shas043/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": {
    "collapsed": true
   },
   "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": {
    "collapsed": true
   },
   "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 0x7f4138ef23c8>"
      ]
     },
     "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": {
    "collapsed": true
   },
   "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.         0.63022077 0.79358807 ... 0.81601509 0.62460404 0.82046569]\n",
      " [0.63022077 0.         0.16602445 ... 0.20685649 0.09056098 0.36080782]\n",
      " [0.79358807 0.16602445 0.         ... 0.072011   0.18369493 0.3778868 ]\n",
      " ...\n",
      " [0.81601509 0.20685649 0.072011   ... 0.         0.19180873 0.44954946]\n",
      " [0.62460404 0.09056098 0.18369493 ... 0.19180873 0.         0.45003144]\n",
      " [0.82046569 0.36080782 0.3778868  ... 0.44954946 0.45003144 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(X1,X1) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <b> linkage </b> class from hierarchy, pass in the parameters:\n",
    "<ul>\n",
    "    <li> The distance matrix </li>\n",
    "    <li> 'complete' for complete linkage </li>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> Z </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Z = hierarchy.linkage(dist_matrix, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Hierarchical clustering is typically visualized as a dendrogram as shown in the following cell. Each merge is represented by a horizontal line. The y-coordinate of the horizontal line is the similarity of the two clusters that were merged, where cities are viewed as singleton clusters. \n",
    "By moving up from the bottom layer to the top node, a dendrogram allows us to reconstruct the history of merges that resulted in the depicted clustering. \n",
    "\n",
    "Next, we will save the dendrogram to a variable called <b>dendro</b>. In doing this, the dendrogram will also be displayed.\n",
    "Using the <b> dendrogram </b> class from hierarchy, pass in the parameter:\n",
    "<ul> <li> Z </li> </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dendro = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "We used __complete__ linkage for our case, change it to __average__ linkage to see how the dendogram changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/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')# write your code here\n",
    "\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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-18 15:52:45--  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",
      "2019-06-18 15:52:45 (824 KB/s) - ‘cars_clus.csv’ saved [17774/17774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O cars_clus.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "lets read dataset to see what features the manufacturer has collected about the existing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset:  (159, 16)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21.500</td>\n",
       "      <td>1.800</td>\n",
       "      <td>140.000</td>\n",
       "      <td>101.200</td>\n",
       "      <td>67.300</td>\n",
       "      <td>172.400</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.200</td>\n",
       "      <td>28.000</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>108.100</td>\n",
       "      <td>70.300</td>\n",
       "      <td>192.900</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.200</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>CL</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>$null$</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>106.900</td>\n",
       "      <td>70.600</td>\n",
       "      <td>192.000</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.200</td>\n",
       "      <td>26.000</td>\n",
       "      <td>2.647</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>210.000</td>\n",
       "      <td>114.600</td>\n",
       "      <td>71.400</td>\n",
       "      <td>196.600</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.990</td>\n",
       "      <td>1.800</td>\n",
       "      <td>150.000</td>\n",
       "      <td>102.600</td>\n",
       "      <td>68.200</td>\n",
       "      <td>178.000</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.400</td>\n",
       "      <td>27.000</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale   type   price engine_s horsepow wheelbas  \\\n",
       "0    Acura  Integra  16.919  16.360  0.000  21.500    1.800  140.000  101.200   \n",
       "1    Acura       TL  39.384  19.875  0.000  28.400    3.200  225.000  108.100   \n",
       "2    Acura       CL  14.114  18.225  0.000  $null$    3.200  225.000  106.900   \n",
       "3    Acura       RL   8.588  29.725  0.000  42.000    3.500  210.000  114.600   \n",
       "4     Audi       A4  20.397  22.255  0.000  23.990    1.800  150.000  102.600   \n",
       "\n",
       "    width   length curb_wgt fuel_cap     mpg lnsales  partition  \n",
       "0  67.300  172.400    2.639   13.200  28.000   2.828        0.0  \n",
       "1  70.300  192.900    3.517   17.200  25.000   3.673        0.0  \n",
       "2  70.600  192.000    3.470   17.200  26.000   2.647        0.0  \n",
       "3  71.400  196.600    3.850   18.000  22.000   2.150        0.0  \n",
       "4  68.200  178.000    2.998   16.400  27.000   3.015        0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'cars_clus.csv'\n",
    "\n",
    "#Read csv\n",
    "pdf = pd.read_csv(filename)\n",
    "print (\"Shape of dataset: \", pdf.shape)\n",
    "\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature sets include  price in thousands (price), engine size (engine_s), horsepower (horsepow), wheelbase (wheelbas), width (width), length (length), curb weight (curb_wgt), fuel capacity (fuel_cap) and fuel efficiency (mpg)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_cleaning\">Data Cleaning</h2>\n",
    "lets simply clear the dataset by dropping the rows that have null value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset before cleaning:  2544\n",
      "Shape of dataset after cleaning:  1872\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>0.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n",
       "1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n",
       "2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n",
       "3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n",
       "4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0  \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0  \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0  \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0  \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Shape of dataset before cleaning: \", pdf.size)\n",
    "pdf[[ 'sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']] = pdf[['sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']].apply(pd.to_numeric, errors='coerce')\n",
    "pdf = pdf.dropna()\n",
    "pdf = pdf.reset_index(drop=True)\n",
    "print (\"Shape of dataset after cleaning: \", pdf.size)\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection\n",
    "Lets select our feature set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 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:"
   ]
  },
  {
   "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:"
   ]
  },
  {
   "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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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": 24,
     "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": 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:"
   ]
  },
  {
   "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:"
   ]
  },
  {
   "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": [
    "\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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