kdbaseball8/ML0101EN-Clus-Hierarchical-Cars-py-v1.ipynb

## 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": 1,
   "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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f71600b7cf8>"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "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": 5,
   "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": 5,
     "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": 6,
   "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": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.52457729 0.16015357 ... 0.48290335 0.44969598 0.24467174]\n",
      " [0.52457729 0.         0.68469845 ... 0.05797256 0.96638205 0.28766518]\n",
      " [0.16015357 0.68469845 0.         ... 0.64241825 0.29897101 0.40354781]\n",
      " ...\n",
      " [0.48290335 0.05797256 0.64241825 ... 0.         0.92902005 0.25502969]\n",
      " [0.44969598 0.96638205 0.29897101 ... 0.92902005 0.         0.67903883]\n",
      " [0.24467174 0.28766518 0.40354781 ... 0.25502969 0.67903883 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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/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": 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": [
    "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/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"
     ]
    },
    {
     "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": [
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-07-06 22:00:19--  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)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|: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",
      "2019-07-06 22:00:19 (714 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": 12,
   "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",
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       "    }\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": 12,
     "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": 13,
   "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",
       "    .dataframe tbody tr th {\n",
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       "    }\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": 13,
     "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": 14,
   "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": 15,
   "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": 15,
     "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/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": 18,
   "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": 18,
     "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": 19,
   "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": 19,
     "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": 20,
   "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 = 'right')"
   ]
  },
  {
   "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": 21,
   "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": 22,
   "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": 22,
     "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": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf['cluster_'] = agglom.labels_\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": 25,
   "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": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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": 26,
     "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": null,
   "metadata": {},
   "outputs": [],
   "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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