ML0101EN-Clus-Hierarchical-Cars-py-v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Hierarchical Clustering</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to Lab of Hierarchical Clustering with Python using Scipy and Scikit-learn package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#generating_data\">Generating Random Data</a></li>\n",
    "                <li><a href=\"#agglomerative_clustering\">Agglomerative Clustering</a></li>\n",
    "                <li><a href=\"#dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</a></li>\n",
    "            </ol>            \n",
    "        <li><a href=\"#clustering_vehicle_dataset\">Clustering on the Vehicle Dataset</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#data_cleaning\">Data Cleaning</a></li>\n",
    "                <li><a href=\"#clustering_using_scipy\">Clustering Using Scipy</a></li>\n",
    "                <li><a href=\"#clustering_using_skl\">Clustering using scikit-learn</a></li>\n",
    "            </ol>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</h1>\n",
    "\n",
    "We will be looking at a clustering technique, which is <b>Agglomerative Hierarchical Clustering</b>. Remember that agglomerative is the bottom up approach. <br> <br>\n",
    "In this lab, we will be looking at Agglomerative clustering, which is more popular than Divisive clustering. <br> <br>\n",
    "We will also be using Complete Linkage as the Linkage Criteria. <br>\n",
    "<b> <i> NOTE: You can also try using Average Linkage wherever Complete Linkage would be used to see the difference! </i> </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from scipy import ndimage \n",
    "from scipy.cluster import hierarchy \n",
    "from scipy.spatial import distance_matrix \n",
    "from matplotlib import pyplot as plt \n",
    "from sklearn import manifold, datasets \n",
    "from sklearn.cluster import AgglomerativeClustering \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"generating_data\">Generating Random Data</h3>\n",
    "We will be generating a set of data using the <b>make_blobs</b> class. <br> <br>\n",
    "Input these parameters into make_blobs:\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Choose a number from 10-1500 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Choose arrays of x,y coordinates for generating the centers. Have 1-10 centers (ex. centers=[[1,1], [2,5]]) </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. The larger the number, the further apart the clusters</li>\n",
    "    <ul> <li> Choose a number between 0.5-1.5 </li> </ul>\n",
    "</ul> <br>\n",
    "Save the result to <b>X1</b> and <b>y1</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1, y1 = make_blobs(n_samples=50, centers=[[4,4], [-2, -1], [1, 1], [10,4]], cluster_std=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plot of the randomly generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd4fdf9b588>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"agglomerative_clustering\">Agglomerative Clustering</h3>\n",
    "We will start by clustering the random data points we just created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b> Agglomerative Clustering </b> class will require two inputs:\n",
    "<ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 </li> </ul>\n",
    "    <li> <b>linkage</b>: Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. </li>\n",
    "    <ul> \n",
    "        <li> Value will be: 'complete' </li> \n",
    "        <li> <b>Note</b>: It is recommended you try everything with 'average' as well </li>\n",
    "    </ul>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> agglom </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 4, linkage = 'average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model with <b> X2 </b> and <b> y2 </b> from the generated data above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='average', memory=None,\n",
       "            n_clusters=4, pooling_func='deprecated')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglom.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to show the clustering! <br>\n",
    "Remember to read the code and comments to gain more understanding on how the plotting works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# These two lines of code are used to scale the data points down,\n",
    "# Or else the data points will be scattered very far apart.\n",
    "\n",
    "# Create a minimum and maximum range of X1.\n",
    "x_min, x_max = np.min(X1, axis=0), np.max(X1, axis=0)\n",
    "\n",
    "# Get the average distance for X1.\n",
    "X1 = (X1 - x_min) / (x_max - x_min)\n",
    "\n",
    "# This loop displays all of the datapoints.\n",
    "for i in range(X1.shape[0]):\n",
    "    # Replace the data points with their respective cluster value \n",
    "    # (ex. 0) and is color coded with a colormap (plt.cm.spectral)\n",
    "    plt.text(X1[i, 0], X1[i, 1], str(y1[i]),\n",
    "             color=plt.cm.nipy_spectral(agglom.labels_[i] / 10.),\n",
    "             fontdict={'weight': 'bold', 'size': 9})\n",
    "    \n",
    "# Remove the x ticks, y ticks, x and y axis\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "#plt.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "# Display the plot of the original data before clustering\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='.')\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3 id=\"dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</h3>\n",
    "Remember that a <b>distance matrix</b> contains the <b> distance from each point to every other point of a dataset </b>. <br>\n",
    "Use the function <b> distance_matrix, </b> which requires <b>two inputs</b>. Use the Feature Matrix, <b> X2 </b> as both inputs and save the distance matrix to a variable called <b> dist_matrix </b> <br> <br>\n",
    "Remember that the distance values are symmetric, with a diagonal of 0's. This is one way of making sure your matrix is correct. <br> (print out dist_matrix to make sure it's correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.52808101 0.06034083 ... 0.92658596 0.38740913 0.15255873]\n",
      " [0.52808101 0.         0.49798159 ... 0.44741501 0.58445861 0.38052185]\n",
      " [0.06034083 0.49798159 0.         ... 0.91171602 0.33155231 0.11765557]\n",
      " ...\n",
      " [0.92658596 0.44741501 0.91171602 ... 0.         1.03131982 0.79682936]\n",
      " [0.38740913 0.58445861 0.33155231 ... 1.03131982 0.         0.35191895]\n",
      " [0.15255873 0.38052185 0.11765557 ... 0.79682936 0.35191895 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(X1,X1) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <b> linkage </b> class from hierarchy, pass in the parameters:\n",
    "<ul>\n",
    "    <li> The distance matrix </li>\n",
    "    <li> 'complete' for complete linkage </li>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> Z </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:1: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Z = hierarchy.linkage(dist_matrix, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Hierarchical clustering is typically visualized as a dendrogram as shown in the following cell. Each merge is represented by a horizontal line. The y-coordinate of the horizontal line is the similarity of the two clusters that were merged, where cities are viewed as singleton clusters. \n",
    "By moving up from the bottom layer to the top node, a dendrogram allows us to reconstruct the history of merges that resulted in the depicted clustering. \n",
    "\n",
    "Next, we will save the dendrogram to a variable called <b>dendro</b>. In doing this, the dendrogram will also be displayed.\n",
    "Using the <b> dendrogram </b> class from hierarchy, pass in the parameter:\n",
    "<ul> <li> Z </li> </ul>"
   ]
  },
  {
   "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": [
    "dendro = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "We used __complete__ linkage for our case, change it to __average__ linkage to see how the dendogram changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:2: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \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": [
    "# write your code here\n",
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "    \n",
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h1 id=\"clustering_vehicle_dataset\">Clustering on Vehicle dataset</h1>\n",
    "\n",
    "Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against.\n",
    "\n",
    "Our objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data\n",
    "To download the data, we will use **`!wget`** to download it from IBM Object Storage.  \n",
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-02-01 07:50:49--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 17774 (17K) [text/csv]\n",
      "Saving to: ‘cars_clus.csv’\n",
      "\n",
      "cars_clus.csv       100%[===================>]  17.36K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2020-02-01 07:50:49 (879 KB/s) - ‘cars_clus.csv’ saved [17774/17774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O cars_clus.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "lets read dataset to see what features the manufacturer has collected about the existing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset:  (159, 16)\n"
     ]
    },
    {
     "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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21.500</td>\n",
       "      <td>1.800</td>\n",
       "      <td>140.000</td>\n",
       "      <td>101.200</td>\n",
       "      <td>67.300</td>\n",
       "      <td>172.400</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.200</td>\n",
       "      <td>28.000</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>108.100</td>\n",
       "      <td>70.300</td>\n",
       "      <td>192.900</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.200</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>CL</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>$null$</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>106.900</td>\n",
       "      <td>70.600</td>\n",
       "      <td>192.000</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.200</td>\n",
       "      <td>26.000</td>\n",
       "      <td>2.647</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>210.000</td>\n",
       "      <td>114.600</td>\n",
       "      <td>71.400</td>\n",
       "      <td>196.600</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.990</td>\n",
       "      <td>1.800</td>\n",
       "      <td>150.000</td>\n",
       "      <td>102.600</td>\n",
       "      <td>68.200</td>\n",
       "      <td>178.000</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.400</td>\n",
       "      <td>27.000</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale   type   price engine_s horsepow wheelbas  \\\n",
       "0    Acura  Integra  16.919  16.360  0.000  21.500    1.800  140.000  101.200   \n",
       "1    Acura       TL  39.384  19.875  0.000  28.400    3.200  225.000  108.100   \n",
       "2    Acura       CL  14.114  18.225  0.000  $null$    3.200  225.000  106.900   \n",
       "3    Acura       RL   8.588  29.725  0.000  42.000    3.500  210.000  114.600   \n",
       "4     Audi       A4  20.397  22.255  0.000  23.990    1.800  150.000  102.600   \n",
       "\n",
       "    width   length curb_wgt fuel_cap     mpg lnsales  partition  \n",
       "0  67.300  172.400    2.639   13.200  28.000   2.828        0.0  \n",
       "1  70.300  192.900    3.517   17.200  25.000   3.673        0.0  \n",
       "2  70.600  192.000    3.470   17.200  26.000   2.647        0.0  \n",
       "3  71.400  196.600    3.850   18.000  22.000   2.150        0.0  \n",
       "4  68.200  178.000    2.998   16.400  27.000   3.015        0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'cars_clus.csv'\n",
    "\n",
    "#Read csv\n",
    "pdf = pd.read_csv(filename)\n",
    "print (\"Shape of dataset: \", pdf.shape)\n",
    "\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature sets include  price in thousands (price), engine size (engine_s), horsepower (horsepow), wheelbase (wheelbas), width (width), length (length), curb weight (curb_wgt), fuel capacity (fuel_cap) and fuel efficiency (mpg)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_cleaning\">Data Cleaning</h2>\n",
    "lets simply clear the dataset by dropping the rows that have null value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset before cleaning:  2544\n",
      "Shape of dataset after cleaning:  1872\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n",
       "1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n",
       "2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n",
       "3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n",
       "4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0  \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0  \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0  \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0  \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Shape of dataset before cleaning: \", pdf.size)\n",
    "pdf[[ 'sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']] = pdf[['sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']].apply(pd.to_numeric, errors='coerce')\n",
    "pdf = pdf.dropna()\n",
    "pdf = pdf.reset_index(drop=True)\n",
    "print (\"Shape of dataset after cleaning: \", pdf.size)\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection\n",
    "Lets select our feature set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureset = pdf[['engine_s',  'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap', 'mpg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "Now we can normalize the feature set. __MinMaxScaler__ transforms features by scaling each feature to a given range. It is by default (0, 1). That is, this estimator scales and translates each feature individually such that it is between zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11428571, 0.21518987, 0.18655098, 0.28143713, 0.30625832,\n",
       "        0.2310559 , 0.13364055, 0.43333333],\n",
       "       [0.31428571, 0.43037975, 0.3362256 , 0.46107784, 0.5792277 ,\n",
       "        0.50372671, 0.31797235, 0.33333333],\n",
       "       [0.35714286, 0.39240506, 0.47722343, 0.52694611, 0.62849534,\n",
       "        0.60714286, 0.35483871, 0.23333333],\n",
       "       [0.11428571, 0.24050633, 0.21691974, 0.33532934, 0.38082557,\n",
       "        0.34254658, 0.28110599, 0.4       ],\n",
       "       [0.25714286, 0.36708861, 0.34924078, 0.80838323, 0.56724368,\n",
       "        0.5173913 , 0.37788018, 0.23333333]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "x = featureset.values #returns a numpy array\n",
    "min_max_scaler = MinMaxScaler()\n",
    "feature_mtx = min_max_scaler.fit_transform(x)\n",
    "feature_mtx [0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"clustering_using_scipy\">Clustering using Scipy</h2>\n",
    "In this part we use Scipy package to cluster the dataset:  \n",
    "First, we calculate the distance matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "leng = feature_mtx.shape[0]\n",
    "D = scipy.zeros([leng,leng])\n",
    "for i in range(leng):\n",
    "    for j in range(leng):\n",
    "        D[i,j] = scipy.spatial.distance.euclidean(feature_mtx[i], feature_mtx[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In agglomerative clustering, at each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster with the remaining clusters in the forest. \n",
    "The following methods are supported in Scipy for calculating the distance between the newly formed cluster and each:\n",
    "    - single\n",
    "    - complete\n",
    "    - average\n",
    "    - weighted\n",
    "    - centroid\n",
    "    \n",
    "    \n",
    "We use __complete__ for our case, but feel free to change it to see how the results change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:3: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "import pylab\n",
    "import scipy.cluster.hierarchy\n",
    "Z = hierarchy.linkage(D, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essentially, Hierarchical clustering does not require a pre-specified number of clusters. However, in some applications we want a partition of disjoint clusters just as in flat clustering.\n",
    "So you can use a cutting line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  5,  6,  5,  4,  6,  5,  5,  5,  5,  5,  4,  4,  5,  1,  6,\n",
       "        5,  5,  5,  4,  2, 11,  6,  6,  5,  6,  5,  1,  6,  6, 10,  9,  8,\n",
       "        9,  3,  5,  1,  7,  6,  5,  3,  5,  3,  8,  7,  9,  2,  6,  6,  5,\n",
       "        4,  2,  1,  6,  5,  2,  7,  5,  5,  5,  4,  4,  3,  2,  6,  6,  5,\n",
       "        7,  4,  7,  6,  6,  5,  3,  5,  5,  6,  5,  4,  4,  1,  6,  5,  5,\n",
       "        5,  6,  4,  5,  4,  1,  6,  5,  6,  6,  5,  5,  5,  7,  7,  7,  2,\n",
       "        2,  1,  2,  6,  5,  1,  1,  1,  7,  8,  1,  1,  6,  1,  1],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "max_d = 3\n",
    "clusters = fcluster(Z, max_d, criterion='distance')\n",
    "clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, you can determine the number of clusters directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 2, 1,\n",
       "       5, 3, 3, 3, 3, 3, 1, 3, 3, 4, 4, 4, 4, 2, 3, 1, 3, 3, 3, 2, 3, 2,\n",
       "       4, 3, 4, 1, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3, 3, 3, 3, 2, 2, 2, 1, 3,\n",
       "       3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 3, 2,\n",
       "       3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1,\n",
       "       3, 4, 1, 1, 3, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "k = 5\n",
    "clusters = fcluster(Z, k, criterion='maxclust')\n",
    "clusters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, plot the dendrogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pylab.figure(figsize=(18,50))\n",
    "def llf(id):\n",
    "    return '[%s %s %s]' % (pdf['manufact'][id], pdf['model'][id], int(float(pdf['type'][id])) )\n",
    "    \n",
    "dendro = hierarchy.dendrogram(Z,  leaf_label_func=llf, leaf_rotation=0, leaf_font_size =12, orientation = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"clustering_using_skl\">Clustering using scikit-learn</h2>\n",
    "Lets redo it again, but this time using scikit-learn package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.57777143 0.75455727 ... 0.28530295 0.24917241 0.18879995]\n",
      " [0.57777143 0.         0.22798938 ... 0.36087756 0.66346677 0.62201282]\n",
      " [0.75455727 0.22798938 0.         ... 0.51727787 0.81786095 0.77930119]\n",
      " ...\n",
      " [0.28530295 0.36087756 0.51727787 ... 0.         0.41797928 0.35720492]\n",
      " [0.24917241 0.66346677 0.81786095 ... 0.41797928 0.         0.15212198]\n",
      " [0.18879995 0.62201282 0.77930119 ... 0.35720492 0.15212198 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(feature_mtx,feature_mtx) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use the 'AgglomerativeClustering' function from scikit-learn library to cluster the dataset. The AgglomerativeClustering performs a hierarchical clustering using a bottom up approach. The linkage criteria determines the metric used for the merge strategy:\n",
    "\n",
    "- Ward minimizes the sum of squared differences within all clusters. It is a variance-minimizing approach and in this sense is similar to the k-means objective function but tackled with an agglomerative hierarchical approach.\n",
    "- Maximum or complete linkage minimizes the maximum distance between observations of pairs of clusters.\n",
    "- Average linkage minimizes the average of the distances between all observations of pairs of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 1, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3, 2, 1, 1, 2, 2, 2, 5, 1,\n",
       "       4, 1, 1, 2, 1, 2, 1, 1, 1, 5, 0, 0, 0, 3, 2, 1, 2, 1, 2, 3, 2, 3,\n",
       "       0, 3, 0, 1, 1, 1, 2, 3, 1, 1, 1, 2, 1, 1, 2, 2, 2, 3, 3, 3, 1, 1,\n",
       "       1, 2, 1, 2, 2, 1, 1, 2, 3, 2, 3, 1, 2, 3, 5, 1, 1, 2, 3, 2, 1, 3,\n",
       "       2, 3, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       2, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 6, linkage = 'complete')\n",
    "agglom.fit(feature_mtx)\n",
    "agglom.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, we can add a new field to our dataframe to show the cluster of each row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "      <th>cluster_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n",
       "1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n",
       "2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n",
       "3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n",
       "4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \\\n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0   \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0   \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0   \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0   \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0   \n",
       "\n",
       "   cluster_  \n",
       "0         1  \n",
       "1         2  \n",
       "2         2  \n",
       "3         1  \n",
       "4         2  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf['cluster_'] = agglom.labels_\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mpg')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "n_clusters = max(agglom.labels_)+1\n",
    "colors = cm.rainbow(np.linspace(0, 1, n_clusters))\n",
    "cluster_labels = list(range(0, n_clusters))\n",
    "\n",
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(16,14))\n",
    "\n",
    "for color, label in zip(colors, cluster_labels):\n",
    "    subset = pdf[pdf.cluster_ == label]\n",
    "    for i in subset.index:\n",
    "            plt.text(subset.horsepow[i], subset.mpg[i],str(subset['model'][i]), rotation=25) \n",
    "    plt.scatter(subset.horsepow, subset.mpg, s= subset.price*10, c=color, label='cluster'+str(label),alpha=0.5)\n",
    "#    plt.scatter(subset.horsepow, subset.mpg)\n",
    "plt.legend()\n",
    "plt.title('Clusters')\n",
    "plt.xlabel('horsepow')\n",
    "plt.ylabel('mpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we are seeing the distribution of each cluster using the scatter plot, but it is not very clear where is the centroid of each cluster. Moreover, there are 2 types of vehicles in our dataset, \"truck\" (value of 1 in the type column) and \"car\" (value of 1 in the type column). So, we use them to distinguish the classes, and summarize the cluster. First we count the number of cases in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_  type\n",
       "0         1.0      6\n",
       "1         0.0     47\n",
       "          1.0      5\n",
       "2         0.0     27\n",
       "          1.0     11\n",
       "3         0.0     10\n",
       "          1.0      7\n",
       "4         0.0      1\n",
       "5         0.0      3\n",
       "Name: cluster_, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.groupby(['cluster_','type'])['cluster_'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the characteristics of each cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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></th>\n",
       "      <th>horsepow</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>1.0</th>\n",
       "      <td>211.666667</td>\n",
       "      <td>4.483333</td>\n",
       "      <td>16.166667</td>\n",
       "      <td>29.024667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0.0</th>\n",
       "      <td>146.531915</td>\n",
       "      <td>2.246809</td>\n",
       "      <td>27.021277</td>\n",
       "      <td>20.306128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>145.000000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>17.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>0.0</th>\n",
       "      <td>203.111111</td>\n",
       "      <td>3.303704</td>\n",
       "      <td>24.214815</td>\n",
       "      <td>27.750593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>182.090909</td>\n",
       "      <td>3.345455</td>\n",
       "      <td>20.181818</td>\n",
       "      <td>26.265364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>0.0</th>\n",
       "      <td>256.500000</td>\n",
       "      <td>4.410000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>42.870400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>160.571429</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>21.527714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>0.0</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0.0</th>\n",
       "      <td>365.666667</td>\n",
       "      <td>6.233333</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>66.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 horsepow  engine_s        mpg      price\n",
       "cluster_ type                                            \n",
       "0        1.0   211.666667  4.483333  16.166667  29.024667\n",
       "1        0.0   146.531915  2.246809  27.021277  20.306128\n",
       "         1.0   145.000000  2.580000  22.200000  17.009200\n",
       "2        0.0   203.111111  3.303704  24.214815  27.750593\n",
       "         1.0   182.090909  3.345455  20.181818  26.265364\n",
       "3        0.0   256.500000  4.410000  21.500000  42.870400\n",
       "         1.0   160.571429  3.071429  21.428571  21.527714\n",
       "4        0.0    55.000000  1.000000  45.000000   9.235000\n",
       "5        0.0   365.666667  6.233333  19.333333  66.010000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_cars = pdf.groupby(['cluster_','type'])['horsepow','engine_s','mpg','price'].mean()\n",
    "agg_cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It is obvious that we have 3 main clusters with the majority of vehicles in those.\n",
    "\n",
    "__Cars__:\n",
    "- Cluster 1: with almost high mpg, and low in horsepower.\n",
    "- Cluster 2: with good mpg and horsepower, but higher price than average.\n",
    "- Cluster 3: with low mpg, high horsepower, highest price.\n",
    "    \n",
    "    \n",
    "    \n",
    "__Trucks__:\n",
    "- Cluster 1: with almost highest mpg among trucks, and lowest in horsepower and price.\n",
    "- Cluster 2: with almost low mpg and medium horsepower, but higher price than average.\n",
    "- Cluster 3: with good mpg and horsepower, low price.\n",
    "\n",
    "\n",
    "Please notice that we did not use __type__ , and __price__ of cars in the clustering process, but Hierarchical clustering could forge the clusters and discriminate them with quite high accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mpg')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "for color, label in zip(colors, cluster_labels):\n",
    "    subset = agg_cars.loc[(label,),]\n",
    "    for i in subset.index:\n",
    "        plt.text(subset.loc[i][0]+5, subset.loc[i][2], 'type='+str(int(i)) + ', price='+str(int(subset.loc[i][3]))+'k')\n",
    "    plt.scatter(subset.horsepow, subset.mpg, s=subset.price*20, c=color, label='cluster'+str(label))\n",
    "plt.legend()\n",
    "plt.title('Clusters')\n",
    "plt.xlabel('horsepow')\n",
    "plt.ylabel('mpg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
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