affinity = 'euclidean', linkage = 'ward')\n",
    "y_hc1 = hc1.fit_predict(x)\n",
    "# This method not only trains your HC on the data set, but also it return exactly that dependent variable which we are \n",
    "# about to create the five different values taken by that variable.\n",
    "\n",
    "print(y_hc1)\n",
    "\n",
    "plt.scatter(x[y_hc1 == 0, 0], x[y_hc1 == 0, 1], s = 100, c = 'red', label = \"Cluster 1\")\n",
    "plt.scatter(x[y_hc1 == 1, 0], x[y_hc1 == 1, 1], s = 100, c = 'blue', label = \"Cluster 2\")\n",
    "plt.scatter(x[y_hc1 == 2, 0], x[y_hc1 == 2, 1], s = 100, c = 'green', label = \"Cluster 3\")\n",
    "\n",
    "plt.title(\"Clusters of Customer\")\n",
    "plt.xlabel(\"Annual Income\")\n",
    "plt.ylabel(\"Spending Score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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
}