ML0101EN-Clus-K-Means-Customer-Seg-py-v1 (1).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",
    "# <center>K-Means Clustering</center>"
   ]
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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 the one of the simplest model among 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 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": [
    "### 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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   "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": [
    "# k-Means on a randomly generated dataset\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>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 21,
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   "outputs": [],
   "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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   "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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Setting up K-Means\n",
    "Now that we have our random data, let's set up our K-Means Clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 24,
   "metadata": {
    "button": false,
    "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,
    "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": 25,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": 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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 26,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, ..., 3, 0, 0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 27,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03743147, -0.99782524],\n",
       "       [ 0.96900523,  0.98370298],\n",
       "       [ 1.99741008, -3.01666822],\n",
       "       [ 3.97334234,  3.98758687]])"
      ]
     },
     "execution_count": 27,
     "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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Creating the Visual Plot\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,
    "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": 28,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": 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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write your code here\n",
    "\n",
    "\n",
    "\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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Customer Segmentation with K-Means\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 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": 30,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'wget' n'est pas reconnu en tant que commande interne\n",
      "ou externe, un programme ex‚cutable ou un fichier de commandes.\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,
    "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": 31,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "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": 31,
     "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": [
    "### Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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": 32,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
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       "      <td>0.0</td>\n",
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      ],
      "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cust_df.drop('Address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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 __tandardScaler()__ to normalize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "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",
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       "        -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": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0\n",
      " 0 2 0 0 2 0 0 0 0 2 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 1\n",
      " 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 2 0 1 0 0 0 0 2 0 2 2 2 0 0 2 2 0 0 0 0 0 0\n",
      " 0 2 0 0 0 0 2 0 0 0 2 0 2 0 0 0 2 0 0 0 0 2 2 0 0 0 0 2 0 0 0 0 2 0 0 0 0\n",
      " 0 2 2 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 2 0 0 0 0 2 0 0 2 0 0 1 0 1 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 2 0 2 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 2\n",
      " 0 0 0 0 0 0 2 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2\n",
      " 2 0 0 2 0 2 0 0 2 0 2 0 0 1 0 2 0 2 0 0 0 0 0 2 2 0 0 0 0 2 0 0 0 2 2 0 0\n",
      " 2 0 0 0 2 0 1 0 0 2 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0\n",
      " 0 0 2 0 0 2 0 2 0 2 2 0 0 0 2 0 2 0 0 0 0 0 2 0 0 0 0 2 2 0 0 2 2 0 0 0 0\n",
      " 0 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0 2 0 2 2 0 0 2 0 0 0 0 0 2 2\n",
      " 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 2 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2]\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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Insights\n",
    "We assign the labels to each row in dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "outputs": [
    {
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       "  </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        0  \n",
       "1        0.0             12.8        2  \n",
       "2        1.0             20.9        0  \n",
       "3        0.0              6.3        0  \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,
    "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": 36,
   "metadata": {
    "button": 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>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",
       "    <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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Customer Id        Age       Edu  Years Employed      Income  \\\n",
       "Clus_km                                                                 \n",
       "0         432.468413  32.964561  1.614792        6.374422   31.164869   \n",
       "1         410.166667  45.388889  2.666667       19.555556  227.166667   \n",
       "2         402.295082  41.333333  1.956284       15.256831   83.928962   \n",
       "\n",
       "         Card Debt  Other Debt  Defaulted  DebtIncomeRatio  \n",
       "Clus_km                                                     \n",
       "0         1.032541    2.104133   0.285185        10.094761  \n",
       "1         5.678444   10.907167   0.285714         7.322222  \n",
       "2         3.103639    5.765279   0.171233        10.724590  "
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {
    "button": 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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x26d720b3490>"
      ]
     },
     "execution_count": 38,
     "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,
    "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,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Want to learn more?\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: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\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 [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
    "\n",
    "### Thanks for completing this lesson!\n",
    "\n",
    "Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>\n",
    "\n",
    "<hr>\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).​"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
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  "widgets": {
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