7.1-KMeansClustering.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"KMeansClustering.ipynb","provenance":[],"authorship_tag":"ABX9TyNYnykcIKOQy0KyOJNNK9qG"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"TPjORNDScelw","colab_type":"code","colab":{}},"source":["import numpy as np \n","import pandas as pd\n","import matplotlib.pyplot as plt \n","from sklearn.cluster import KMeans \n","%matplotlib inline\n","from sklearn.preprocessing import MinMaxScaler\n","#https://archive.ics.uci.edu/ml/datasets/Wholesale+customers#\n","#https://github.com/codebasics/py/blob/master/ML/13_kmeans/13_kmeans_tutorial.ipynb"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lHYj72KQckKx","colab_type":"code","colab":{}},"source":["#df = pd.read_csv('https://raw.githubusercontent.com/FazlyRabbiBD/Data-Science-Book/master/data-wholesale-customers.csv')\n","df = pd.read_csv('https://raw.githubusercontent.com/FazlyRabbiBD/Data-Science-Book/master/data-income-age-group.csv')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"D5eJnwc6eD0l","colab_type":"code","outputId":"26eff7bd-b82b-43cc-b601-97855adf5050","executionInfo":{"status":"ok","timestamp":1589791216688,"user_tz":-360,"elapsed":1429,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":200}},"source":["df.head()"],"execution_count":82,"outputs":[{"output_type":"execute_result","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>Name</th>\n","      <th>Age</th>\n","      <th>Income($)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Rob</td>\n","      <td>27</td>\n","      <td>70000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Michael</td>\n","      <td>29</td>\n","      <td>90000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Mohan</td>\n","      <td>29</td>\n","      <td>61000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ismail</td>\n","      <td>28</td>\n","      <td>60000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Kory</td>\n","      <td>42</td>\n","      <td>150000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Name  Age  Income($)\n","0      Rob   27      70000\n","1  Michael   29      90000\n","2    Mohan   29      61000\n","3   Ismail   28      60000\n","4     Kory   42     150000"]},"metadata":{"tags":[]},"execution_count":82}]},{"cell_type":"code","metadata":{"id":"8ceKoJMZ74kF","colab_type":"code","outputId":"17e3ad67-1364-4cad-a03e-dbd3e81996a4","executionInfo":{"status":"ok","timestamp":1589741086408,"user_tz":-360,"elapsed":1007,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["#df.groupby('Region').count()"],"execution_count":0,"outputs":[{"output_type":"execute_result","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>Channel</th>\n","      <th>Fresh</th>\n","      <th>Milk</th>\n","      <th>Grocery</th>\n","      <th>Frozen</th>\n","      <th>Detergents_Paper</th>\n","      <th>Delicassen</th>\n","      <th>ClusterID</th>\n","    </tr>\n","    <tr>\n","      <th>Region</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>1</th>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>77</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>47</td>\n","      <td>47</td>\n","      <td>47</td>\n","      <td>47</td>\n","      <td>47</td>\n","      <td>47</td>\n","      <td>47</td>\n","      <td>47</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>316</td>\n","      <td>316</td>\n","      <td>316</td>\n","      <td>316</td>\n","      <td>316</td>\n","      <td>316</td>\n","      <td>316</td>\n","      <td>316</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Channel  Fresh  Milk  ...  Detergents_Paper  Delicassen  ClusterID\n","Region                        ...                                         \n","1            77     77    77  ...                77          77         77\n","2            47     47    47  ...                47          47         47\n","3           316    316   316  ...               316         316        316\n","\n","[3 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":79}]},{"cell_type":"code","metadata":{"id":"Ghr8Ajjjq-e2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":200},"outputId":"dbf076e2-10ad-4041-d148-e9742aeb5a93","executionInfo":{"status":"ok","timestamp":1589792494207,"user_tz":-360,"elapsed":4504,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["dfp = df.drop('Name', axis=1)\n","dfp.head()"],"execution_count":92,"outputs":[{"output_type":"execute_result","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>Age</th>\n","      <th>Income($)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>27</td>\n","      <td>70000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>29</td>\n","      <td>90000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>29</td>\n","      <td>61000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>28</td>\n","      <td>60000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>42</td>\n","      <td>150000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Age  Income($)\n","0   27      70000\n","1   29      90000\n","2   29      61000\n","3   28      60000\n","4   42     150000"]},"metadata":{"tags":[]},"execution_count":92}]},{"cell_type":"code","metadata":{"id":"biYKXX-8eJpg","colab_type":"code","outputId":"487a4e65-2ef8-4aa9-c81b-1295a90dee49","executionInfo":{"status":"ok","timestamp":1589786895865,"user_tz":-360,"elapsed":1199,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":195}},"source":["df.info()"],"execution_count":22,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 22 entries, 0 to 21\n","Data columns (total 3 columns):\n"," #   Column     Non-Null Count  Dtype \n","---  ------     --------------  ----- \n"," 0   Name       22 non-null     object\n"," 1   Age        22 non-null     int64 \n"," 2   Income($)  22 non-null     int64 \n","dtypes: int64(2), object(1)\n","memory usage: 656.0+ bytes\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"78AbAC2Pprx4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":297},"outputId":"0447b646-bb27-49f3-822f-c4773e8910aa","executionInfo":{"status":"ok","timestamp":1589790890541,"user_tz":-360,"elapsed":4769,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["plt.scatter(dfp.Age,dfp['Income($)'])\n","plt.xlabel('Age')\n","plt.ylabel('Income($)')"],"execution_count":63,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Income($)')"]},"metadata":{"tags":[]},"execution_count":63},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"pMjfDMs5p-0q","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":200},"outputId":"ef8c961b-0092-47c0-9955-3cf4e36c711b","executionInfo":{"status":"ok","timestamp":1589792843758,"user_tz":-360,"elapsed":3056,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["scaler = MinMaxScaler()\n","scaler.fit(dfp)\n","scaled = pd.DataFrame(scaler.transform(dfp),columns=dfp.columns)\n","scaled.head()"],"execution_count":101,"outputs":[{"output_type":"execute_result","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>Age</th>\n","      <th>Income($)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.058824</td>\n","      <td>0.213675</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.176471</td>\n","      <td>0.384615</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.176471</td>\n","      <td>0.136752</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.117647</td>\n","      <td>0.128205</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.941176</td>\n","      <td>0.897436</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Age  Income($)\n","0  0.058824   0.213675\n","1  0.176471   0.384615\n","2  0.176471   0.136752\n","3  0.117647   0.128205\n","4  0.941176   0.897436"]},"metadata":{"tags":[]},"execution_count":101}]},{"cell_type":"code","metadata":{"id":"KOuwsGXgeXNt","colab_type":"code","colab":{}},"source":["#from sklearn.preprocessing import normalize\n","#data_scaled = normalize(df)\n","#data_scaled = pd.DataFrame(data_scaled, columns=df.columns)\n","#data_scaled.head()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_gIRa7VkqWT8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":716},"outputId":"25cfb2b7-2e2c-4e18-ab1d-514bd8aa38ba","executionInfo":{"status":"ok","timestamp":1589792909441,"user_tz":-360,"elapsed":1440,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["scaled"],"execution_count":102,"outputs":[{"output_type":"execute_result","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>Age</th>\n","      <th>Income($)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.058824</td>\n","      <td>0.213675</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.176471</td>\n","      <td>0.384615</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.176471</td>\n","      <td>0.136752</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.117647</td>\n","      <td>0.128205</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.941176</td>\n","      <td>0.897436</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.764706</td>\n","      <td>0.940171</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.882353</td>\n","      <td>0.982906</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.705882</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.588235</td>\n","      <td>0.948718</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.529412</td>\n","      <td>0.726496</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.647059</td>\n","      <td>0.786325</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.058824</td>\n","      <td>0.025641</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>0.117647</td>\n","      <td>0.051282</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>0.176471</td>\n","      <td>0.038462</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>0.352941</td>\n","      <td>0.068376</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>0.823529</td>\n","      <td>0.170940</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>0.882353</td>\n","      <td>0.153846</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>1.000000</td>\n","      <td>0.162393</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>0.764706</td>\n","      <td>0.299145</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>0.882353</td>\n","      <td>0.316239</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>0.764706</td>\n","      <td>0.111111</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         Age  Income($)\n","0   0.058824   0.213675\n","1   0.176471   0.384615\n","2   0.176471   0.136752\n","3   0.117647   0.128205\n","4   0.941176   0.897436\n","5   0.764706   0.940171\n","6   0.882353   0.982906\n","7   0.705882   1.000000\n","8   0.588235   0.948718\n","9   0.529412   0.726496\n","10  0.647059   0.786325\n","11  0.000000   0.000000\n","12  0.058824   0.025641\n","13  0.117647   0.051282\n","14  0.176471   0.038462\n","15  0.352941   0.068376\n","16  0.823529   0.170940\n","17  0.882353   0.153846\n","18  1.000000   0.162393\n","19  0.764706   0.299145\n","20  0.882353   0.316239\n","21  0.764706   0.111111"]},"metadata":{"tags":[]},"execution_count":102}]},{"cell_type":"code","metadata":{"id":"btqtMgPRmSX3","colab_type":"code","outputId":"d2606c8c-58ab-4232-8afc-10c044388870","executionInfo":{"status":"ok","timestamp":1589792926597,"user_tz":-360,"elapsed":2147,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":295}},"source":["Sum_of_squared_distances = []\n","K = range(1,15)\n","for k in K:\n","    km = KMeans(n_clusters=k)\n","    km = km.fit(scaled)\n","    Sum_of_squared_distances.append(km.inertia_)\n","\n","plt.plot(K, Sum_of_squared_distances, 'bx-')\n","plt.xlabel('k')\n","plt.ylabel('Sum_of_squared_distances')\n","plt.title('Elbow Method For Optimal k')\n","plt.show()"],"execution_count":103,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"57fGCPMsep8b","colab_type":"code","outputId":"7123ff2f-9c69-4e21-dd97-784a407ebe33","executionInfo":{"status":"ok","timestamp":1589793070621,"user_tz":-360,"elapsed":1191,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["clusterNum = 3\n","k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n","k_means.fit(scaled)\n","labels = k_means.labels_\n","print(labels)"],"execution_count":104,"outputs":[{"output_type":"stream","text":["[0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 2 2 2 2 2 2]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hqvZhbGcsq8W","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":70},"outputId":"06c8c53c-202b-4453-dc9b-a9532d8ff477","executionInfo":{"status":"ok","timestamp":1589791242731,"user_tz":-360,"elapsed":1487,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["k_means.cluster_centers_"],"execution_count":86,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.85294118, 0.2022792 ],\n","       [0.72268908, 0.8974359 ],\n","       [0.1372549 , 0.11633428]])"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"code","metadata":{"id":"5BuicStegFx8","colab_type":"code","outputId":"5459ff42-305f-4c94-ad6d-d8aecf21511b","executionInfo":{"status":"ok","timestamp":1589793090238,"user_tz":-360,"elapsed":1472,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":200}},"source":["df[\"Cluster\"] = labels\n","df.head(5)"],"execution_count":105,"outputs":[{"output_type":"execute_result","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>Name</th>\n","      <th>Age</th>\n","      <th>Income($)</th>\n","      <th>Cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Rob</td>\n","      <td>27</td>\n","      <td>70000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Michael</td>\n","      <td>29</td>\n","      <td>90000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Mohan</td>\n","      <td>29</td>\n","      <td>61000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ismail</td>\n","      <td>28</td>\n","      <td>60000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Kory</td>\n","      <td>42</td>\n","      <td>150000</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Name  Age  Income($)  Cluster\n","0      Rob   27      70000        0\n","1  Michael   29      90000        0\n","2    Mohan   29      61000        0\n","3   Ismail   28      60000        0\n","4     Kory   42     150000        1"]},"metadata":{"tags":[]},"execution_count":105}]},{"cell_type":"code","metadata":{"id":"bUWCAxSogKnK","colab_type":"code","outputId":"7c31e167-57b6-4990-a04d-cbe68c8c2ed2","executionInfo":{"status":"ok","timestamp":1589793155604,"user_tz":-360,"elapsed":1247,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":170}},"source":["df.groupby('Cluster').mean()"],"execution_count":106,"outputs":[{"output_type":"execute_result","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>Age</th>\n","      <th>Income($)</th>\n","    </tr>\n","    <tr>\n","      <th>Cluster</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>28.333333</td>\n","      <td>58611.111111</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>38.285714</td>\n","      <td>150000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>40.500000</td>\n","      <td>68666.666667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               Age      Income($)\n","Cluster                          \n","0        28.333333   58611.111111\n","1        38.285714  150000.000000\n","2        40.500000   68666.666667"]},"metadata":{"tags":[]},"execution_count":106}]},{"cell_type":"code","metadata":{"id":"erB4Dvri4Ne1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":170},"outputId":"21f787a5-dcb9-4f11-a182-99e7524c01e6","executionInfo":{"status":"ok","timestamp":1589790976558,"user_tz":-360,"elapsed":1013,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["df.groupby('Cluster').count()"],"execution_count":73,"outputs":[{"output_type":"execute_result","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>Name</th>\n","      <th>Age</th>\n","      <th>Income($)</th>\n","    </tr>\n","    <tr>\n","      <th>Cluster</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>7</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         Name  Age  Income($)\n","Cluster                      \n","0           9    9          9\n","1           6    6          6\n","2           7    7          7"]},"metadata":{"tags":[]},"execution_count":73}]},{"cell_type":"code","metadata":{"id":"cTqzQMH54rjC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":716},"outputId":"974127b4-3f84-49d9-b410-516678017361","executionInfo":{"status":"ok","timestamp":1589791020776,"user_tz":-360,"elapsed":1220,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["df"],"execution_count":75,"outputs":[{"output_type":"execute_result","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>Name</th>\n","      <th>Age</th>\n","      <th>Income($)</th>\n","      <th>Cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Rob</td>\n","      <td>27</td>\n","      <td>70000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Michael</td>\n","      <td>29</td>\n","      <td>90000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Mohan</td>\n","      <td>29</td>\n","      <td>61000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ismail</td>\n","      <td>28</td>\n","      <td>60000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Kory</td>\n","      <td>42</td>\n","      <td>150000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Gautam</td>\n","      <td>39</td>\n","      <td>155000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>David</td>\n","      <td>41</td>\n","      <td>160000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Andrea</td>\n","      <td>38</td>\n","      <td>162000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Brad</td>\n","      <td>36</td>\n","      <td>156000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Angelina</td>\n","      <td>35</td>\n","      <td>130000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Donald</td>\n","      <td>37</td>\n","      <td>137000</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Tom</td>\n","      <td>26</td>\n","      <td>45000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Arnold</td>\n","      <td>27</td>\n","      <td>48000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Jared</td>\n","      <td>28</td>\n","      <td>51000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Stark</td>\n","      <td>29</td>\n","      <td>49500</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Ranbir</td>\n","      <td>32</td>\n","      <td>53000</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Dipika</td>\n","      <td>40</td>\n","      <td>65000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Priyanka</td>\n","      <td>41</td>\n","      <td>63000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Nick</td>\n","      <td>43</td>\n","      <td>64000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>Alia</td>\n","      <td>39</td>\n","      <td>80000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Sid</td>\n","      <td>41</td>\n","      <td>82000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Abdul</td>\n","      <td>39</td>\n","      <td>58000</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        Name  Age  Income($)  Cluster\n","0        Rob   27      70000        0\n","1    Michael   29      90000        0\n","2      Mohan   29      61000        0\n","3     Ismail   28      60000        0\n","4       Kory   42     150000        2\n","5     Gautam   39     155000        2\n","6      David   41     160000        2\n","7     Andrea   38     162000        2\n","8       Brad   36     156000        2\n","9   Angelina   35     130000        2\n","10    Donald   37     137000        2\n","11       Tom   26      45000        0\n","12    Arnold   27      48000        0\n","13     Jared   28      51000        0\n","14     Stark   29      49500        0\n","15    Ranbir   32      53000        0\n","16    Dipika   40      65000        1\n","17  Priyanka   41      63000        1\n","18      Nick   43      64000        1\n","19      Alia   39      80000        1\n","20       Sid   41      82000        1\n","21     Abdul   39      58000        1"]},"metadata":{"tags":[]},"execution_count":75}]},{"cell_type":"code","metadata":{"id":"XNYoPn_f7qfA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":283},"outputId":"f58b614a-0c77-4725-dce4-09dd456e5bb5","executionInfo":{"status":"ok","timestamp":1589795847664,"user_tz":-360,"elapsed":1173,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["df1 = df[df.Cluster==0]\n","df2 = df[df.Cluster==1]\n","df3 = df[df.Cluster==2]\n","\n","plt.scatter(df1.Age,df1['Income($)'],color='yellow')\n","plt.scatter(df2.Age,df2['Income($)'],color='red')\n","plt.scatter(df3.Age,df3['Income($)'],color='black')\n","plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n","plt.legend()"],"execution_count":113,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f41af5ccb38>"]},"metadata":{"tags":[]},"execution_count":113},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAfsUlEQVR4nO3df5BV5Z3n8feHH0JajNDYYQw/unFkdcD4szWk3Mz4YwYxWmKVmajTE1lD0rVrYjKb2WRwqFqiGWribGpMyGomvdEVtRNijBuZlC7LKFkzqfFHkzgqELWjNDal0gHEuAwo4bt/nKfx0vTpH/d2973d9/OqunXP+Z7nnPPcU3C+fc7znPMoIjAzM+vNuHJXwMzMKpeThJmZ5XKSMDOzXE4SZmaWy0nCzMxyTSh3BYbaCSecEA0NDeWuhpnZqLJp06bfRERdz/iYSxINDQ20tbWVuxpmZqOKpI7e4r7dZGZmuZwkzMwsl5OEmZnlGnNtEr1599136ezsZP/+/eWuyqgwefJkZs2axcSJE8tdFTMrs6pIEp2dnRx33HE0NDQgqdzVqWgRwa5du+js7GTu3Lnlro6ZlVlV3G7av38/06dPd4IYAElMnz7dV11mfWlthYYGGDcu+25tLXeNhk1VXEkAThCD4GNl1ofWVmhuhn37svmOjmweoKmpfPUaJlVxJWFmNmRWrHgvQXTbty+Lj0H9JglJd0naKen5HvEbJf1K0mZJf1cQv0lSu6QXJF1SEF+cYu2SlhfE50p6MsV/IOmYFJ+U5tvT8oah+MGj1Ztvvskdd9wx6PXa2tr4/Oc/3+uyhoYGfvOb35RaNbPqsn374OIDVaG3sAZyJXE3sLgwIOlCYAlwRkQsAL6e4vOBa4AFaZ07JI2XNB64HbgUmA9cm8oC3ArcFhEnA3uAZSm+DNiT4relclWrryRx8ODB3PUaGxtZvXr1cFXLrPrMmTO4+EB038Lq6ICI925hVUCi6DdJRMTjwO4e4f8EfC0iDqQyO1N8CbA2Ig5ExCtAO3Be+rRHxMsR8Q6wFlii7Ob3RcADaf01wJUF21qTph8ALtYI3izfv3c/ty+4nf17h6YB95577uH000/njDPO4JOf/CRdXV1cddVVnHvuuZx77rn8/Oc/B+ArX/kKn/rUp7jgggs46aSTDp/gly9fzq9//WvOPPNMvvSlL/HTn/6Uj370o1xxxRXMnz+f/fv3c/311/OhD32Is846i40bNwLw05/+lMsvvxyAXbt2sWjRIhYsWMCnP/1pPCqhWRFWrYKamiNjNTVZvFiVfAsrIvr9AA3A8wXzzwA3A08C/xc4N8X/O/DnBeXuBD6ePt8tiH8ylT2BLHl0x2d37wd4HphVsOzXwAk59WsG2oC2OXPmRE9btmw5KtafZ1ufja/wlXj2e88Oet2enn/++Zg3b150dXVFRMSuXbvi2muvjZ/97GcREdHR0RGnnnpqRESsXLkyPvKRj8T+/fujq6sramtr45133olXXnklFixYcHibGzdujJqamnj55ZcjIuLrX/96XH/99RERsXXr1pg9e3b827/9W2zcuDEuu+yyiIi48cYb4+abb46IiJ/85CcBHK5TT8UcM7Oqcd99EfX1EVL2fd99pW1PisiuIY78SENR2wEB2qKX82uxvZsmALXAQuBc4H5JJxW5rZJFRAvQAtDY2FjSn8c/+rMf8cK6F/jdgd8B8OPrfsw/fuYfOeWKU7jqe1cVtc3HHnuMP/3TP+WEE04AoLa2ln/6p39iy5Yth8u89dZbvP322wBcdtllTJo0iUmTJvGBD3yAN954o9ftnnfeeYefZfjnf/5nbrzxRgBOPfVU6uvrefHFF48o//jjj/Pggw8e3se0adOK+j1mVa+paWh7Ms2Zk91i6i1eZsX2buoEHkwJ6CngENlVwQ6yq4Fus1IsL74LmCppQo84heuk5cen8sPqwlsu5Pg5xzNuYnZoxk0cx9T6qVz41QuHdD+HDh3iiSee4JlnnuGZZ55hx44dTJkyBYBJkyYdLjd+/PjcNodjjz12SOtkVnUqpbF4OG5hDZFik8SPgQsBJP074BjgN8A64JrUM2kuMA94CngamJd6Mh1D1ri9Ll3ibCS7HQWwFHgoTa9L86Tlj6Xyw6r25FouvOVCDr17iInHTuTQu4e44OYLqP392qK3edFFF/HDH/6QXbuyHLd7924WLVrEt771rcNlnnnmmT63cdxxx/Hb3/42d/lHP/pRWtM/8BdffJHt27dzyimnHFHmD//wD/ne974HwCOPPMKePXuK+j1mY8JwNRYXk3iamqClBerrQcq+W1oq4rmLgXSB/T7wL8ApkjolLQPuAk5K3WLXAkvTVcVm4H5gC/C/gc9GxO8i4iDwOWA9sBW4P5UF+Cvgi5Lagelk7Rik7+kp/kXgcLfZ4bb5/s1MPHYiF9x8AROPncjmH27uf6U+LFiwgBUrVvBHf/RHnHHGGXzxi19k9erVtLW1cfrppzN//nz+4R/+oc9tTJ8+nfPPP5/TTjuNL33pS0ctv+GGGzh06BAf+tCHuPrqq7n77ruPuCIBWLlyJY8//jgLFizgwQcfZE4FXMqalc1wNBaXkniammDbNjh0KPuugAQBoBH443xENTY2Rs9Bh7Zu3cof/MEfDHgbO57ewfFzjmfKjCm8/cbbvPXqW3yw8YNDXdWKNthjZjbqjBuXnch7krITdTEaGnpvW6ivz078FUzSpoho7BmvmtdyDMbMc2cenp4yYwpTZkwpY23MbFgMR2PxcD1oV0Z+LYeZVafhaCwejgftyqxqksRYu602nHysrCoMR2NxBfdSKlZVJInJkyeza9cun/wGINJ4EpMnTy53VcyG31A3Fg9XL6UydtWtioZrj0w3OB6ZzqyC9Hw1OWRXJ0PcRTav4boqkoSZ2ag1Qj2m8pJEVdxuMjMbtcrcY8pJwsyskpW5x5SThJlZJStzjyknCTOzSlbm9zr5iWszs0o31K8mHwRfSZiZWS4nCTMzy+UkYWZmuZwkzMws10AGHbpL0s40wFDPZX8pKSSdkOYlabWkdknPSjq7oOxSSS+lz9KC+DmSnkvrrJakFK+VtCGV3yDJAzKbmY2wgVxJ3A0s7hmUNBtYBBQ+9ncp2ZCl84Bm4NupbC2wEvgwcB6wsuCk/23gMwXrde9rOfBoRMwDHmUER6YzM7NMv0kiIh4Hdvey6Dbgy0Dhy5+WAPekoUyfAKZKOhG4BNgQEbsjYg+wAViclr0/Ip5I41ffA1xZsK01aXpNQdzMzEZIUW0SkpYAOyLiX3ssmgm8WjDfmWJ9xTt7iQPMiIjX0vTrwIw+6tMsqU1SW1dX12B/jpmZ5Rh0kpBUA/w18F+Hvjq9S1cZua+rjYiWiGiMiMa6urqRqpaZjWVlHMOhkhRzJfH7wFzgXyVtA2YBv5D0e8AOYHZB2Vkp1ld8Vi9xgDfS7SjS984i6mpmNnjdYzh0dEBE9t3cXJWJYtBJIiKei4gPRERDRDSQ3SI6OyJeB9YB16VeTguBvemW0XpgkaRpqcF6EbA+LXtL0sLUq+k64KG0q3VAdy+opQVxM7PhtWLFkYP8QDa/YkV56lNGA+kC+33gX4BTJHVKWtZH8YeBl4F24H8ANwBExG7gq8DT6XNLipHKfDet82vgkRT/GvAnkl4C/jjNm5kNvzKP4VBJPDKdmVlPIzQaXCXxyHRmZgNV5jEcKomThJlZT2Uew6GSeDwJM7PelHEMh0riKwkzG9v8vENJfCVhZmNX9/MO3d1Zu593AF8lDJCvJMxs7PLzDiVzkjCzscvPO5TMScLMxq45cwYXt6M4SZjZ2OXnHUrmJGFmY5efdyiZezeZ2djm5x1K4isJMzPL5SRhZma5nCTMzCyXk4SZmeUayKBDd0naKen5gth/k/QrSc9K+l+SphYsu0lSu6QXJF1SEF+cYu2SlhfE50p6MsV/IOmYFJ+U5tvT8oah+tFmZjYwA7mSuBtY3CO2ATgtIk4HXgRuApA0H7gGWJDWuUPSeEnjgduBS4H5wLWpLMCtwG0RcTKwB+ge+W4ZsCfFb0vlzMxsBPWbJCLicWB3j9j/iYiDafYJYFaaXgKsjYgDEfEK2ZCk56VPe0S8HBHvAGuBJWlc64uAB9L6a4ArC7a1Jk0/AFycypuZ2QgZijaJT/HeuNQzgVcLlnWmWF58OvBmQcLpjh+xrbR8byp/FEnNktoktXV1dZX8g8zMLFNSkpC0AjgIlPUF7RHREhGNEdFYV1dXzqqYmY0pRT9xLek/AJcDF0dEpPAOYHZBsVkpRk58FzBV0oR0tVBYvntbnZImAMen8mZmNkKKupKQtBj4MnBFRBS+rH0dcE3qmTQXmAc8BTwNzEs9mY4ha9xel5LLRuDjaf2lwEMF21qapj8OPFaQjMzMbAT0eyUh6fvABcAJkjqBlWS9mSYBG1Jb8hMR8R8jYrOk+4EtZLehPhsRv0vb+RywHhgP3BURm9Mu/gpYK+lvgF8Cd6b4ncC9ktrJGs6vGYLfa2Zmg6Cx9sd5Y2NjtLW1lbsaZmajiqRNEdHYM+4nrs3MLJeThJmZ5XKSMDOzXE4SZmaWy0nCzMxyOUmYmVkuJwkzM8vlJGFmZrmcJMzMLJeThJmZ5XKSMDOzXE4SZmaWy0nCzMxyOUmYmVkuJwkzM8vlJGFmZrn6TRKS7pK0U9LzBbFaSRskvZS+p6W4JK2W1C7pWUlnF6yzNJV/SdLSgvg5kp5L66xWGuoubx9mZjZyBnIlcTewuEdsOfBoRMwDHk3zAJeSjWs9D2gGvg3ZCZ9s2NMPA+cBKwtO+t8GPlOw3uJ+9mFmZiOk3yQREY+TjTFdaAmwJk2vAa4siN8TmSeAqZJOBC4BNkTE7ojYA2wAFqdl74+IJyIbR/WeHtvqbR9mZjZCim2TmBERr6Xp14EZaXom8GpBuc4U6yve2Uu8r30cRVKzpDZJbV1dXUX8HDMz603JDdfpCiCGoC5F7yMiWiKiMSIa6+rqhrMqZmZVpdgk8Ua6VUT63pniO4DZBeVmpVhf8Vm9xPvah5mZjZBik8Q6oLuH0lLgoYL4damX00Jgb7pltB5YJGlaarBeBKxPy96StDD1arqux7Z624eZmY2QCf0VkPR94ALgBEmdZL2UvgbcL2kZ0AF8IhV/GPgY0A7sA64HiIjdkr4KPJ3K3RIR3Y3hN5D1oHof8Ej60Mc+zMxshCi73T92NDY2RltbW7mrYWY2qkjaFBGNPeN+4trMzHI5SZiZWS4nCTMzy+UkYWZFaAUayE4hDWnexqJ+ezeZmR2plezVbPvSfEeaB2gqS41s+PhKwswGaQXvJYhu+1LcxhonCTMbpO2DjNto5iRhZoM0Z5BxG82cJMxskFYBNT1iNSluY42ThJkNUhPQAtQDSt8tuNF6bHLvJjMrQhNOCtXBVxJmZpbLScLMzHI5SZiZVYDW1lYaGhoYN24cDQ0NtLZWxlPsbpMwMyuz1tZWmpub2bcve0ixo6OD5ubsKfampvK2/ZR0JSHpP0vaLOl5Sd+XNFnSXElPSmqX9ANJx6Syk9J8e1reULCdm1L8BUmXFMQXp1i7pOWl1NXMbCQUc0WwYsWKwwmi2759+1ixovxPsRedJCTNBD4PNEbEacB44BrgVuC2iDgZ2AMsS6ssA/ak+G2pHJLmp/UWAIuBOySNlzQeuB24FJgPXJvKmplVpO4rgo6ODiLi8BVBf4li+/ben1bPi4+kUtskJgDvkzSB7Gma14CLgAfS8jXAlWl6SZonLb84jWu9BFgbEQci4hWyoU/PS5/2iHg5It4B1qayZmYVqdgrgjlzen9aPS8+kopOEhGxA/g62QtbXgP2ApuANyPiYCrWCcxM0zOBV9O6B1P56YXxHuvkxY8iqVlSm6S2rq6uYn+SmVlJir0iWLVqFTU1Rz7FXlNTw6pV5X+KvZTbTdPI/rKfC3wQOJbsdtGIi4iWiGiMiMa6urpyVMHMrOgrgqamJlpaWqivr0cS9fX1tLS0lL3RGkq73fTHwCsR0RUR7wIPAucDU9PtJ4BZwI40vQOYDZCWHw/sKoz3WCcvbmZWkUq5ImhqamLbtm0cOnSIbdu2VUSCgNKSxHZgoaSa1LZwMbAF2Ah8PJVZCjyUpteledLyxyIiUvya1PtpLjAPeAp4GpiXeksdQ9a4va6E+pqZDatKviIolrLzdJErSzcDVwMHgV8CnyZrN1gL1KbYn0fEAUmTgXuBs4DdwDUR8XLazgrgU2k7fxERj6T4x4BvkPWcuisi+k3HjY2N0dbWVvRvMjOrRpI2RUTjUfFSkkQlcpIwMxu8vCTh13KYmVkuJwkzM8vlJGFmZrmcJMzMLJeThFlVawUayE4FDWne7D1+VbhZ1WoFmoHudw11pHnw0KTWzVcSZlVrBe8liG77Utws4yRhVrXyXjpX/tdTW+VwkjCrWnkvnSv/66mtcjhJmFWtVWTDwBSqSXGzjJOEWdVqAlqAekDpuwU3Wg+fYoY2LTcnCbOq1gRsAw6lbyeI4VLs0KYD3fZwJR8nCTOzEdDf0KbFnuiHM/mA3wJrZjYixo0bR2/nW0nce++9NDc3H5FEampqBjQWRUNDAx0dHUfF6+vr2bZt24Dr57fAmtkQ8pPag9XX0Kb9XWX0pdhxtQfKScLMBqn7Se0OIHjvSW0nir70NbRpKSf6YsfVHqiSkoSkqZIekPQrSVslfURSraQNkl5K39NSWUlaLald0rOSzi7YztJU/iVJSwvi50h6Lq2zOg2TamYjIu9qwU9qF6OvoU1LOdGXMq72gERE0R9gDfDpNH0MMBX4O2B5ii0Hbk3THwMeIetrtxB4MsVrgZfT97Q0PS0teyqVVVr30v7qdM4554SZleq+iKiJI/971aS4ovf/fipLTceC++67L2pqaoLs0iyAqKmpifvuu2/A69fX14ekqK+vH/B6hYC26O0831twIB/geOAVUuN3QfwF4MQ0fSLwQpr+DnBtz3LAtcB3CuLfSbETgV8VxI8ol/dxkjAbCvXR+3+x+n6WWbEn7KE40ZciL0mU8hbYuUAX8D8lnQFsAr4AzIiI11KZ14EZaXom8GrB+p0p1le8s5f4USQ1k15fOVT34cyqW1/vdbqXI98eC35SO9PdHbW7Ebq7OyrQby+lpqamfsuUQyltEhOAs4FvR8RZwP8ju710WMpOw97HNiJaIqIxIhrr6uqGe3dmVaCv9zr5Se08pfRSqlSlJIlOoDMinkzzD5AljTcknQiQvnem5TuA2QXrz0qxvuKzeomb2bDr771OflK7N8PdHbUcik4SEfE68KqkU1LoYmALsA7o7qG0FHgoTa8Drku9nBYCe9NtqfXAIknTUk+oRcD6tOwtSQtTr6brCrZlZsPKVwvFGO7uqOVQ6sh0NwKtko4h65V0PVniuV/SMrIO1J9IZR8m6+HUTnYz83qAiNgt6avA06ncLRGxO03fANwNvI+sd9MjJdbXzAasCSeFwVm1alWvT04PWXfUMvBrOcysgrSSPW+xnaz9YxWjLVG1trayYsUKtm/fzpw5c1i1alVFNkj3lPdaDicJM6sQPcfchqwdxLe5RoLf3WRmFc5PclciJwkzyzHSL/HzmNuVyEnCzHpRjpf4ecztSuQkYWa96O/Wz3BcZXjM7UrkJGFW1fJO9n3d+hmuqww/m1GJSn1OwsxGrZ69ibpP9pDd4jl6tLMs3tdVRqkndD+bUWl8JWFWtfo62fd168cNzNXEScKsavV1su/r1o8bmKuJk4TZmJfX7tDfyT7vJX5uYK4mThJmY1pfjczFnuzdwFxN3HBtNqb11e6wraDMYN+V5AbmauEkYTam9dfI7JO99c23m8zGNDcyW2mcJMzGNDcyW2lKThKSxkv6paSfpPm5kp6U1C7pB2lAIiRNSvPtaXlDwTZuSvEXJF1SEF+cYu2Slvfct5n1x43MVpqhuJL4ArC1YP5W4LaIOBnYAyxL8WXAnhS/LZVD0nzgGmABsBi4IyWe8cDtwKXAfODaVNbMBsXjUVvxSkoSkmYBlwHfTfMCLgIeSEXWAFem6SVpnrT84lR+CbA2Ig5ExCtkw5uelz7tEfFyRLwDrE1lzcxshJR6JfEN4Mtkf6IATAfejIiDab4TmJmmZwKvAqTle1P5w/Ee6+TFjyKpWVKbpLaurq4Sf5KZmXUrOklIuhzYGRGbhrA+RYmIlohojIjGurq6clfHzGzMKOU5ifOBKyR9DJgMvB/4JjBV0oR0tTAL2JHK7wBmA52SJgDHA7sK4t0K18mLm5nZCCj6SiIiboqIWRHRQNbw/FhENAEbgY+nYkuBh9L0ujRPWv5YRESKX5N6P80F5gFPAU8D81JvqWPSPtYVW18zMxu84Xji+q+AtZL+BvglcGeK3wncK6kd2E120iciNku6H9gCHAQ+GxG/A5D0OWA9MB64KyI2D0N9zcwsh7I/5seOxsbGaGtrK3c1zMxGFUmbIqKxZ9xPXJuZWS4nCTMzy+UkYWZmuZwkzMwsl5OEmZnlcpIwM7NcThJmZpbLScLMzHI5SZiZWS4nCTMzy+UkYWZmuZwkzMwsl5OEmZnlcpIwM7NcThJmZparlDGuZ0vaKGmLpM2SvpDitZI2SHopfU9LcUlaLald0rOSzi7Y1tJU/iVJSwvi50h6Lq2zWpJK+bFmZjY4pVxJHAT+MiLmAwuBz0qaDywHHo2IecCjaR7gUrKhSecBzcC3IUsqwErgw8B5wMruxJLKfKZgvcUl1NfMzAaplDGuX4uIX6Tp3wJbgZnAEmBNKrYGuDJNLwHuicwTwFRJJwKXABsiYndE7AE2AIvTsvdHxBNpLOx7CrZlZmYjYEjaJCQ1AGcBTwIzIuK1tOh1YEaangm8WrBaZ4r1Fe/sJW5mZiOk5CQhaQrwI+AvIuKtwmXpCmDYB9GW1CypTVJbV1fXcO/OzKxqlJQkJE0kSxCtEfFgCr+RbhWRvnem+A5gdsHqs1Ksr/isXuJHiYiWiGiMiMa6urpSfpKZmRUopXeTgDuBrRHx9wWL1gHdPZSWAg8VxK9LvZwWAnvTban1wCJJ01KD9SJgfVr2lqSFaV/XFWzLzMxGwIQS1j0f+CTwnKRnUuyvga8B90taBnQAn0jLHgY+BrQD+4DrASJit6SvAk+ncrdExO40fQNwN/A+4JH0MTOzEaKs2WDsaGxsjLa2tnJXw8xsVJG0KSIae8b9xLWZmeVykjAzs1xOEmZmlstJwszMcjlJmJlZLicJMzPL5SRhZma5nCTMzCyXk4SZmeVykjAzs1xOEmZmlstJwszMcjlJmJlZLicJMzPL5SRhZma5nCTMzCxXKSPTjQhJi4FvAuOB70bE14ZjP3u372X1yas5FIeonVvL1Q9ezXfO/g6H3j0EwLSTptH8i2YmHz95OHZvZlaRKvpKQtJ44HbgUmA+cK2k+cOxr5/f+vMsIRyE3S/tZv0X1x9OEAB7Xt7DSw+/NBy7NjOrWBU9fKmkjwBfiYhL0vxNABHxt3nrDHb40m80fIO9HXsHVa/Trj2Nq7531aDWMTOrZKN1+NKZwKsF850pdgRJzZLaJLV1dXUNagdX3HkF4yYM/DAc98HjuPCrFw5qH2Zmo1WlJ4kBiYiWiGiMiMa6urpBrXvSxSfx4c9/eGCFBYu/uZja368topZmZqNPpSeJHcDsgvlZKTaknr//+YEVDNj8w81DvXszs4pV6b2bngbmSZpLlhyuAf5sqHdy0d9cxN5X91J7ci1Tfm8KT33rKd5+/W0mTZ3EiWefyIG9Bzjw1gGOrTuW064+bah3b2ZWsSo6SUTEQUmfA9aTdYG9KyKG/E/5M5eeecT83AvmDvUuzMxGpYpOEgAR8TDwcLnrYWZWjSq9TcLMzMrIScLMzHI5SZiZWS4nCTMzy1XRr+UohqQuoKPI1U8AfjOE1RlLfGzy+dj0zsclXyUem/qIOOpp5DGXJEohqa23d5eYj01ffGx65+OSbzQdG99uMjOzXE4SZmaWy0niSC3lrkAF87HJ52PTOx+XfKPm2LhNwszMcvlKwszMcjlJmJlZLicJQNJiSS9Iape0vNz1KTdJd0naKen5glitpA2SXkrf08pZx3KQNFvSRklbJG2W9IUU97GRJkt6StK/pmNzc4rPlfRk+r/1A0nHlLuu5SBpvKRfSvpJmh81x6Xqk4Sk8cDtwKXAfOBaSfPLW6uyuxtY3CO2HHg0IuYBj6b5anMQ+MuImA8sBD6b/q342MAB4KKIOAM4E1gsaSFwK3BbRJwM7AGWlbGO5fQFYGvB/Kg5LlWfJIDzgPaIeDki3gHWAkvKXKeyiojHgd09wkuANWl6DXDliFaqAkTEaxHxizT9W7L/9DPxsSEyb6fZiekTwEXAAylelcdG0izgMuC7aV6MouPiJJH9J3+1YL4zxexIMyLitTT9OjCjnJUpN0kNwFnAk/jYAIdvqTwD7AQ2AL8G3oyIg6lItf7f+gbwZeBQmp/OKDouThI2aJH1m67avtOSpgA/Av4iIt4qXFbNxyYifhcRZ5KNRX8ecGqZq1R2ki4HdkbEpnLXpVgVPzLdCNgBzC6Yn5VidqQ3JJ0YEa9JOpHsr8WqI2kiWYJojYgHU9jHpkBEvClpI/ARYKqkCemv5mr8v3U+cIWkjwGTgfcD32QUHRdfScDTwLzU2+AY4BpgXZnrVInWAUvT9FLgoTLWpSzSveQ7ga0R8fcFi3xspDpJU9P0+4A/IWuz2Qh8PBWrumMTETdFxKyIaCA7tzwWEU2MouPiJ66BlOW/AYwH7oqIVWWuUllJ+j5wAdnrjN8AVgI/Bu4H5pC9iv0TEdGzcXtMk/TvgZ8Bz/He/eW/JmuXqPZjczpZA+x4sj8+74+IWySdRNYZpBb4JfDnEXGgfDUtH0kXAP8lIi4fTcfFScLMzHL5dpOZmeVykjAzs1xOEmZmlstJwszMcjlJmJlZLicJMzPL5SRhZma5/j9ENf+exPYoqgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Gw_1CmkaEkRL","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":88},"outputId":"7d4e32f1-241a-4f24-c31a-ec03321bdaaa","executionInfo":{"status":"ok","timestamp":1589793452131,"user_tz":-360,"elapsed":10312,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["frame = pd.DataFrame(scaled)\n","frame['cluster'] = labels\n","frame['cluster'].value_counts()"],"execution_count":110,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    9\n","1    7\n","2    6\n","Name: cluster, dtype: int64"]},"metadata":{"tags":[]},"execution_count":110}]},{"cell_type":"markdown","metadata":{"id":"dr5vxNUR7qQg","colab_type":"text"},"source":[""]},{"cell_type":"code","metadata":{"id":"qUvAnydrr8lJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":363},"outputId":"d5c06b49-6947-4da3-9bf7-aa7d22e9d8d5","executionInfo":{"status":"error","timestamp":1589791010262,"user_tz":-360,"elapsed":2519,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["df1 = scaled[scaled.cluster==0]\n","df2 = df[df.cluster==1]\n","df3 = df[df.cluster==2]\n","plt.scatter(df1.Age,df1['Income($)'],color='green')\n","plt.scatter(df2.Age,df2['Income($)'],color='red')\n","plt.scatter(df3.Age,df3['Income($)'],color='black')\n","plt.scatter(k_means.cluster_centers_[:,0],k_means.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n","plt.legend()"],"execution_count":74,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-74-a2e9c08b87a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Income($)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Income($)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'red'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5272\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5273\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'cluster'"]}]},{"cell_type":"code","metadata":{"id":"gBkHvoU3sZb1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":52},"outputId":"85b01616-a087-4b84-9c9d-60f230da4abc","executionInfo":{"status":"ok","timestamp":1589790351246,"user_tz":-360,"elapsed":1173,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["km = KMeans(n_clusters=3)\n","y_predicted = km.fit_predict(df[['Age','Income($)']])\n","y_predicted"],"execution_count":56,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2],\n","      dtype=int32)"]},"metadata":{"tags":[]},"execution_count":56}]},{"cell_type":"code","metadata":{"id":"4ntHlqQb45qC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":200},"outputId":"c1cc55af-6627-4baf-b52a-b7a4d18ecbbf","executionInfo":{"status":"ok","timestamp":1589790363802,"user_tz":-360,"elapsed":1110,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["df['cluster']=y_predicted\n","df.head()"],"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Name</th>\n","      <th>Age</th>\n","      <th>Income($)</th>\n","      <th>ClusterID</th>\n","      <th>cluster</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Rob</td>\n","      <td>27</td>\n","      <td>70000</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Michael</td>\n","      <td>29</td>\n","      <td>90000</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Mohan</td>\n","      <td>29</td>\n","      <td>61000</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Ismail</td>\n","      <td>28</td>\n","      <td>60000</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Kory</td>\n","      <td>42</td>\n","      <td>150000</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      Name  Age  Income($)  ClusterID  cluster\n","0      Rob   27      70000          1        0\n","1  Michael   29      90000          1        0\n","2    Mohan   29      61000          1        2\n","3   Ismail   28      60000          1        2\n","4     Kory   42     150000          2        1"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"_bH0HpMB48vx","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":70},"outputId":"afbe92d7-6c26-4eb8-ac1e-0b2c18750670","executionInfo":{"status":"ok","timestamp":1589790375376,"user_tz":-360,"elapsed":1162,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["km.cluster_centers_"],"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[3.40000000e+01, 8.05000000e+04],\n","       [3.82857143e+01, 1.50000000e+05],\n","       [3.29090909e+01, 5.61363636e+04]])"]},"metadata":{"tags":[]},"execution_count":58}]},{"cell_type":"code","metadata":{"id":"ozNd5bVo4_jc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":283},"outputId":"d168d83f-e854-4c93-d0c4-59e7a35721f6","executionInfo":{"status":"ok","timestamp":1589790390546,"user_tz":-360,"elapsed":2549,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["df1 = df[df.cluster==0]\n","df2 = df[df.cluster==1]\n","df3 = df[df.cluster==2]\n","plt.scatter(df1.Age,df1['Income($)'],color='green')\n","plt.scatter(df2.Age,df2['Income($)'],color='red')\n","plt.scatter(df3.Age,df3['Income($)'],color='black')\n","plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='*',label='centroid')\n","plt.legend()"],"execution_count":59,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f41af32f198>"]},"metadata":{"tags":[]},"execution_count":59},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"-COtEPpm5Cwg","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}