{"cells":[{"cell_type":"code","source":"from google.colab import drive\nimport os\ndrive.mount('/content/gdrive')\n# Establecer ruta de acceso en dr\nimport os\nprint(os.getcwd())\nos.chdir(\"/content/gdrive/My Drive\")","metadata":{"id":"nR7uNvK-Ar47","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"340099fff1564420954cdf63efb5917e","outputId":"63bfc7be-7a1f-45e4-c0a8-390c5186927c","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":2518,"user_tz":240,"timestamp":1650828276465},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n/content/gdrive/My Drive\n"}],"execution_count":6},{"cell_type":"code","source":"# K-Means Clustering\n\n# Importacion de librerias\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Carga del conjunto de datos\ndataset = pd.read_csv(\"Mall_Customers.csv\", sep = \",\")\ndataset.head()","metadata":{"id":"kwFbLrasAbZX","colab":{"height":207,"base_uri":"https://localhost:8080/"},"cell_id":"69d952b490404e37a2315b7c2cdeab95","outputId":"5b935869-d1bc-4b2d-e848-e6ed70b49b3e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":488,"user_tz":240,"timestamp":1650828277963},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n0           1    Male   19                  15                      39\n1           2    Male   21                  15                      81\n2           3  Female   20                  16                       6\n3           4  Female   23                  16                      77\n4           5  Female   31                  17                      40","text/html":"\n  <div id=\"df-d2cf1ed8-1596-4df2-a4e4-e891cf93125b\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CustomerID</th>\n      <th>Genre</th>\n      <th>Age</th>\n      <th>Annual Income (k$)</th>\n      <th>Spending Score (1-100)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Male</td>\n      <td>19</td>\n      <td>15</td>\n      <td>39</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>15</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Female</td>\n      <td>20</td>\n      <td>16</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Female</td>\n      <td>23</td>\n      <td>16</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Female</td>\n      <td>31</td>\n      <td>17</td>\n      <td>40</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d2cf1ed8-1596-4df2-a4e4-e891cf93125b')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path 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background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-d2cf1ed8-1596-4df2-a4e4-e891cf93125b button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-d2cf1ed8-1596-4df2-a4e4-e891cf93125b');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\n          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":7}],"execution_count":7},{"cell_type":"code","source":"#Generamos nuestra x\nX = dataset.iloc[:, [3, 4]].values","metadata":{"id":"G-eJ02osAbZb","cell_id":"07d8203baab4439e9da398916d4dcf54","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":387,"user_tz":240,"timestamp":1650828280931},"deepnote_cell_type":"code"},"outputs":[],"execution_count":8},{"cell_type":"code","source":"X","metadata":{"id":"oFLYeMuv35iz","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"8539ebcd6aa948b9b4a882a3b7fd9471","outputId":"5d2d5d89-ef78-42fc-9e3c-188766e20351","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":414,"user_tz":240,"timestamp":1650828281725},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[ 15,  39],\n       [ 15,  81],\n       [ 16,   6],\n       [ 16,  77],\n       [ 17,  40],\n       [ 17,  76],\n       [ 18,   6],\n       [ 18,  94],\n       [ 19,   3],\n       [ 19,  72],\n       [ 19,  14],\n       [ 19,  99],\n       [ 20,  15],\n       [ 20,  77],\n       [ 20,  13],\n       [ 20,  79],\n       [ 21,  35],\n       [ 21,  66],\n       [ 23,  29],\n       [ 23,  98],\n       [ 24,  35],\n       [ 24,  73],\n       [ 25,   5],\n       [ 25,  73],\n       [ 28,  14],\n       [ 28,  82],\n       [ 28,  32],\n       [ 28,  61],\n       [ 29,  31],\n       [ 29,  87],\n       [ 30,   4],\n       [ 30,  73],\n       [ 33,   4],\n       [ 33,  92],\n       [ 33,  14],\n       [ 33,  81],\n       [ 34,  17],\n       [ 34,  73],\n       [ 37,  26],\n       [ 37,  75],\n       [ 38,  35],\n       [ 38,  92],\n       [ 39,  36],\n       [ 39,  61],\n       [ 39,  28],\n       [ 39,  65],\n       [ 40,  55],\n       [ 40,  47],\n       [ 40,  42],\n       [ 40,  42],\n       [ 42,  52],\n       [ 42,  60],\n       [ 43,  54],\n       [ 43,  60],\n       [ 43,  45],\n       [ 43,  41],\n       [ 44,  50],\n       [ 44,  46],\n       [ 46,  51],\n       [ 46,  46],\n       [ 46,  56],\n       [ 46,  55],\n       [ 47,  52],\n       [ 47,  59],\n       [ 48,  51],\n       [ 48,  59],\n       [ 48,  50],\n       [ 48,  48],\n       [ 48,  59],\n       [ 48,  47],\n       [ 49,  55],\n       [ 49,  42],\n       [ 50,  49],\n       [ 50,  56],\n       [ 54,  47],\n       [ 54,  54],\n       [ 54,  53],\n       [ 54,  48],\n       [ 54,  52],\n       [ 54,  42],\n       [ 54,  51],\n       [ 54,  55],\n       [ 54,  41],\n       [ 54,  44],\n       [ 54,  57],\n       [ 54,  46],\n       [ 57,  58],\n       [ 57,  55],\n       [ 58,  60],\n       [ 58,  46],\n       [ 59,  55],\n       [ 59,  41],\n       [ 60,  49],\n       [ 60,  40],\n       [ 60,  42],\n       [ 60,  52],\n       [ 60,  47],\n       [ 60,  50],\n       [ 61,  42],\n       [ 61,  49],\n       [ 62,  41],\n       [ 62,  48],\n       [ 62,  59],\n       [ 62,  55],\n       [ 62,  56],\n       [ 62,  42],\n       [ 63,  50],\n       [ 63,  46],\n       [ 63,  43],\n       [ 63,  48],\n       [ 63,  52],\n       [ 63,  54],\n       [ 64,  42],\n       [ 64,  46],\n       [ 65,  48],\n       [ 65,  50],\n       [ 65,  43],\n       [ 65,  59],\n       [ 67,  43],\n       [ 67,  57],\n       [ 67,  56],\n       [ 67,  40],\n       [ 69,  58],\n       [ 69,  91],\n       [ 70,  29],\n       [ 70,  77],\n       [ 71,  35],\n       [ 71,  95],\n       [ 71,  11],\n       [ 71,  75],\n       [ 71,   9],\n       [ 71,  75],\n       [ 72,  34],\n       [ 72,  71],\n       [ 73,   5],\n       [ 73,  88],\n       [ 73,   7],\n       [ 73,  73],\n       [ 74,  10],\n       [ 74,  72],\n       [ 75,   5],\n       [ 75,  93],\n       [ 76,  40],\n       [ 76,  87],\n       [ 77,  12],\n       [ 77,  97],\n       [ 77,  36],\n       [ 77,  74],\n       [ 78,  22],\n       [ 78,  90],\n       [ 78,  17],\n       [ 78,  88],\n       [ 78,  20],\n       [ 78,  76],\n       [ 78,  16],\n       [ 78,  89],\n       [ 78,   1],\n       [ 78,  78],\n       [ 78,   1],\n       [ 78,  73],\n       [ 79,  35],\n       [ 79,  83],\n       [ 81,   5],\n       [ 81,  93],\n       [ 85,  26],\n       [ 85,  75],\n       [ 86,  20],\n       [ 86,  95],\n       [ 87,  27],\n       [ 87,  63],\n       [ 87,  13],\n       [ 87,  75],\n       [ 87,  10],\n       [ 87,  92],\n       [ 88,  13],\n       [ 88,  86],\n       [ 88,  15],\n       [ 88,  69],\n       [ 93,  14],\n       [ 93,  90],\n       [ 97,  32],\n       [ 97,  86],\n       [ 98,  15],\n       [ 98,  88],\n       [ 99,  39],\n       [ 99,  97],\n       [101,  24],\n       [101,  68],\n       [103,  17],\n       [103,  85],\n       [103,  23],\n       [103,  69],\n       [113,   8],\n       [113,  91],\n       [120,  16],\n       [120,  79],\n       [126,  28],\n       [126,  74],\n       [137,  18],\n       [137,  83]])"},"metadata":{},"execution_count":9}],"execution_count":9},{"cell_type":"markdown","source":"Nos quedamos con las variables 3 y 4 de conjunto de datos, las cuales corresponden al ingreso anual en miles y la puntuación del cliente.","metadata":{"id":"hmz_9AZQAbZc","cell_id":"f3d97ebb3b8a44069cf249aa0f18dc5b","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"# Metodo del Codo para encontrar el numero optimo de clusters\nfrom sklearn.cluster import KMeans\nwcss = []\nfor i in range(1, 11):\n    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n    kmeans.fit(X)\n    wcss.append(kmeans.inertia_)\n\n# Grafica de la suma de las distancias\nplt.plot(range(1, 11), wcss)\nplt.title('The Elbow Method')\nplt.xlabel('Number of clusters')\nplt.ylabel('WCSS')\nplt.show()","metadata":{"id":"bO4ZgQGlAbZe","colab":{"height":295,"base_uri":"https://localhost:8080/"},"cell_id":"2639f0fe5a374f81a56df3c2b270854d","outputId":"6e74a49a-6e94-45ac-caaa-d347e5e70d49","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4515,"user_tz":240,"timestamp":1650828288691},"deepnote_cell_type":"code"},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}],"execution_count":10},{"cell_type":"markdown","source":"En el bloque anterior, generamos los clusters para valores de 1 a 10 (en el rango de 1 a 11) y obtenemos para cada uno de ellos, la suma de las distancias con el tributo inertia_ del objeto kmeans. En la gráfica observamos que la disminución en la suma de las distancias se atenúa cuando el número de clusters es igual a 5, por lo que, para este caso práctico, el número óptimo de clusters será de 5.\n\n","metadata":{"id":"yUAs_LIoAbZf","cell_id":"9d7efcd946914bd09d71bd823b10835f","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"# Creando el k-Means para los 5 grupos encontrados\nkmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42) #k-means++ acelera la convergencia, respecto de random\ny_kmeans = kmeans.fit_predict(X)","metadata":{"id":"SV6wYYkSAbZg","cell_id":"f416550134ec45d09d8a8a5665ee7eca","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":686,"user_tz":240,"timestamp":1650828290534},"deepnote_cell_type":"code"},"outputs":[],"execution_count":11},{"cell_type":"code","source":"y_kmeans","metadata":{"id":"5OFHBCXd5BUB","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"a1fe1f3bec59442cbdfc9c282669cebf","outputId":"df85b585-96bf-4371-d395-baf4e0731fdb","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":6,"user_tz":240,"timestamp":1650828291220},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 0,\n       2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 4, 0, 4, 1, 4, 1, 4,\n       0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 0, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n       1, 4], dtype=int32)"},"metadata":{},"execution_count":12}],"execution_count":12},{"cell_type":"code","source":"dataset['pred']=y_kmeans\ndataset","metadata":{"id":"sbI-pTMa5iAA","colab":{"height":424,"base_uri":"https://localhost:8080/"},"cell_id":"b68204e54994439081ccd47755381f1d","outputId":"179efd57-6a32-45d4-e521-956ec19dd95b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":17,"user_tz":240,"timestamp":1650828292430},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"     CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)  pred\n0             1    Male   19                  15                      39     2\n1             2    Male   21                  15                      81     3\n2             3  Female   20                  16                       6     2\n3             4  Female   23                  16                      77     3\n4             5  Female   31                  17                      40     2\n..          ...     ...  ...                 ...                     ...   ...\n195         196  Female   35                 120                      79     4\n196         197  Female   45                 126                      28     1\n197         198    Male   32                 126                      74     4\n198         199    Male   32                 137                      18     1\n199         200    Male   30                 137                      83     4\n\n[200 rows x 6 columns]","text/html":"\n  <div id=\"df-04c6da76-9728-41d9-8eb7-c2feca02117d\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CustomerID</th>\n      <th>Genre</th>\n      <th>Age</th>\n      <th>Annual Income (k$)</th>\n      <th>Spending Score (1-100)</th>\n      <th>pred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Male</td>\n      <td>19</td>\n      <td>15</td>\n      <td>39</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>15</td>\n      <td>81</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Female</td>\n      <td>20</td>\n      <td>16</td>\n      <td>6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Female</td>\n      <td>23</td>\n      <td>16</td>\n      <td>77</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Female</td>\n      <td>31</td>\n      <td>17</td>\n      <td>40</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>195</th>\n      <td>196</td>\n      <td>Female</td>\n      <td>35</td>\n      <td>120</td>\n      <td>79</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>197</td>\n      <td>Female</td>\n      <td>45</td>\n      <td>126</td>\n      <td>28</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>198</td>\n      <td>Male</td>\n      <td>32</td>\n      <td>126</td>\n      <td>74</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>199</td>\n      <td>Male</td>\n      <td>32</td>\n      <td>137</td>\n      <td>18</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>200</td>\n      <td>Male</td>\n      <td>30</td>\n      <td>137</td>\n      <td>83</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>200 rows × 6 columns</p>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04c6da76-9728-41d9-8eb7-c2feca02117d')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":13}],"execution_count":13},{"cell_type":"code","source":"dataset.groupby(['pred']).mean()","metadata":{"id":"xK4J9mxx5mGE","colab":{"height":238,"base_uri":"https://localhost:8080/"},"cell_id":"58dc149898e1438da13f50b1712eb409","outputId":"58a74e30-8f87-46da-852b-2ca1dea9727d","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":11,"user_tz":240,"timestamp":1650828294254},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"      CustomerID        Age  Annual Income (k$)  Spending Score (1-100)\npred                                                                   \n0      86.320988  42.716049           55.296296               49.518519\n1     164.371429  41.114286           88.200000               17.114286\n2      23.000000  45.217391           26.304348               20.913043\n3      23.090909  25.272727           25.727273               79.363636\n4     162.000000  32.692308           86.538462               82.128205","text/html":"\n  <div id=\"df-1d12af37-dd1b-41b5-a37b-05710eee3756\">\n    <div class=\"colab-df-container\">\n      <div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CustomerID</th>\n      <th>Age</th>\n      <th>Annual Income (k$)</th>\n      <th>Spending Score (1-100)</th>\n    </tr>\n    <tr>\n      <th>pred</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>86.320988</td>\n      <td>42.716049</td>\n      <td>55.296296</td>\n      <td>49.518519</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>164.371429</td>\n      <td>41.114286</td>\n      <td>88.200000</td>\n      <td>17.114286</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>23.000000</td>\n      <td>45.217391</td>\n      <td>26.304348</td>\n      <td>20.913043</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23.090909</td>\n      <td>25.272727</td>\n      <td>25.727273</td>\n      <td>79.363636</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>162.000000</td>\n      <td>32.692308</td>\n      <td>86.538462</td>\n      <td>82.128205</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d12af37-dd1b-41b5-a37b-05710eee3756')\"\n              title=\"Convert this dataframe to an interactive table.\"\n              style=\"display:none;\">\n        \n  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n       width=\"24px\">\n    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n  </svg>\n      </button>\n      \n  <style>\n    .colab-df-container {\n      display:flex;\n      flex-wrap:wrap;\n      gap: 12px;\n    }\n\n    .colab-df-convert {\n      background-color: #E8F0FE;\n      border: none;\n      border-radius: 50%;\n      cursor: pointer;\n      display: none;\n      fill: #1967D2;\n      height: 32px;\n      padding: 0 0 0 0;\n      width: 32px;\n    }\n\n    .colab-df-convert:hover {\n      background-color: #E2EBFA;\n      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n      fill: #174EA6;\n    }\n\n    [theme=dark] .colab-df-convert {\n      background-color: #3B4455;\n      fill: #D2E3FC;\n    }\n\n    [theme=dark] .colab-df-convert:hover {\n      background-color: #434B5C;\n      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n      fill: #FFFFFF;\n    }\n  </style>\n\n      <script>\n        const buttonEl =\n          document.querySelector('#df-1d12af37-dd1b-41b5-a37b-05710eee3756 button.colab-df-convert');\n        buttonEl.style.display =\n          google.colab.kernel.accessAllowed ? 'block' : 'none';\n\n        async function convertToInteractive(key) {\n          const element = document.querySelector('#df-1d12af37-dd1b-41b5-a37b-05710eee3756');\n          const dataTable =\n            await google.colab.kernel.invokeFunction('convertToInteractive',\n                                                     [key], {});\n          if (!dataTable) return;\n\n          const docLinkHtml = 'Like what you see? Visit the ' +\n            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n            + ' to learn more about interactive tables.';\n          element.innerHTML = '';\n          dataTable['output_type'] = 'display_data';\n          await google.colab.output.renderOutput(dataTable, element);\n          const docLink = document.createElement('div');\n          docLink.innerHTML = docLinkHtml;\n          element.appendChild(docLink);\n        }\n      </script>\n    </div>\n  </div>\n  "},"metadata":{},"execution_count":14}],"execution_count":14},{"cell_type":"markdown","source":"Para poder observar gráficamente la asignación de los 200 clientes a 5 grupos o clusters realizamos lo siguiente, le asignamos un color a cada grupo y marcamos los centroides en amarillo:","metadata":{"id":"LYkwuK4hAbZg","cell_id":"5cdcc2538fc943ae9519a5b4a0be3f60","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"# Visualizacion grafica de los clusters\nplt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\nplt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\nplt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\nplt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\nplt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n\nplt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n\nplt.title('Clusters of customers')\nplt.xlabel('Annual Income (k$)')\nplt.ylabel('Spending Score (1-100)')\nplt.legend()\nplt.show()","metadata":{"id":"MRaMWK8PAbZh","colab":{"height":295,"base_uri":"https://localhost:8080/"},"cell_id":"cac6345910944253bdddf2e8516d1f3b","outputId":"a7534b1c-936a-4668-a1c0-c39c42bc513b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":562,"user_tz":240,"timestamp":1650828296268},"deepnote_cell_type":"code"},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}],"execution_count":15},{"cell_type":"markdown","source":"<a 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