tholsar/ML0101EN-Clus-K-Means-Customer-Seg-py-v1.ipynb

## ML0101EN-Clus-K-Means-Customer-Seg-py-v1.ipynb
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   "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>K-Means Clustering</center></h1>"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered one of the simplest models amongst them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you will learn how to use k-Means for customer segmentation.\n",
    "\n",
    "Some real-world applications of k-means:\n",
    "- Customer segmentation\n",
    "- Understand what the visitors of a website are trying to accomplish\n",
    "- Pattern recognition\n",
    "- Machine learning\n",
    "- Data compression\n",
    "\n",
    "\n",
    "In this notebook we practice k-means clustering with 2 examples:\n",
    "- k-means on a random generated dataset\n",
    "- Using k-means for customer segmentation"
   ]
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   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "        <li><a href=\"#random_generated_dataset\">k-Means on a randomly generated dataset</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#setting_up_K_means\">Setting up K-Means</a></li>\n",
    "                <li><a href=\"#creating_visual_plot\">Creating the Visual Plot</a></li>\n",
    "            </ol>\n",
    "        <li><a href=\"#customer_segmentation_K_means\">Customer Segmentation with K-Means</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#pre_processing\">Pre-processing</a></li>\n",
    "                <li><a href=\"#modeling\">Modeling</a></li>\n",
    "                <li><a href=\"#insights\">Insights</a></li>\n",
    "            </ol>\n",
    "    </ul>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
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   "source": [
    "### Import libraries\n",
    "Lets first import the required libraries.\n",
    "Also run <b> %matplotlib inline </b> since we will be plotting in this section."
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
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   "source": [
    "import random \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
  {
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   "source": [
    "<h1 id=\"random_generated_dataset\">k-Means on a randomly generated dataset</h1>\n",
    "Lets create our own dataset for this lab!\n"
   ]
  },
  {
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   "source": [
    "First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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   "source": [
    "np.random.seed(0)"
   ]
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   "source": [
    "Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
    "<b> <u> Input </u> </b>\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Value will be: 5000 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
    "    <ul> <li> Value will be: 0.9 </li> </ul>\n",
    "</ul>\n",
    "<br>\n",
    "<b> <u> Output </u> </b>\n",
    "<ul>\n",
    "    <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
    "    <ul> <li> The generated samples. </li> </ul> \n",
    "    <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
    "    <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
    "</ul>\n"
   ]
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   "source": [
    "X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "Display the scatter plot of the randomly generated data."
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"setting_up_K_means\">Setting up K-Means</h2>\n",
    "Now that we have our random data, let's set up our K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The KMeans class has many parameters that can be used, but we will be using these three:\n",
    "<ul>\n",
    "    <li> <b>init</b>: Initialization method of the centroids. </li>\n",
    "    <ul>\n",
    "        <li> Value will be: \"k-means++\" </li>\n",
    "        <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\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 (since we have 4 centers)</li> </ul>\n",
    "    <li> <b>n_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. </li>\n",
    "    <ul> <li> Value will be: 12 </li> </ul>\n",
    "</ul>\n",
    "\n",
    "Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's grab the labels for each point in the model using KMeans' <b> .labels\\_ </b> attribute and save it as <b> k_means_labels </b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, ..., 1, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03743147, -0.99782524],\n",
       "       [ 3.97334234,  3.98758687],\n",
       "       [ 1.99741008, -3.01666822],\n",
       "       [ 0.96900523,  0.98370298]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"creating_visual_plot\">Creating the Visual Plot</h2>\n",
    "So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Please read through the code and comments to understand how to plot the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "Try to cluster the above dataset into 3 clusters.  \n",
    "Notice: do not generate data again, use the same dataset as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "# write your code here\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "\n",
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "<h1 id=\"customer_segmentation_K_means\">Customer Segmentation with K-Means</h1>\n",
    "Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data.\n",
    "Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.\n",
    "\n",
    "Lets download the dataset. 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": 23,
   "metadata": {
    "button": false,
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    "deletable": true,
    "jupyter": {
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    },
    "new_sheet": false,
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-08-01 04:01:54--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 34276 (33K) [text/csv]\n",
      "Saving to: ‘Cust_Segmentation.csv’\n",
      "\n",
      "Cust_Segmentation.c 100%[===================>]  33.47K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2020-08-01 04:01:54 (1.69 MB/s) - ‘Cust_Segmentation.csv’ saved [34276/34276]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data From CSV File  \n",
    "Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "button": false,
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>Address</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA001</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NBA021</td>\n",
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       "    <tr>\n",
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       "      <td>5.802</td>\n",
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       "      <td>NBA013</td>\n",
       "      <td>20.9</td>\n",
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       "      <th>3</th>\n",
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       "      <td>29</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA009</td>\n",
       "      <td>6.3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA008</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted Address  DebtIncomeRatio  \n",
       "0        0.0  NBA001              6.3  \n",
       "1        0.0  NBA021             12.8  \n",
       "2        1.0  NBA013             20.9  \n",
       "3        0.0  NBA009              6.3  \n",
       "4        0.0  NBA008              7.2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cust_df = pd.read_csv(\"Cust_Segmentation.csv\")\n",
    "cust_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"pre_processing\">Pre-processing</h2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "As you can see, __Address__ in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
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       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
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       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.9</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  \n",
       "0        0.0              6.3  \n",
       "1        0.0             12.8  \n",
       "2        1.0             20.9  \n",
       "3        0.0              6.3  \n",
       "4        0.0              7.2  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cust_df.drop('Address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Normalizing over the standard deviation\n",
    "Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use __StandardScaler()__ to normalize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
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    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.74291541,  0.31212243, -0.37878978, ..., -0.59048916,\n",
       "        -0.52379654, -0.57652509],\n",
       "       [ 1.48949049, -0.76634938,  2.5737211 , ...,  1.51296181,\n",
       "        -0.52379654,  0.39138677],\n",
       "       [-0.25251804,  0.31212243,  0.2117124 , ...,  0.80170393,\n",
       "         1.90913822,  1.59755385],\n",
       "       ...,\n",
       "       [-1.24795149,  2.46906604, -1.26454304, ...,  0.03863257,\n",
       "         1.90913822,  3.45892281],\n",
       "       [-0.37694723, -0.76634938,  0.50696349, ..., -0.70147601,\n",
       "        -0.52379654, -1.08281745],\n",
       "       [ 2.1116364 , -0.76634938,  1.09746566, ...,  0.16463355,\n",
       "        -0.52379654, -0.2340332 ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X = df.values[:,1:]\n",
    "X = np.nan_to_num(X)\n",
    "Clus_dataSet = StandardScaler().fit_transform(X)\n",
    "Clus_dataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"modeling\">Modeling</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means clustering we can do all this process much easier.\n",
    "\n",
    "Lets apply k-means on our dataset, and take look at cluster labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 0 2 2 1 0 2 0 2 0 0 2 2 2 2 2 2 2 0 2 2 2 2 0 0 0 2 2 0 2 0 2 2 2 2 2 2\n",
      " 2 2 0 2 0 2 1 2 0 2 2 2 0 0 2 2 0 0 2 2 2 0 2 0 2 0 0 2 2 0 2 2 2 0 0 0 2\n",
      " 2 2 2 2 0 2 0 0 1 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 0 0 2 2 2 2 2 2 0 2\n",
      " 2 2 2 2 2 2 2 0 2 2 2 2 2 2 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 2 2 2 2 0 2 0 2\n",
      " 2 2 2 2 2 2 0 2 0 0 2 0 2 2 0 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 0 2 2 2 0 2\n",
      " 2 2 2 2 0 2 2 0 2 0 2 2 0 1 2 0 2 2 2 2 2 2 1 0 2 2 2 2 0 2 2 0 0 2 0 2 0\n",
      " 2 2 2 2 0 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 1 0 2 2 2 2 2 2 2 0 2 2 2 2\n",
      " 2 2 0 2 2 0 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 2 0 2 0 2 0 0 2 2 2 2 2 2\n",
      " 2 2 2 0 0 0 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 2 2 2 2 2 0 2 0 0 2\n",
      " 2 2 2 2 0 2 2 2 2 2 2 0 2 2 0 2 2 0 2 2 2 2 2 0 2 2 2 1 2 2 2 0 2 0 0 0 2\n",
      " 2 2 0 2 2 2 2 2 2 2 2 2 2 2 0 2 0 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2\n",
      " 2 0 2 2 0 2 2 2 2 0 2 2 2 2 0 2 2 0 2 2 2 2 2 2 2 2 2 0 2 2 2 0 2 2 2 2 1\n",
      " 2 2 2 2 2 2 0 2 2 2 1 2 2 2 2 0 2 1 2 2 2 2 0 2 0 0 0 2 2 0 0 2 2 2 2 2 2\n",
      " 2 0 2 2 2 2 0 2 2 2 0 2 0 2 2 2 0 2 2 2 2 0 0 2 2 2 2 0 2 2 2 2 0 2 2 2 2\n",
      " 2 0 0 2 2 2 2 2 2 2 2 2 2 2 1 0 2 2 2 2 2 2 0 2 2 2 2 0 2 2 0 2 2 1 2 1 2\n",
      " 2 1 2 2 2 2 2 2 2 2 2 0 2 0 2 2 1 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 0 2 0\n",
      " 2 2 2 2 2 2 0 2 2 2 2 0 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 0\n",
      " 0 2 2 0 2 0 2 2 0 2 0 2 2 1 2 0 2 0 2 2 2 2 2 0 0 2 2 2 2 0 2 2 2 0 0 2 2\n",
      " 0 2 2 2 0 2 1 2 2 0 2 2 2 2 2 2 2 0 2 2 2 0 2 2 2 2 2 0 2 2 0 2 2 2 2 2 2\n",
      " 2 2 0 2 2 0 2 0 2 0 0 2 2 2 0 2 0 2 2 2 2 2 0 2 2 2 2 0 0 2 2 0 0 2 2 2 2\n",
      " 2 0 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 2 0 2 0 0 2 2 0 2 2 2 2 2 0 0\n",
      " 2 2 2 2 2 2 2 0 2 2 2 2 2 2 1 0 0 2 2 2 2 2 2 2 0 2 2 2 2 2 2 0 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 0]\n"
     ]
    }
   ],
   "source": [
    "clusterNum = 3\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
    "k_means.fit(X)\n",
    "labels = k_means.labels_\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"insights\">Insights</h2>\n",
    "We assign the labels to each row in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "      <th>Clus_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  Clus_km  \n",
       "0        0.0              6.3        2  \n",
       "1        0.0             12.8        0  \n",
       "2        1.0             20.9        2  \n",
       "3        0.0              6.3        2  \n",
       "4        0.0              7.2        1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clus_km\"] = labels\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We can easily check the centroid values by averaging the features in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clus_km</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>402.295082</td>\n",
       "      <td>41.333333</td>\n",
       "      <td>1.956284</td>\n",
       "      <td>15.256831</td>\n",
       "      <td>83.928962</td>\n",
       "      <td>3.103639</td>\n",
       "      <td>5.765279</td>\n",
       "      <td>0.171233</td>\n",
       "      <td>10.724590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410.166667</td>\n",
       "      <td>45.388889</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>227.166667</td>\n",
       "      <td>5.678444</td>\n",
       "      <td>10.907167</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>7.322222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>432.468413</td>\n",
       "      <td>32.964561</td>\n",
       "      <td>1.614792</td>\n",
       "      <td>6.374422</td>\n",
       "      <td>31.164869</td>\n",
       "      <td>1.032541</td>\n",
       "      <td>2.104133</td>\n",
       "      <td>0.285185</td>\n",
       "      <td>10.094761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Customer Id        Age       Edu  Years Employed      Income  \\\n",
       "Clus_km                                                                 \n",
       "0         402.295082  41.333333  1.956284       15.256831   83.928962   \n",
       "1         410.166667  45.388889  2.666667       19.555556  227.166667   \n",
       "2         432.468413  32.964561  1.614792        6.374422   31.164869   \n",
       "\n",
       "         Card Debt  Other Debt  Defaulted  DebtIncomeRatio  \n",
       "Clus_km                                                     \n",
       "0         3.103639    5.765279   0.171233        10.724590  \n",
       "1         5.678444   10.907167   0.285714         7.322222  \n",
       "2         1.032541    2.104133   0.285185        10.094761  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Clus_km').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets look at the distribution of customers based on their age and income:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "button": false,
    "collapsed": false,
    "deletable": true,
    "jupyter": {
     "outputs_hidden": false
    },
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": [
    "area = np.pi * ( X[:, 1])**2  \n",
    "plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
    "plt.xlabel('Age', fontsize=18)\n",
    "plt.ylabel('Income', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f61f8ba0d68>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "# plt.ylabel('Age', fontsize=18)\n",
    "# plt.xlabel('Income', fontsize=16)\n",
    "# plt.zlabel('Education', fontsize=16)\n",
    "ax.set_xlabel('Education')\n",
    "ax.set_ylabel('Age')\n",
    "ax.set_zlabel('Income')\n",
    "\n",
    "ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.\n",
    "Now we can create a profile for each group, considering the common characteristics of each cluster. \n",
    "For example, the 3 clusters can be:\n",
    "\n",
    "- AFFLUENT, EDUCATED AND OLD AGED\n",
    "- MIDDLE AGED AND MIDDLE INCOME\n",
    "- YOUNG AND LOW INCOME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "deletable": true,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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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    "name": "ipython",
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