{"cells":[{"cell_type":"code","source":"# librerias\nfrom numpy import mean\nfrom numpy import std\nfrom pandas import read_csv\nfrom sklearn.model_selection import LeaveOneOut\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import RandomForestClassifier","metadata":{"id":"kO15uZdopvtz","cell_id":"82e0d29c90624ac693a187c6ef7ea1ec","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1586,"user_tz":240,"timestamp":1652617056366},"deepnote_cell_type":"code"},"outputs":[],"execution_count":7},{"cell_type":"markdown","source":"**Descripcion de datos**\nhttps://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.names\n\n**Enlace con datos**\nhttps://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv\n\n1. CRIM      per capita crime rate by town\n2. ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n3. INDUS proportion of non-retail business acres per town\n4. CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n5. NOX nitric oxides concentration (parts per 10 million)\n6. RM average number of rooms per dwelling\n7. AGE proportion of owner-occupied units built prior to 1940\n8. DIS weighted distances to five Boston employment centres\n9. RAD index of accessibility to radial highways\n10. TAX full-value property-tax rate per 10,000\n11. PTRATIO pupil teacher ratio by town\n12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n13. LSTAT % lower status of the population\n14. MEDV Median value of owner-occupied homes in $1000's","metadata":{"id":"_mKNx3NeYutA","cell_id":"b5f263f1954c4799a28130c4a27608a0","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"# librerias\nfrom pandas import read_csv\n# cargar datos\nurl = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'\ndataframe = read_csv(url, header=None)\n# shape\nprint(dataframe.shape)","metadata":{"id":"u_0oIqz5ZVZb","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"b8193cbf10e849f1b0030671e5b5ec8b","outputId":"a040a7d8-314b-4e1e-8dea-71a81c436dcc","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":968,"user_tz":240,"timestamp":1652616992857},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"(506, 14)\n"}],"execution_count":1},{"cell_type":"code","source":"dataframe","metadata":{"id":"LE0y7f4PZdHk","colab":{"height":423,"base_uri":"https://localhost:8080/"},"cell_id":"d3cf352430c7496d9b77fedfaca0458b","outputId":"d19bf9d4-f4fd-4206-86ed-07f98e5173fe","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":355,"user_tz":240,"timestamp":1652616994831},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"          0     1      2   3      4      5     6       7   8      9     10  \\\n0    0.00632  18.0   2.31   0  0.538  6.575  65.2  4.0900   1  296.0  15.3   \n1    0.02731   0.0   7.07   0  0.469  6.421  78.9  4.9671   2  242.0  17.8   \n2    0.02729   0.0   7.07   0  0.469  7.185  61.1  4.9671   2  242.0  17.8   \n3    0.03237   0.0   2.18   0  0.458  6.998  45.8  6.0622   3  222.0  18.7   \n4    0.06905   0.0   2.18   0  0.458  7.147  54.2  6.0622   3  222.0  18.7   \n..       ...   ...    ...  ..    ...    ...   ...     ...  ..    ...   ...   \n501  0.06263   0.0  11.93   0  0.573  6.593  69.1  2.4786   1  273.0  21.0   \n502  0.04527   0.0  11.93   0  0.573  6.120  76.7  2.2875   1  273.0  21.0   \n503  0.06076   0.0  11.93   0  0.573  6.976  91.0  2.1675   1  273.0  21.0   \n504  0.10959   0.0  11.93   0  0.573  6.794  89.3  2.3889   1  273.0  21.0   \n505  0.04741   0.0  11.93   0  0.573  6.030  80.8  2.5050   1  273.0  21.0   \n\n         11    12    13  \n0    396.90  4.98  24.0  \n1    396.90  9.14  21.6  \n2    392.83  4.03  34.7  \n3    394.63  2.94  33.4  \n4    396.90  5.33  36.2  \n..      ...   ...   ...  \n501  391.99  9.67  22.4  \n502  396.90  9.08  20.6  \n503  396.90  5.64  23.9  \n504  393.45  6.48  22.0  \n505  396.90  7.88  11.9  \n\n[506 rows x 14 columns]","text/html":"\n  <div id=\"df-8b4f7cba-36eb-42e8-b86a-f4002699cb4a\">\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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n      <td>21.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n      <td>33.4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n      <td>36.2</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>501</th>\n      <td>0.06263</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0</td>\n      <td>0.573</td>\n      <td>6.593</td>\n      <td>69.1</td>\n      <td>2.4786</td>\n      <td>1</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>391.99</td>\n      <td>9.67</td>\n      <td>22.4</td>\n    </tr>\n    <tr>\n      <th>502</th>\n      <td>0.04527</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0</td>\n      <td>0.573</td>\n      <td>6.120</td>\n      <td>76.7</td>\n      <td>2.2875</td>\n      <td>1</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>396.90</td>\n      <td>9.08</td>\n      <td>20.6</td>\n    </tr>\n    <tr>\n      <th>503</th>\n      <td>0.06076</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0</td>\n      <td>0.573</td>\n      <td>6.976</td>\n      <td>91.0</td>\n      <td>2.1675</td>\n      <td>1</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>396.90</td>\n      <td>5.64</td>\n      <td>23.9</td>\n    </tr>\n    <tr>\n      <th>504</th>\n      <td>0.10959</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0</td>\n      <td>0.573</td>\n      <td>6.794</td>\n      <td>89.3</td>\n      <td>2.3889</td>\n      <td>1</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>393.45</td>\n      <td>6.48</td>\n      <td>22.0</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <td>0.04741</td>\n      <td>0.0</td>\n      <td>11.93</td>\n      <td>0</td>\n      <td>0.573</td>\n      <td>6.030</td>\n      <td>80.8</td>\n      <td>2.5050</td>\n      <td>1</td>\n      <td>273.0</td>\n      <td>21.0</td>\n      <td>396.90</td>\n      <td>7.88</td>\n      <td>11.9</td>\n    </tr>\n  </tbody>\n</table>\n<p>506 rows × 14 columns</p>\n</div>\n      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b4f7cba-36eb-42e8-b86a-f4002699cb4a')\"\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-8b4f7cba-36eb-42e8-b86a-f4002699cb4a 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-8b4f7cba-36eb-42e8-b86a-f4002699cb4a');\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":2}],"execution_count":2},{"cell_type":"code","source":"dataframe.dtypes","metadata":{"id":"BAOnINNWbxhV","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"e4dd56061579419eb3b23f6c1f3dbd24","outputId":"7637266f-7d5e-4db6-947c-a672c042a37e","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":240,"timestamp":1652616995873},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0     float64\n1     float64\n2     float64\n3       int64\n4     float64\n5     float64\n6     float64\n7     float64\n8       int64\n9     float64\n10    float64\n11    float64\n12    float64\n13    float64\ndtype: object"},"metadata":{},"execution_count":3}],"execution_count":3},{"cell_type":"code","source":"dataframe.isnull().sum()","metadata":{"id":"0jTYRZU-Zd6J","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"4de5280f6f12494da8c49cb4bcefa023","outputId":"5179df6f-01a9-4018-9cda-d0ebc76dd30b","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":4,"user_tz":240,"timestamp":1652616997142},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"0     0\n1     0\n2     0\n3     0\n4     0\n5     0\n6     0\n7     0\n8     0\n9     0\n10    0\n11    0\n12    0\n13    0\ndtype: int64"},"metadata":{},"execution_count":4}],"execution_count":4},{"cell_type":"code","source":"# separar en X y y\ndata= dataframe.values\nX, y = data[:, :-1], data[:, -1]\nprint(X.shape, y.shape)","metadata":{"id":"COZGHu1YZf9Y","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"d647c79fc67647579da43fe7a82227c8","outputId":"7f645398-cbd3-4c2b-a99b-9be68933873d","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":7,"user_tz":240,"timestamp":1652616998405},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"(506, 13) (506,)\n"}],"execution_count":5},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import RandomForestRegressor\n# crear el modelo\nmodel = RandomForestRegressor(random_state=42, n_estimators=10,max_depth=4)\nscores = cross_val_score(model, X, y, cv=7, verbose=1,scoring='r2')","metadata":{"id":"oxihow3SqgYI","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"c045e3d459ea4738923f6d5359235434","outputId":"eeb97737-f3ab-42d5-ed77-8bb4626a84ff","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":702,"user_tz":240,"timestamp":1652617397725},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.3s finished\n"}],"execution_count":14},{"cell_type":"code","source":"print(\"%0.2f de r2 promedio con una desviacion estandar de %0.2f\" % (scores.mean(), scores.std()))","metadata":{"id":"g3CRDsNTqscO","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"ba81d4ca8cd3472c8c430ce14a49318b","outputId":"b133c5b5-bc74-4ac0-827d-8a5cf5c2c7df","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":334,"user_tz":240,"timestamp":1652617420786},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":"0.52 de r2 promedio con una desviacion estandar de 0.26\n"}],"execution_count":16},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=4f9037d8-f2e9-4683-9ce3-67576f784ec7' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyB3aWR0aD0iODBweCIgaGVpZ2h0PSI4MHB4IiB2aWV3Qm94PSIwIDAgODAgODAiIHZlcnNpb249IjEuMSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIiB4bWxuczp4bGluaz0iaHR0cDovL3d3dy53My5vcmcvMTk5OS94bGluayI+CiAgICA8IS0tIEdlbmVyYXRvcjogU2tldGNoIDU0LjEgKDc2NDkwKSAtIGh0dHBzOi8vc2tldGNoYXBwLmNvbSAtLT4KICAgIDx0aXRsZT5Hcm91cCAzPC90aXRsZT4KICAgIDxkZXNjPkNyZWF0ZWQgd2l0aCBTa2V0Y2guPC9kZXNjPgogICAgPGcgaWQ9IkxhbmRpbmciIHN0cm9rZT0ibm9uZSIgc3Ryb2tlLXdpZHRoPSIxIiBmaWxsPSJub25lIiBmaWxsLXJ1bGU9ImV2ZW5vZGQiPgogICAgICAgIDxnIGlkPSJBcnRib2FyZCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoLTEyMzUuMDAwMDAwLCAtNzkuMDAwMDAwKSI+CiAgICAgICAgICAgIDxnIGlkPSJHcm91cC0zIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSgxMjM1LjAwMDAwMCwgNzkuMDAwMDAwKSI+CiAgICAgICAgICAgICAgICA8cG9seWdvbiBpZD0iUGF0aC0yMCIgZmlsbD0iIzAyNjVCNCIgcG9pbnRzPSIyLjM3NjIzNzYyIDgwIDM4LjA0NzY2NjcgODAgNTcuODIxNzgyMiA3My44MDU3NTkyIDU3LjgyMTc4MjIgMzIuNzU5MjczOSAzOS4xNDAyMjc4IDMxLjY4MzE2ODMiPjwvcG9seWdvbj4KICAgICAgICAgICAgICAgIDxwYXRoIGQ9Ik0zNS4wMDc3MTgsODAgQzQyLjkwNjIwMDcsNzYuNDU0OTM1OCA0Ny41NjQ5MTY3LDcxLjU0MjI2NzEgNDguOTgzODY2LDY1LjI2MTk5MzkgQzUxLjExMjI4OTksNTUuODQxNTg0MiA0MS42NzcxNzk1LDQ5LjIxMjIyODQgMjUuNjIzOTg0Niw0OS4yMTIyMjg0IEMyNS40ODQ5Mjg5LDQ5LjEyNjg0NDggMjkuODI2MTI5Niw0My4yODM4MjQ4IDM4LjY0NzU4NjksMzEuNjgzMTY4MyBMNzIuODcxMjg3MSwzMi41NTQ0MjUgTDY1LjI4MDk3Myw2Ny42NzYzNDIxIEw1MS4xMTIyODk5LDc3LjM3NjE0NCBMMzUuMDA3NzE4LDgwIFoiIGlkPSJQYXRoLTIyIiBmaWxsPSIjMDAyODY4Ij48L3BhdGg+CiAgICAgICAgICAgICAgICA8cGF0aCBkPSJNMCwzNy43MzA0NDA1IEwyNy4xMTQ1MzcsMC4yNTcxMTE0MzYgQzYyLjM3MTUxMjMsLTEuOTkwNzE3MDEgODAsMTAuNTAwMzkyNyA4MCwzNy43MzA0NDA1IEM4MCw2NC45NjA0ODgyIDY0Ljc3NjUwMzgsNzkuMDUwMzQxNCAzNC4zMjk1MTEzLDgwIEM0Ny4wNTUzNDg5LDc3LjU2NzA4MDggNTMuNDE4MjY3Nyw3MC4zMTM2MTAzIDUzLjQxODI2NzcsNTguMjM5NTg4NSBDNTMuNDE4MjY3Nyw0MC4xMjg1NTU3IDM2LjMwMzk1NDQsMzcuNzMwNDQwNSAyNS4yMjc0MTcsMzcuNzMwNDQwNSBDMTcuODQzMDU4NiwzNy43MzA0NDA1IDkuNDMzOTE5NjYsMzcuNzMwNDQwNSAwLDM3LjczMDQ0MDUgWiIgaWQ9IlBhdGgtMTkiIGZpbGw9IiMzNzkzRUYiPjwvcGF0aD4KICAgICAgICAgICAgPC9nPgogICAgICAgIDwvZz4KICAgIDwvZz4KPC9zdmc+' 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