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>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to Lab of Hierarchical Clustering with Python using Scipy and Scikit-learn package.\n"
   ]
  },
  {
   "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>\n"
   ]
  },
  {
   "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>\n"
   ]
  },
  {
   "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>.\n"
   ]
  },
  {
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2fc8835898>"
      ]
     },
     "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b> Agglomerative Clustering </b> class will require two inputs:\n",
    "\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>\n"
   ]
  },
  {
   "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.\n"
   ]
  },
  {
   "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.\n"
   ]
  },
  {
   "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": [
    "<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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.54759853 0.61436915 ... 0.68567049 0.12229454 0.33247052]\n",
      " [0.54759853 0.         0.08436573 ... 0.38746993 0.4782266  0.42297   ]\n",
      " [0.61436915 0.08436573 0.         ... 0.44398441 0.55352406 0.50724267]\n",
      " ...\n",
      " [0.68567049 0.38746993 0.44398441 ... 0.         0.56884659 0.37783682]\n",
      " [0.12229454 0.4782266  0.55352406 ... 0.56884659 0.         0.2101845 ]\n",
      " [0.33247052 0.42297    0.50724267 ... 0.37783682 0.2101845  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",
    "\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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
    "\n",
    "<ul> <li> Z </li> </ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9oZIOlfRzSRvc/TEzM4UrqQ+W9AtJy939hXbKFZtKTzAm3yPyTjW9USR9X9KOrEkkrZd0i6QXJL1S0gfdfZOZ3SjpBIXGTYfGVe5+l5lNa4SUmS3US425PHnt+xQ2ju9J+qykqZJWu/tVZra7pI9I6lNo6P9U2MvImuY/JP0yZ70ecveBnPk9LWmBpO0KQbcjKesZCkMfM9crp16/IGmuwjDKaZLeKGmlpM2S3qsQ4rvMT9LPCuroE5JWNL5GoGl550jaXdK9kuZL2pasxyPu/sWc9VXBsiYn5ftZ8voPSvqNpG9Lejavntz9yuZ6cPe/MbM/KSj7v0j6LUk/TNpgqqQTFba/1creNj9aML+8bfomhWGsWdvLo3nr6+7bmpeRLOc0SSvarYuceX1Bod0ab25Lfr9e0lqFNslc3zxJ+b4u6WGF7esKST9w96daTPPOZPnPSpqhUDdbJe0t6WvufnfO9nSpu59iZp9SyIGrJB0t6Rh3P8DMzlWo0xslHSapX9JZyq6/90maroy2cvdfFJT/XQq5kLXdTsnZ3hvv7bxtvfT7fpfyVBzWF0q6T9IN2vmN8gZJv6ewkZt23oje6u6TkunnSfo3hcr8B4Ugv18hNI6X9ApJwworfZK7H29mf68QYFcqNOb+CpfIX5A8/hFJn1NoqO+7+55m9i1Jd+ilhv4jhb2MrGk+r9AryFqvT7v7zJz5DUt6SOFD6ARJH3L3dcmHUNF6fVThw2GHdn6T7ebu05J6WuvuhyZ/r1LYSLLmd7a7vyanjo5NXj9T0jWSvufug8k8b3D3E1Lter27v93Mht19es76Tilojz0l/URh6Od8hYDcKunkZJ2z6mmbpIGMelibzDOv7De7+zFqYmaPS/rnjDZ8g6S3FMwvb5v+U0mfUfb2sqVgff+iuWzJ+l2gEITt1sVsZW8vTyl0gi5w99XJulzt7u8ws/UF63tQQflecPf5Znagwvv5NIX2u1XSxTnT7HD3Y5N5D7r73OTvVZI82WaKtqebJM1PBe8Wd59mZj909xNfXFiY3yM59fekQudkmprayt2PK1jnOxXyKKsdX5mzvX9A0jdzpnlaOe/7Ut9k6u6V/Ui6Je9xSWsk7ZHx3BZJk1P/76nwKfq4pFVNr70h+X29pBuTv29qes1NCj3oxv8PpP4eThrhWiUfVCWmWVWwXltKzm9fSdcp7Lbd2GK9fl/SJUo+QJLHr1bodf21pC8mr/u0pMUKb7a8+Q0X1NGq5O+XJcu8SCEQ/lHSdxRC5R2SzpF0bon6K2qPVan/70qXs6CeBpN6WJyuh0abFJT9Kwo9szMlnZT8/prCXkHetlk0v9y2L9heitZ3q6TzJS1r+tlQoi522iaS35nbS/J7ssKH/6WSTm9Rf2uS9S0q36qMeniVQs8xb5rbUq89LfX36qQ+8ranjQq90Q2Sdk89t17SN5L5XyTpjyV9VdI/FdTfU3ltlfzOW+dtBe2Yt71vKZgm932ftY01/1Tds/6sQo9tdVIBe0g6RuENcaHC8dbnmqY5StLP3f2J1GMTJC1S6PFMUujZHCdp0N2/lHyKHq6wER8s6TXuvsXMdlM4VPCAwie+S5qiEKrDyTyeUOiFzFToERyssHH050xziKQfN63X1OT/xxQ+XJrnd7NCz2Ohu29N1mmywuX475L0rdR6HStpbWO93H1+8voFCp/Styu80U5TONzxc4VdrD9U2NAvUQjwrPk9r9BjOSSjjrY2lpWq94kKn/g3JOU8SOE7Xv7b3XeY2VkK4Ze1vp9Llp+1rG3ufnSyjCM82e0zs9UKu5mZ9eTuezTXg7ufmK6n5rK7+3VmdrjCrua0pB3vUOgNZ7XhzZIWFNTFPIXtpnm6fSXdreztZd+C9Z0s6Qx339S0vMsk7dNuXaSmL3puoqSzJc1x978sqj+Fdswr3/nufq2amNntBdN8QdKQu29PPT5ZYVt+RtKHlf1+/H5qVo+6+/Nm9nJJb1PomZ6s8EHxtKTb3f1eM7s5p/7eqxD8u7SVuy8qKP8mT76rI6MdD1N2/mx195fnTHOfcvKsuT2yVH5RjJntpfC1qnsoVEq/u39xBPN7q8LuxJXufl/y2Fvc/faM175M4djRXQqV+YjCcd2TFIJts0IFH6WXGvouSQcq9KTeptCgtypsDKZQ+beZWb+k1yns3mxXOCxxUVK+VyeP71Co04vNbLbCMfj0iZVtkn7l4QRhv0JDP6hw2CbzpIuZzU/W6UdecGwrmd9Bkta5+73JY0fqpZO3D6bqaF5Slglq88SPmU1J6m9vhfD6XXf/u1QZfpma33MKH2gbkjp+OPXcdoXe02aF3siWpuUck7x2u7sPperhx0nZX1CbJ6WTtpqrsF022v4ghQ+TezNef4SH8yONbXpaMm1jm1Gyfo8rfDhvUThWvodCb25Hal6TFDoYa9LBlTz3MXc/L9lm9lPo1KTX682Snldoq2GFD+993f3fM8o8X9Kh7v7VgnqY13gvZZRhosKhodLbRTLNHHUwSMDCycO3KRy2abTJ77SaLmdesxXq/enUYx9P5vmcwna4WeG9/VpJ5yWdkImecXLSzE5Q6EF76rFPSLrDM05YJ8/do7AdPN+oi6Tt3+zutyTvkQMV3qf3WRsnfavuWd+iXU9qHCLpfs84jlhifl9WCIXt2vXk44lZkygcFji5zeeuVfi0y1tW4yTEc9r5JMmZkm7LeHxvhSDMO7EyIWd+eyscU8ssu7u/PeM5JZ/oWdOsV+g55q1TZvncfUnG/ArbV6G3r2R+e7dRF1nrq6R8eSee71b4sN3lOXc/PqfsVW5LUgiza9RmGyr07JrrsHH8+a6c9Xo0mTar/g5Oza8xz8L3XE47NsrwM7W/XXw5p9y57ZGaLrNNiqYrmF/e9jlRoTeu1HMv1lHB+6fRuUvPr1FPza9tPPeMwl5Y1nY7KSmfpaZ9vcKecOt8LHOspOyPpD9TOKlwXOqxq0cwv5tSf89LKuEIheO+zyS/VyW/G38/2fTcqtTfnnpt8zRFy0o/N5j6e0vO46sKpmn1XF7Znyyop8Y0zev1fAfrtKqT9q1ofTsp+07P1bgtFdVt6TZsow7TZXyqYFltv+c6bcdO6rbT93eHeZG5Xq3qqKD9/6+gnvKWVbSdjSgfOwrRFhWWeVKjw3ndpvyTj3knLK8veG5rwTRFy8o7SfJ0zuOrC6Zp9VzuehXUUycnb3PL0En71rC+hWXPe24Ut6UtnbZhUR3mlPG5orbKm1/V7dhJ3bYoQ0fTdbheuXXUov2LptvluVbr1ElbvTivTiulRKVNVDiTf84I5nGkwu5X+rEJCuNb90lXStNy857bv2CaomW9XtKEjI3iYzmPn14wTavncteroJ7ypjmqg3U6vZP2rWF9i8qe21ajuC3NG2kbZtRhXhk/U6atOnnPtdOOndRti2V3NF0n61X0eJn2Kqrb9HNl16mTtuJb9wAgAnw3CABEgLAGgAgQ1gAQAcIaACJAWANABP4f9XGV2wgtsWUAAAAASUVORK5CYII=\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",
    "\n",
    "We used **complete** linkage for our case, change it to **average** linkage to see how the dendogram changes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
     ]
    },
    {
     "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": [
    "\n",
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data\n",
    "\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-26 21:20:50--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/cars_clus.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 198.23.119.245\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|198.23.119.245|: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 0.002s  \n",
      "\n",
      "2021-03-26 21:20:51 (9.77 MB/s) - ‘cars_clus.csv’ saved [17774/17774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O cars_clus.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/cars_clus.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "\n",
    "lets read dataset to see what features the manufacturer has collected about the existing models.\n"
   ]
  },
  {
   "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).\n"
   ]
  },
  {
   "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:\n"
   ]
  },
  {
   "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",
       "        vertical-align: middle;\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": 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",
    "\n",
    "Lets select our feature set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureset = pdf[['engine_s',  'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap', 'mpg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "\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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "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. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: scipy.zeros is deprecated and will be removed in SciPy 2.0.0, use numpy.zeros instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "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",
    "\n",
    "```\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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 19,
     "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf['cluster_'] = agglom.labels_\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf.groupby(['cluster_','type'])['cluster_'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the characteristics of each cluster:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_cars = pdf.groupby(['cluster_','type'])['horsepow','engine_s','mpg','price'].mean()\n",
    "agg_cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is obvious that we have 3 main clusters with the majority of vehicles in those.\n",
    "\n",
    "**Cars**:\n",
    "\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",
    "**Trucks**:\n",
    "\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",
    "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.\n"
   ]
  },
  {
   "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"
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    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description    |\n",
    "| ----------------- | ------- | ---------- | --------------------- |\n",
    "| 2020-08-04        | 0       | Nayef      | Upload file to Gitlab |\n",
    "|                   |         |            |                       |\n"
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    "<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>\n"
   ]
  }
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