0_ml_exercise_preprocess.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1h9xaezG1QuPDGzBbJh7k0VtwGWZcUXMe","timestamp":1713751789570}],"authorship_tag":"ABX9TyMeiYKDfS1FcJDDlxYN7trX"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LFAOVuhcAtqa","executionInfo":{"status":"ok","timestamp":1714748009779,"user_tz":-120,"elapsed":53422,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"933e4e63-76ea-4a00-c58d-fd68b2aeab28"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n","/content/gdrive/MyDrive/University/machine_learning/old_ml/exercise\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","%cd gdrive/MyDrive/University/machine_learning/old_ml/exercise"]},{"cell_type":"code","source":["!ls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6aIkU7fml3-7","executionInfo":{"status":"ok","timestamp":1714748010287,"user_tz":-120,"elapsed":516,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"e72525d6-fa66-49cc-9b42-c1bf454afda6"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["0_ml_exercise_preprocess.ipynb\t1_ml_exercise_classical.ipynb  2_ml_exercise_neural.ipynb  data\n"]}]},{"cell_type":"code","source":["#imports for data exploration\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import seaborn as sns\n","\n","#imports for feature selection\n","import sklearn\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_selection import SelectKBest, chi2, RFE\n","from sklearn.ensemble import RandomForestClassifier\n","\n","np.random.seed(42)"],"metadata":{"id":"F6t7lUa2B0IC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["depress = pd.read_csv(\"data/depression.csv\", encoding=\"UTF-8\")"],"metadata":{"id":"-SJZ6bIqBW7V"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# 1 Preprocessing"],"metadata":{"id":"2GPxqV4QtrqP"}},{"cell_type":"markdown","source":["## 1.1 Data exploration\n","<a name=\"data_explore\"></a>"],"metadata":{"id":"-l0KuX8LzP7K"}},{"cell_type":"code","source":["depress.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":360},"id":"Bjn39sHSBt2E","executionInfo":{"status":"ok","timestamp":1714518384679,"user_tz":-120,"elapsed":14,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"9fbb6872-d66c-4bcc-c4dc-1dcfb6bdb1ef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  AGERNG  GENDER            EDU                      PROF     MARSTS RESDPL  \\\n","0  26-30  Female  Post Graduate                Unemployed  Unmarried   Town   \n","1  26-30    Male  Post Graduate  Service holder (Private)  Unmarried   City   \n","2  21-25    Male            HSC                   Student  Unmarried   City   \n","3  16-20    Male            HSC                   Student  Unmarried   City   \n","4  21-25    Male       Graduate                   Student  Unmarried   Town   \n","\n","        LIVWTH ENVSAT POSSAT FINSTR  ... ANXI DEPRI ABUSED CHEAT THREAT  \\\n","0  With Family    Yes    Yes     No  ...  Yes    No     No    No     No   \n","1  With Family    Yes     No    Yes  ...  Yes   Yes    Yes    No     No   \n","2  With Family    Yes    Yes     No  ...  Yes   Yes     No    No     No   \n","3  With Family     No    Yes     No  ...  Yes   Yes     No   Yes     No   \n","4  With Family     No    Yes    Yes  ...  Yes   Yes     No    No     No   \n","\n","  SUICIDE INFER CONFLICT LOST DEPRESSED  \n","0      No    No      Yes   No         0  \n","1      No   Yes       No   No         1  \n","2      No    No       No   No         0  \n","3      No    No       No   No         1  \n","4      No   Yes      Yes   No         1  \n","\n","[5 rows x 31 columns]"],"text/html":["\n","  <div id=\"df-6f911537-d616-46cd-807c-b5a37825e755\" 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>AGERNG</th>\n","      <th>GENDER</th>\n","      <th>EDU</th>\n","      <th>PROF</th>\n","      <th>MARSTS</th>\n","      <th>RESDPL</th>\n","      <th>LIVWTH</th>\n","      <th>ENVSAT</th>\n","      <th>POSSAT</th>\n","      <th>FINSTR</th>\n","      <th>...</th>\n","      <th>ANXI</th>\n","      <th>DEPRI</th>\n","      <th>ABUSED</th>\n","      <th>CHEAT</th>\n","      <th>THREAT</th>\n","      <th>SUICIDE</th>\n","      <th>INFER</th>\n","      <th>CONFLICT</th>\n","      <th>LOST</th>\n","      <th>DEPRESSED</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>26-30</td>\n","      <td>Female</td>\n","      <td>Post Graduate</td>\n","      <td>Unemployed</td>\n","      <td>Unmarried</td>\n","      <td>Town</td>\n","      <td>With Family</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>...</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>26-30</td>\n","      <td>Male</td>\n","      <td>Post Graduate</td>\n","      <td>Service holder (Private)</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>With Family</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>21-25</td>\n","      <td>Male</td>\n","      <td>HSC</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>With Family</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>16-20</td>\n","      <td>Male</td>\n","      <td>HSC</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>With Family</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>21-25</td>\n","      <td>Male</td>\n","      <td>Graduate</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>Town</td>\n","      <td>With Family</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>...</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 31 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f911537-d616-46cd-807c-b5a37825e755')\"\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 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    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Classical and neural machine learning algorithms covered in this course so far cannot work with textual data*, so it must be converted to numerical data; as Sklearn's encoders transform data into floats, I will use these instead of strings.\n","\n","There are a variety of ways to do this, but I will limit myself to techniques seen in the early ML classes (so no text vectorisation, for instance).\n","\n","*This assignment was given before we moved on to Natural Language Processing."],"metadata":{"id":"MBbCvceLt2kO"}},{"cell_type":"code","source":["depress = depress.replace({\"Yes\": 1.0, \"No\":0.0}).replace({\"With Family\": 1.0, \"Without Family\": 0.0}).replace({\"Female\":1.0, \"Male\":0.0})\n","depress[\"DEPRESSED\"] = depress[\"DEPRESSED\"].astype(float)\n","\n","depress.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"seJS09utR9cE","executionInfo":{"status":"ok","timestamp":1714518385142,"user_tz":-120,"elapsed":9,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"101e6537-80c0-4c18-8506-7f7c2f643fbf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 604 entries, 0 to 603\n","Data columns (total 31 columns):\n"," #   Column     Non-Null Count  Dtype  \n","---  ------     --------------  -----  \n"," 0   AGERNG     604 non-null    object \n"," 1   GENDER     604 non-null    float64\n"," 2   EDU        604 non-null    object \n"," 3   PROF       604 non-null    object \n"," 4   MARSTS     604 non-null    object \n"," 5   RESDPL     604 non-null    object \n"," 6   LIVWTH     604 non-null    float64\n"," 7   ENVSAT     604 non-null    float64\n"," 8   POSSAT     604 non-null    float64\n"," 9   FINSTR     604 non-null    float64\n"," 10  DEBT       604 non-null    float64\n"," 11  PHYEX      604 non-null    object \n"," 12  SMOKE      604 non-null    float64\n"," 13  DRINK      604 non-null    float64\n"," 14  ILLNESS    604 non-null    float64\n"," 15  PREMED     604 non-null    float64\n"," 16  EATDIS     604 non-null    float64\n"," 17  AVGSLP     604 non-null    object \n"," 18  INSOM      604 non-null    float64\n"," 19  TSSN       604 non-null    object \n"," 20  WRKPRE     604 non-null    object \n"," 21  ANXI       604 non-null    float64\n"," 22  DEPRI      604 non-null    float64\n"," 23  ABUSED     604 non-null    float64\n"," 24  CHEAT      604 non-null    float64\n"," 25  THREAT     604 non-null    float64\n"," 26  SUICIDE    604 non-null    float64\n"," 27  INFER      604 non-null    float64\n"," 28  CONFLICT   604 non-null    float64\n"," 29  LOST       604 non-null    float64\n"," 30  DEPRESSED  604 non-null    float64\n","dtypes: float64(22), object(9)\n","memory usage: 146.4+ KB\n"]}]},{"cell_type":"markdown","source":["Some of the features have an ordered relationship, so ordinally encoding these might be desirable. However, when we look at the feature values listed in [section 1.1](#data_explore), we see that the sequence of these values is disordered. Following the philosophy of ordinal encoding, where each subsequent number of the feature's category is given more weight, I will assign floats manually, in accordance to which category would logically put an individual more at risk of developing depression."],"metadata":{"id":"DHLhGWcjvNSj"}},{"cell_type":"code","source":["depress.EDU.replace({\"Post Graduate\": 0.0, \"Graduate\": 1.0, \"HSC\": 2.0, \"SSC\": 3.0}, inplace=True)\n","depress.PHYEX.replace({\"Regularly\": 0.0, \"Sometimes\": 1.0, \"Never\": 2.0}, inplace=True)\n","depress.AVGSLP.replace({\"More than 8 hours\": 0.0, \"8 hours\": 1.0, \"7 hours\": 2.0,\n","                  \"6 hours\": 3.0, \"5 hours\": 4.0, \"Below 5 hours\": 5.0}, inplace=True)\n","depress.TSSN.replace({\"Less than 2 hours\": 0.0, \"2-4 hours a day\": 1.0, \"5-7 hours a day\": 2.0,\n","                 \"8-10 hours a day\": 3.0, \"More than 10 hours a day\": 4.0}, inplace=True)\n","depress.WRKPRE.replace({\"No Pressure\": 0.0, \"Mild\": 1.0, \"Moderate\": 2.0, \"Severe\": 3.0}, inplace=True)\n","\n","depress.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":290},"id":"eeEJCndC9qkr","executionInfo":{"status":"ok","timestamp":1714518385142,"user_tz":-120,"elapsed":8,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"bac88852-ce84-48e1-b19b-c6e9a67c822d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  AGERNG  GENDER  EDU                      PROF     MARSTS RESDPL  LIVWTH  \\\n","0  26-30     1.0  0.0                Unemployed  Unmarried   Town     1.0   \n","1  26-30     0.0  0.0  Service holder (Private)  Unmarried   City     1.0   \n","2  21-25     0.0  2.0                   Student  Unmarried   City     1.0   \n","3  16-20     0.0  2.0                   Student  Unmarried   City     1.0   \n","4  21-25     0.0  1.0                   Student  Unmarried   Town     1.0   \n","\n","   ENVSAT  POSSAT  FINSTR  ...  ANXI  DEPRI  ABUSED  CHEAT  THREAT  SUICIDE  \\\n","0     1.0     1.0     0.0  ...   1.0    0.0     0.0    0.0     0.0      0.0   \n","1     1.0     0.0     1.0  ...   1.0    1.0     1.0    0.0     0.0      0.0   \n","2     1.0     1.0     0.0  ...   1.0    1.0     0.0    0.0     0.0      0.0   \n","3     0.0     1.0     0.0  ...   1.0    1.0     0.0    1.0     0.0      0.0   \n","4     0.0     1.0     1.0  ...   1.0    1.0     0.0    0.0     0.0      0.0   \n","\n","   INFER  CONFLICT  LOST  DEPRESSED  \n","0    0.0       1.0   0.0        0.0  \n","1    1.0       0.0   0.0        1.0  \n","2    0.0       0.0   0.0        0.0  \n","3    0.0       0.0   0.0        1.0  \n","4    1.0       1.0   0.0        1.0  \n","\n","[5 rows x 31 columns]"],"text/html":["\n","  <div id=\"df-bc5bf799-da95-42ef-800e-58211ea5c396\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    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  <td>Unmarried</td>\n","      <td>Town</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>26-30</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Service holder (Private)</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>21-25</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>16-20</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>21-25</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>Town</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 31 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc5bf799-da95-42ef-800e-58211ea5c396')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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Sweeveldt","userId":"07747401850940922729"}},"outputId":"467985a3-0024-412b-83f1-635abd99f9fa"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array(['16-20', '21-25', '26-30', '31-35', '36-40', '41-45', '46-50',\n","        '51-55', '56-60', '61+'], dtype=object),\n"," array(['Businessman', 'Other', 'Service holder (Government)',\n","        'Service holder (Private)', 'Student', 'Unemployed'], dtype=object),\n"," array(['Divorced', 'Married', 'Unmarried'], dtype=object),\n"," array(['City', 'Town', 'Village'], dtype=object)]"]},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["feat_labels = cat_encoder.categories_\n","\n","# flatten the array so we can use them as headers in the dataframe\n","feat_labels = np.hstack(feat_labels)"],"metadata":{"id":"UYFe4PMvU6BS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["enc_feat = pd.DataFrame(feat_arr, columns=feat_labels)"],"metadata":{"id":"g8bQjOSIXEFq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# create a new data frame with the encoded categories\n","depress_enc = pd.concat([enc_feat, depress], axis=1)\n","depress_enc.drop(depress_enc[[\"AGERNG\", \"PROF\", \"MARSTS\", \"RESDPL\"]], axis=1, inplace=True)\n","depress_enc.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":236},"id":"XPnBtB09X1w0","executionInfo":{"status":"ok","timestamp":1714518385143,"user_tz":-120,"elapsed":6,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"6b5f378a-276f-4dc5-a59d-73e2c137a1f5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   16-20  21-25  26-30  31-35  36-40  41-45  46-50  51-55  56-60  61+  ...  \\\n","0    0.0    0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0  0.0  ...   \n","1    0.0    0.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0  0.0  ...   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One is Sklearn's [SelectKBest](#selectkbest) SelectKBest feature selector, the other one is Sklearn's [RFE](#rfe). A third option, [Boruta](#boruta) was explored, but ultimately cut from this workflow."],"metadata":{"id":"fphi-afzxjcl"}},{"cell_type":"markdown","source":["###1.3.1 Boruta\n","<a name=\"boruta\"></a>"],"metadata":{"id":"WMKbW_sH5rOr"}},{"cell_type":"markdown","source":["Boruta works as a wrapper around a RandomForest algorithm and will iteratively select important features by eliminating those which it deems irrelevant. The features will be ranked based on importance (1 being important, with higher n indicating decreased relevance). Originally, I was going to use BorutaPy to perform another type of recursive feature elimination.\n","\n","However, Boruta is no longer compatible with modern versions of NumPy. One way to make it work is by downgrading NumPy, which is for me a non-negotiable. There's still a hacky way to make it work by manually changing the NumPy data types before fitting, to prevent the raising of an Attribute Error due to the incompatibility.\n","\n","Moreover, according to the research paper, the features selected by Boruta did not lead to the best performing models, with sklearn's SelectKBest being the superior method. While I will retain the code written to implement Boruta, I'm hashing it out and give preference to a different method by sklearn known as RFE (recursive feature elimination)."],"metadata":{"id":"im2hAyezyhm8"}},{"cell_type":"code","source":["# magic command to remove all text output\n","\n","#%%capture\n","#!pip install Boruta"],"metadata":{"id":"yZQjIi93zn7a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#from boruta import BorutaPy\n","\n","#forest = RandomForestClassifier(max_depth=5, random_state=42)\n","\n","#feat_names = np.array(X.columns)\n","#feat_select = BorutaPy(forest, n_estimators=\"auto\", random_state=42)\n","\n","#np.int = np.int32\n","#np.float = np.float64\n","#np.bool = np.bool_\n","\n","#feat_select.fit(X_train_select.to_numpy(), y_train_select.to_numpy())\n","\n","#X_filtered = feat_select.transform(X_train_select.to_numpy())\n","\n","#feat_ranks = list(zip(feat_names,\n","#                         feat_select.ranking_,\n","#                         feat_select.support_))\n","\n","#depress_rank = pd.DataFrame(feat_ranks, columns=[\"Feature\", \"Ranking\", \"Useful?\"])\n","#depress_rank.sort_values(\"Ranking\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"okzXkw11vHX2","executionInfo":{"status":"ok","timestamp":1713755679337,"user_tz":-120,"elapsed":26524,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"033cc20e-95cd-4622-f23f-4b0a05cf7d75"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        Feature  Ranking  Useful?\n","47                         LOST        1     True\n","26                       POSSAT        1     True\n","27                       FINSTR        1     True\n","37                         TSSN        1     True\n","25                       ENVSAT        1     True\n","39                         ANXI        1     True\n","46                     CONFLICT        1     True\n","41                       ABUSED        1     True\n","42                        CHEAT        1     True\n","43                       THREAT        1     True\n","45                        INFER        1     True\n","40                        DEPRI        1     True\n","38                       WRKPRE        2    False\n","36                        INSOM        3    False\n","44                      SUICIDE        4    False\n","35                       AVGSLP        5    False\n","28                         DEBT        6    False\n","29                        PHYEX        7    False\n","23                          EDU        8    False\n","21                      Village        9    False\n","34                       EATDIS       10    False\n","13     Service holder (Private)       11    False\n","1                         21-25       12    False\n","12  Service holder (Government)       13    False\n","20                         Town       14    False\n","24                       LIVWTH       15    False\n","30                        SMOKE       15    False\n","22                       GENDER       17    False\n","19                         City       18    False\n","17                      Married       19    False\n","14                      Student       20    False\n","2                         26-30       21    False\n","18                    Unmarried       22    False\n","0                         16-20       23    False\n","31                        DRINK       25    False\n","11                        Other       25    False\n","33                       PREMED       25    False\n","32                      ILLNESS       27    False\n","15                   Unemployed       28    False\n","10                  Businessman       29    False\n","5                         41-45       30    False\n","8                         56-60       31    False\n","3                         31-35       31    False\n","6                         46-50       33    False\n","9                           61+       34    False\n","7                         51-55       35    False\n","4                         36-40       35    False\n","16                     Divorced       37    False"],"text/html":["\n","  <div id=\"df-297f6a8e-3727-4308-9ea3-70562157cefe\" 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>Feature</th>\n","      <th>Ranking</th>\n","      <th>Useful?</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>47</th>\n","      <td>LOST</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    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<td>7</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>EDU</td>\n","      <td>8</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Village</td>\n","      <td>9</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>EATDIS</td>\n","      <td>10</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Service holder (Private)</td>\n","      <td>11</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>21-25</td>\n","      <td>12</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Service holder (Government)</td>\n","      <td>13</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Town</td>\n","      <td>14</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>LIVWTH</td>\n","      <td>15</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>SMOKE</td>\n","      <td>15</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>GENDER</td>\n","      <td>17</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>City</td>\n","      <td>18</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Married</td>\n","      <td>19</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Student</td>\n","      <td>20</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>26-30</td>\n","      <td>21</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Unmarried</td>\n","      <td>22</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>16-20</td>\n","      <td>23</td>\n","      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[\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":59}]},{"cell_type":"markdown","source":["###1.3.2 Select K Best\n","<a name=\"selectkbest\"></a>"],"metadata":{"id":"uNJBNUQq53AR"}},{"cell_type":"code","source":["best_feat = SelectKBest(score_func=chi2, k=10)\n","scorefit = best_feat.fit(X_train_select, y_train_select)\n","\n","# create data frame to store the results\n","depress_scores = pd.DataFrame(scorefit.scores_)\n","depress_columns = pd.DataFrame(X_train_select.columns)\n","\n","feat_scores = pd.concat([depress_columns, depress_scores], axis=1)\n","feat_scores.columns = [\"Feature\", \"Score\"]\n","\n","feat_scores.sort_values(\"Score\", ascending=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"gwktNrorc385","executionInfo":{"status":"ok","timestamp":1714518391797,"user_tz":-120,"elapsed":231,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"5e9a9f13-7e85-43ef-cb6f-ef009c0da8ca"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        Feature      Score\n","45                        INFER  53.780432\n","40                        DEPRI  53.754232\n","39                         ANXI  46.900530\n","26                       POSSAT  42.904378\n","41                       ABUSED  41.823757\n","42                        CHEAT  36.554157\n","46                     CONFLICT  35.617527\n","44                      SUICIDE  21.020899\n","47                         LOST  20.957207\n","36                        INSOM  20.639833\n","25                       ENVSAT  18.811774\n","43                       THREAT  18.630685\n","27                       FINSTR  17.216600\n","28                         DEBT  10.619588\n","34                       EATDIS   8.570859\n","38                       WRKPRE   6.766633\n","37                         TSSN   5.896015\n","21                      Village   4.573678\n","12  Service holder (Government)   2.943291\n","30                        SMOKE   2.574374\n","9                           61+   1.919355\n","8                         56-60   1.919355\n","10                  Businessman   1.681096\n","31                        DRINK   1.521076\n","32                      ILLNESS   1.286058\n","20                         Town   1.111686\n","16                     Divorced   1.042017\n","6                         46-50   1.042017\n","7                         51-55   1.042017\n","35                       AVGSLP   0.763039\n","17                      Married   0.757492\n","2                         26-30   0.708880\n","15                   Unemployed   0.633654\n","22                       GENDER   0.592175\n","4                         36-40   0.521008\n","33                       PREMED   0.498658\n","19                         City   0.436314\n","1                         21-25   0.398648\n","3                         31-35   0.220182\n","5                         41-45   0.220182\n","29                        PHYEX   0.213719\n","0                         16-20   0.197392\n","18                    Unmarried   0.082674\n","24                       LIVWTH   0.042009\n","14                      Student   0.031059\n","23                          EDU   0.010798\n","11                        Other   0.005647\n","13     Service holder (Private)   0.000002"],"text/html":["\n","  <div id=\"df-048faf69-0638-497d-bf68-fdc45a660650\" 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","        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<td>CONFLICT</td>\n","      <td>35.617527</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>SUICIDE</td>\n","      <td>21.020899</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>LOST</td>\n","      <td>20.957207</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>INSOM</td>\n","      <td>20.639833</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>ENVSAT</td>\n","      <td>18.811774</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>THREAT</td>\n","      <td>18.630685</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>FINSTR</td>\n","      <td>17.216600</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>DEBT</td>\n","      <td>10.619588</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>EATDIS</td>\n","      <td>8.570859</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>WRKPRE</td>\n","      <td>6.766633</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>TSSN</td>\n","      <td>5.896015</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Village</td>\n","      <td>4.573678</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Service holder (Government)</td>\n","      <td>2.943291</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>SMOKE</td>\n","      <td>2.574374</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>61+</td>\n","      <td>1.919355</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>56-60</td>\n","      <td>1.919355</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Businessman</td>\n","      <td>1.681096</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>DRINK</td>\n","      <td>1.521076</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>ILLNESS</td>\n","      <td>1.286058</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Town</td>\n","      <td>1.111686</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Divorced</td>\n","      <td>1.042017</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>46-50</td>\n","      <td>1.042017</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>51-55</td>\n","      <td>1.042017</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>AVGSLP</td>\n","      <td>0.763039</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Married</td>\n","      <td>0.757492</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>26-30</td>\n","      <td>0.708880</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Unemployed</td>\n","      <td>0.633654</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>GENDER</td>\n","      <td>0.592175</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>36-40</td>\n","      <td>0.521008</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>PREMED</td>\n","      <td>0.498658</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>City</td>\n","      <td>0.436314</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>21-25</td>\n","      <td>0.398648</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>31-35</td>\n","      <td>0.220182</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41-45</td>\n","      <td>0.220182</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>PHYEX</td>\n","      <td>0.213719</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>16-20</td>\n","      <td>0.197392</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Unmarried</td>\n","      <td>0.082674</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>LIVWTH</td>\n","      <td>0.042009</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Student</td>\n","      <td>0.031059</td>\n","    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]\n","\n","depress_kbest.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"1f8N63Ul8Gvu","executionInfo":{"status":"ok","timestamp":1714500183810,"user_tz":-120,"elapsed":18,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"c99f9d0b-da39-4432-d5b3-560c54a0a3c1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   DEPRI  INFER  ANXI  POSSAT  CHEAT  ABUSED  CONFLICT  SUICIDE  THREAT  LOST  \\\n","0    0.0    0.0   1.0     1.0    0.0     0.0       1.0      0.0     0.0   0.0   \n","1    1.0    1.0   1.0     0.0    0.0     1.0       0.0      0.0     0.0   0.0   \n","2    1.0    0.0   1.0     1.0    0.0     0.0       0.0      0.0     0.0   0.0   \n","3    1.0    0.0   1.0     1.0    1.0     0.0       0.0      0.0     0.0   0.0   \n","4    1.0    1.0   1.0     1.0    0.0     0.0       1.0      0.0     0.0   0.0   \n","\n","   FINSTR  INSOM  ENVSAT  DEBT  \n","0     0.0    0.0     1.0   0.0  \n","1     1.0    1.0     1.0   0.0  \n","2     0.0    0.0     1.0   0.0  \n","3     0.0    0.0     0.0   0.0  \n","4     1.0    1.0     0.0   0.0  "],"text/html":["\n","  <div id=\"df-2eed68e5-ed8c-4b17-88c6-4f72117baa59\" 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>DEPRI</th>\n","      <th>INFER</th>\n","      <th>ANXI</th>\n","      <th>POSSAT</th>\n","      <th>CHEAT</th>\n","      <th>ABUSED</th>\n","      <th>CONFLICT</th>\n","      <th>SUICIDE</th>\n","      <th>THREAT</th>\n","      <th>LOST</th>\n","      <th>FINSTR</th>\n","      <th>INSOM</th>\n","      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correlation between isolated features\n","\n","plt.figure(figsize=(14, 10))\n","\n","#define heatmap\n","hmap = depress_kbest.corr()\n","\n","#define colour map\n","cmap = sns.diverging_palette(h_neg=20, h_pos=250, s=65, l=30,\n","                            center=\"light\", as_cmap=True)\n","\n","#define mask to hide mirrored values in the table\n","mask = np.triu(np.ones_like(hmap, dtype=bool))\n","\n","#create the plot\n","sns.heatmap(hmap, mask=mask, center=0, annot=True,\n","            square=True, cmap=cmap);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":885},"id":"foe1i87t_EwA","executionInfo":{"status":"ok","timestamp":1714413586172,"user_tz":-120,"elapsed":2454,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"a908f8fa-c944-4927-8263-be2c022ad8d6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x1000 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["The plot shows that feelings of inferiority (INFER) and deprivation of something (DEPRI) amplify each other. Similarly, anxiety (ANXI) leads to feeling deprived, as well as inferiority complex, and vice versa. Furthermore, whether participants are satisfied with their environment (ENVSAT) is related to satisficatin with their position workplace or academic achievements (POSSAT). Whether a respondent is in debt correlates higher to financial stress (FINSTR).\n","\n","Conversly, financial stress goes down with higher workplace/achievement satisfaction. Environment satisfaction lowers when the participants feel deprived of something, or when they felt abused recently, or if they are suffering from financial stress."],"metadata":{"id":"aUheHY0zBYbO"}},{"cell_type":"code","source":["# attach label column to the dataframe\n","\n","depress_kbest = pd.concat([depress_kbest, depress_enc[\"DEPRESSED\"]], axis=1)\n","depress_kbest.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ZxeLZqsJJ_qq","executionInfo":{"status":"ok","timestamp":1714518427389,"user_tz":-120,"elapsed":333,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"cac6b95f-bd2d-4e09-8242-b168a0f19bfd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   DEPRI  INFER  ANXI  POSSAT  CHEAT  ABUSED  CONFLICT  SUICIDE  THREAT  LOST  \\\n","0    0.0    0.0   1.0     1.0    0.0     0.0       1.0      0.0     0.0   0.0   \n","1    1.0    1.0   1.0     0.0    0.0     1.0       0.0      0.0     0.0   0.0   \n","2    1.0    0.0   1.0     1.0    0.0     0.0       0.0      0.0     0.0   0.0   \n","3    1.0    0.0   1.0     1.0    1.0     0.0       0.0      0.0     0.0   0.0   \n","4    1.0    1.0   1.0     1.0    0.0     0.0       1.0      0.0     0.0   0.0   \n","\n","   FINSTR  INSOM  ENVSAT  DEBT  DEPRESSED  DEPRESSED  \n","0     0.0    0.0     1.0   0.0        0.0        0.0  \n","1     1.0    1.0     1.0   0.0        1.0        1.0  \n","2     0.0    0.0     1.0   0.0        0.0        0.0  \n","3     0.0    0.0     0.0   0.0        1.0        1.0  \n","4     1.0    1.0     0.0   0.0        1.0        1.0  "],"text/html":["\n","  <div id=\"df-3a072b42-62c9-47a3-9145-124f03fad1f9\" 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","   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},\n    {\n      \"column\": \"ANXI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48831805873280476,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"POSSAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5004116785653043,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHEAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4054577796940218,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n    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})\n","\n","depress_rfe_ranking.sort_values(\"Rank\")"],"metadata":{"id":"ZZeEzb5dNnc4","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1714518446805,"user_tz":-120,"elapsed":7257,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"548d9748-773a-4c0c-91d2-6f764409620b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        Feature  Rank  Useful?\n","23                          EDU     1     True\n","45                        INFER     1     True\n","41                       ABUSED     1     True\n","40                        DEPRI     1     True\n","39                         ANXI     1     True\n","38                       WRKPRE     1     True\n","37                         TSSN     1     True\n","35                       AVGSLP     1     True\n","29                        PHYEX     1     True\n","27                       FINSTR     1     True\n","26                       POSSAT     1     True\n","25                       ENVSAT     1     True\n","46                     CONFLICT     1     True\n","47                         LOST     1     True\n","19                         City     2    False\n","36                        INSOM     3    False\n","42                        CHEAT     4    False\n","1                         21-25     5    False\n","43                       THREAT     6    False\n","20                         Town     7    False\n","22                       GENDER     8    False\n","28                         DEBT     9    False\n","24                       LIVWTH    10    False\n","14                      Student    11    False\n","18                    Unmarried    12    False\n","21                      Village    13    False\n","2                         26-30    14    False\n","30                        SMOKE    15    False\n","33                       PREMED    16    False\n","0                         16-20    17    False\n","34                       EATDIS    18    False\n","17                      Married    19    False\n","13     Service holder (Private)    20    False\n","44                      SUICIDE    21    False\n","31                        DRINK    22    False\n","32                      ILLNESS    23    False\n","15                   Unemployed    24    False\n","11                        Other    25    False\n","10                  Businessman    26    False\n","12  Service holder (Government)    27    False\n","5                         41-45    28    False\n","6                         46-50    29    False\n","8                         56-60    30    False\n","3                         31-35    31    False\n","9                           61+    32    False\n","7                         51-55    33    False\n","16                     Divorced    34    False\n","4                         36-40    35    False"],"text/html":["\n","  <div 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<th>40</th>\n","      <td>DEPRI</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>ANXI</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>WRKPRE</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>TSSN</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>AVGSLP</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>PHYEX</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>FINSTR</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>POSSAT</td>\n","      <td>1</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>ENVSAT</td>\n","     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 </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>GENDER</td>\n","      <td>8</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>DEBT</td>\n","      <td>9</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>LIVWTH</td>\n","      <td>10</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Student</td>\n","      <td>11</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Unmarried</td>\n","      <td>12</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>Village</td>\n","      <td>13</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>26-30</td>\n","      <td>14</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>SMOKE</td>\n","      <td>15</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>PREMED</td>\n","      <td>16</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>16-20</td>\n","      <td>17</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>EATDIS</td>\n","      <td>18</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Married</td>\n","      <td>19</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Service holder (Private)</td>\n","      <td>20</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>SUICIDE</td>\n","      <td>21</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>DRINK</td>\n","      <td>22</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>ILLNESS</td>\n","      <td>23</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Unemployed</td>\n","      <td>24</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Other</td>\n","      <td>25</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Businessman</td>\n","      <td>26</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Service holder (Government)</td>\n","      <td>27</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41-45</td>\n","      <td>28</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>46-50</td>\n","      <td>29</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>56-60</td>\n","      <td>30</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>31-35</td>\n","      <td>31</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      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]\n","\n","depress_rfe.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"FYBkiCWGk7zp","executionInfo":{"status":"ok","timestamp":1714512703644,"user_tz":-120,"elapsed":588,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"dfef0849-b97b-4759-b45f-9bedada6f7ce"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   DEPRI  INFER  ANXI  POSSAT  CHEAT  CONFLICT  LOST  FINSTR  ENVSAT  EDU  \\\n","0    0.0    0.0   1.0     1.0    0.0       1.0   0.0     0.0     1.0  0.0   \n","1    1.0    1.0   1.0     0.0    0.0       0.0   0.0     1.0     1.0  0.0   \n","2    1.0    0.0   1.0     1.0    0.0       0.0   0.0     0.0     1.0  2.0   \n","3    1.0    0.0   1.0     1.0    1.0       0.0   0.0     0.0     0.0  2.0   \n","4    1.0    1.0   1.0     1.0    0.0       1.0   0.0     1.0     0.0  1.0   \n","\n","   WRKPRE  TSSN  AVGSLP  PHYEX  \n","0     0.0   1.0     0.0    1.0  \n","1     2.0   2.0     3.0    2.0  \n","2     1.0   3.0     1.0    2.0  \n","3     3.0   1.0     2.0    1.0  \n","4     1.0   2.0     2.0    2.0  "],"text/html":["\n","  <div id=\"df-96598534-6d7b-4ead-a5cb-3c01c5e4c388\" 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>DEPRI</th>\n","      <th>INFER</th>\n","      <th>ANXI</th>\n","      <th>POSSAT</th>\n","      <th>CHEAT</th>\n","      <th>CONFLICT</th>\n","      <th>LOST</th>\n","      <th>FINSTR</th>\n","      <th>ENVSAT</th>\n","      <th>EDU</th>\n","      <th>WRKPRE</th>\n","      <th>TSSN</th>\n","      <th>AVGSLP</th>\n","      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Yfrl+3oa4vEdLiSk0yHDveIEXxqtLXJ6OHQBaCopjgJ/o9sh6t48ZGmRoyWC3DwsAaIADBw7IYrHo3Llzatu2rdasWaPY2Fjt379fISEhateunUt/s9ksq9UqSbJarS6FMcd2x7baZGZmasGCBdXa8/PzFR4eXqd5FxQU1KmftyEu7+HpmJrqs1Rd4iovL2+anQOAl6E4BgAA4AN69Oih/fv369SpU3r77beVkpKi7du3N+k+58yZo/T0dOfrsrIyde7cWQkJCTKZTJd8r91uV0FBgUaNGqXg4OAmnWdzIi7v0VJi6p2x2a3jhQYaWjSwqk5xOa72BAB/R3EMAADAB4SEhOjaa6+VJA0YMEB79+7Vc889p/Hjx6uiokInT550uXqspKTEuQJvTEyM9uzZ4zKeYzXLn67Se7HQ0FCFhoZWaw8ODq5zsaE+fb0JcXkPT8dkqwxoknHrEpevHUsAaChWqwQAAPBBVVVVstlsGjBggIKDg7V161bntiNHjqi4uFgWi0WSZLFYdODAAZWWljr7FBQUyGQyKTY2ttnnDgAA0Jy4cgwAAMDLzZkzR6NHj1aXLl10+vRp5ebmatu2bdq8ebMiIyM1ZcoUpaenKyoqSiaTSdOnT5fFYlFcXJwkKSEhQbGxsZo4caKWLFkiq9WquXPnKi0trcYrwwAAAHwJxTEAAAAvV1paqkmTJun48eOKjIxU3759tXnzZo0aNUqStGzZMgUGBio5OVk2m02JiYl68cUXne8PCgrSunXrlJqaKovFojZt2iglJUULFy70VEgAAADNhuIYAACAl1u5cuUlt4eFhSkrK0tZWVm19unatas2bNjg7qkBAAC0eDxzDAAAAAAAAH6L4hgAAAAAAAD8FsUxAAAAAAAA+C2KYwAAAAAAAPBbFMcAAAAAAADgtyiOAQAAAAAAwG9RHAMAAAAAAIDfojgGAAAAAAAAv0VxDAAAAAAAAH6L4hgAAAAAAAD8FsUxAAAAAAAA+C2KYwAAAAAAAPBbFMcA+K3ly5erb9++MplMMplMslgs2rhxo3P7uXPnlJaWpvbt26tt27ZKTk5WSUmJyxjFxcVKSkpSeHi4oqOjNWvWLJ0/f765QwEAAPAJmZmZGjRokCIiIhQdHa177rlHR44ccenDZzQA7kZxDIDf6tSpkxYvXqyioiLt27dPI0aM0NixY3Xo0CFJ0syZM/X+++8rLy9P27dv17FjxzRu3Djn+ysrK5WUlKSKigrt2rVLr732mnJycjRv3jxPhQQAAODVtm/frrS0NO3evVsFBQWy2+1KSEjQ2bNnnX34jAbA3Vp5egIA4Cljxoxxef34449r+fLl2r17tzp16qSVK1cqNzdXI0aMkCRlZ2erV69e2r17t+Li4pSfn6/Dhw9ry5YtMpvN6tevnxYtWqTZs2crIyNDISEhnggLAADAa23atMnldU5OjqKjo1VUVKShQ4fq1KlTfEYD4HYUxwBAF/7CmJeXp7Nnz8pisaioqEh2u13x8fHOPj179lSXLl1UWFiouLg4FRYWqk+fPjKbzc4+iYmJSk1N1aFDh9S/f/8a92Wz2WSz2Zyvy8rKJEl2u112u73WOTq2hQYajYr1UmN7gmPfnpxDUyAu71KfuHwtdgBoyU6dOiVJioqKkqQm/YwGwH9RHAPg1w4cOCCLxaJz586pbdu2WrNmjWJjY7V//36FhISoXbt2Lv3NZrOsVqskyWq1unzocmx3bKtNZmamFixYUK09Pz9f4eHhl53zooFVl+1TXxs2bHD7mPVVUFDg6Sk0CeLyLnWJq7y8vBlmAgCoqqrSjBkzdMstt6h3796SLnzGaorPaA3946Wjj8QfML0FcTWd0CD3/w44fq+a+g+YFMcA+LUePXpo//79OnXqlN5++22lpKRo+/btTbrPOXPmKD093fm6rKxMnTt3VkJCgkwmU63vs9vtKigo0KP7AmWrCnDrnA5mJLp1vPpwxDVq1CgFBwd7bB7uRlzepT5xOU6YAABNKy0tTQcPHtRHH33U5Ptq7B8vJf6A6W2Iy/2WDG66sZv6D5gUxwD4tZCQEF177bWSpAEDBmjv3r167rnnNH78eFVUVOjkyZMuf5ksKSlRTEyMJCkmJkZ79uxxGc+xUpKjT01CQ0MVGhparT04OLhOxQZbVYBsle4tjrWEIkdd4/c2xOVd6hKXL8YNAC3NtGnTtG7dOu3YsUOdOnVytsfExDTJZ7SG/vFS4g+Y3oa4mk7vjM1uHzM00NCigVVN/gdMimMAcJGqqirZbDYNGDBAwcHB2rp1q5KTkyVJR44cUXFxsSwWiyTJYrHo8ccfV2lpqaKjoyVd+IuGyWRSbGysx2IAAG/SO2OzWwv+Xy1OcttYAJqfYRiaPn261qxZo23btql79+4u25vqM1pj/3gp8QdMb0Nc7ufun/+LNfUfMCmOAfBbc+bM0ejRo9WlSxedPn1aubm52rZtmzZv3qzIyEhNmTJF6enpioqKkslk0vTp02WxWBQXFydJSkhIUGxsrCZOnKglS5bIarVq7ty5SktLq/HDFQAAAC4tLS1Nubm5Wrt2rSIiIpzPCIuMjFTr1q35jAagSQTWp3NmZqYGDRqkiIgIRUdH65577tGRI0dc+pw7d05paWlq37692rZtq+TkZOclrA7FxcVKSkpSeHi4oqOjNWvWLJ0/f77x0QBAPZSWlmrSpEnq0aOHRo4cqb1792rz5s0aNWqUJGnZsmW66667lJycrKFDhyomJkbvvPOO8/1BQUFat26dgoKCZLFYdN9992nSpElauHChp0ICAADwasuXL9epU6c0fPhwdejQwfn1t7/9zdmHz2gA3K1eV45t375daWlpGjRokM6fP68//elPSkhI0OHDh9WmTRtJ0syZM7V+/Xrl5eUpMjJS06ZN07hx47Rz505JUmVlpZKSkhQTE6Ndu3bp+PHjmjRpkoKDg/XEE0+4P0IAqMXKlSsvuT0sLExZWVnKysqqtU/Xrl1bxINSAQAAfIFhXH61Oz6jAXC3ehXHNm3a5PI6JydH0dHRKioq0tChQ3Xq1CmtXLlSubm5GjFihCQpOztbvXr10u7duxUXF6f8/HwdPnxYW7ZskdlsVr9+/bRo0SLNnj1bGRkZCgkJcV90AAAAAAAAwCU06pljp06dkiRFRUVJkoqKimS32xUfH+/s07NnT3Xp0kWFhYWKi4tTYWGh+vTpI7PZ7OyTmJio1NRUHTp0SP3796+2H5vNJpvN5nztWIHAbrfLbrdfco6O7aGBl/8LRH1cbr9NzbF/T8/D3Yir6YQGufd3QPrx96quv4cAAAAAALQ0DS6OVVVVacaMGbrlllvUu3dvSZLValVISIjLkrqSZDabnQ9StFqtLoUxx3bHtppkZmZqwYIF1drz8/MVHh5ep/kuGlhVp3511VIu0S0oKPD0FJoEcbnfksFNN/bl4iovL2+6nQMAAAAA0AgNLo6lpaXp4MGD+uijj9w5nxrNmTNH6enpztdlZWXq3LmzEhISZDKZLvleu92ugoICPbovULYq9y0rejAj0W1jNYQjrlGjRvnU8rPE1XR6Z2x2+5ihgYYWDay6bFyOqz0BAAAAAGhpGlQcmzZtmtatW6cdO3aoU6dOzvaYmBhVVFTo5MmTLlePlZSUKCYmxtlnz549LuM5VrN09Pmp0NDQGpfcDQ4OrnOhwVYVIFul+4pjLaVwU5/vgTchLvdz58//T10uLl88lgAAAAAA3xBYn86GYWjatGlas2aNPvjgA3Xv3t1l+4ABAxQcHKytW7c6244cOaLi4mJZLBZJksVi0YEDB1RaWursU1BQIJPJpNjY2MbEAgAAAAAAANRLva4cS0tLU25urtauXauIiAjnM8IiIyPVunVrRUZGasqUKUpPT1dUVJRMJpOmT58ui8WiuLg4SVJCQoJiY2M1ceJELVmyRFarVXPnzlVaWlqNV4cBAAAAAAAATaVeV44tX75cp06d0vDhw9WhQwfn19/+9jdnn2XLlumuu+5ScnKyhg4dqpiYGL3zzjvO7UFBQVq3bp2CgoJksVh03333adKkSVq4cKH7ogIAAPAjmZmZGjRokCIiIhQdHa177rlHR44ccelz7tw5paWlqX379mrbtq2Sk5Odj7ZwKC4uVlJSksLDwxUdHa1Zs2bp/PnzzRkKAABAs6vXlWOGYVy2T1hYmLKyspSVlVVrn65du7aY1R4BAAC83fbt25WWlqZBgwbp/Pnz+tOf/qSEhAQdPnxYbdq0kSTNnDlT69evV15eniIjIzVt2jSNGzdOO3fulCRVVlYqKSlJMTEx2rVrl44fP65JkyYpODhYTzzxhCfDAwAAaFINXq0SAAAALcOmTZtcXufk5Cg6OlpFRUUaOnSoTp06pZUrVyo3N1cjRoyQJGVnZ6tXr17avXu34uLilJ+fr8OHD2vLli0ym83q16+fFi1apNmzZysjI0MhISGeCA0AAKDJURwDAADwMadOnZIkRUVFSZKKiopkt9sVHx/v7NOzZ0916dJFhYWFiouLU2Fhofr06SOz2ezsk5iYqNTUVB06dEj9+/evth+bzSabzeZ8XVZWJkmy2+2y2+2XnKNje2jg5e9MqI/L7bepOfbv6Xm4my/G1VJiCg1y7++A43eqLnF5OnYAaCkojgEAAPiQqqoqzZgxQ7fccot69+4tSbJarQoJCVG7du1c+prNZucCS1ar1aUw5tju2FaTzMxMLViwoFp7fn6+wsPD6zTfRQOr6tSvrlrKozsKCgo8PYUm4YtxeTqmJYObZty6xFVeXt40OwcAL0NxDAAAwIekpaXp4MGD+uijj5p8X3PmzFF6errzdVlZmTp37qyEhASZTKZLvtdut6ugoECP7guUrSrAbXM6mJHotrEawhHXqFGjFBwc7NG5uJMvxtVSYuqdsdmt44UGGlo0sKpOcTmu9gQAf0dxDAAAwEdMmzZN69at044dO9SpUydne0xMjCoqKnTy5EmXq8d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correlation between isolated features\n","\n","plt.figure(figsize=(14, 10))\n","\n","#define heatmap\n","hmap = depress_rfe.corr()\n","\n","#define colour map\n","cmap = sns.diverging_palette(h_neg=20, h_pos=250, s=65, l=30,\n","                            center=\"light\", as_cmap=True)\n","\n","#define mask to hide mirrored values in the table\n","mask = np.triu(np.ones_like(hmap, dtype=bool))\n","\n","#create the plot\n","sns.heatmap(hmap, mask=mask, center=0, annot=True,\n","            square=True, cmap=cmap);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":885},"id":"f1zKwf7_mHVw","executionInfo":{"status":"ok","timestamp":1714512715039,"user_tz":-120,"elapsed":1265,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"0de35cfb-ce59-4e8b-82f8-32d8bb332756"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1400x1000 with 2 Axes>"],"image/png":"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SelectKBest only returned features with binary values as useful, RFE also returned features with ordinal relationships to one another. The correlations with these features are weaker than those selected by SelectKBest."],"metadata":{"id":"lTMeoupLmemc"}},{"cell_type":"code","source":["# attach label column to data frame with selected features\n","\n","depress_rfe = pd.concat([depress_rfe, depress_enc[\"DEPRESSED\"]], axis=1)\n","depress_rfe.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"wIrMs0kJLk5y","executionInfo":{"status":"ok","timestamp":1714512739112,"user_tz":-120,"elapsed":240,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"f4b4b464-a0a9-4cea-f310-52d82b89a27f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   DEPRI  INFER  ANXI  POSSAT  CHEAT  CONFLICT  LOST  FINSTR  ENVSAT  EDU  \\\n","0    0.0    0.0   1.0     1.0    0.0       1.0   0.0     0.0     1.0  0.0   \n","1    1.0    1.0   1.0     0.0    0.0       0.0   0.0     1.0     1.0  0.0   \n","2    1.0    0.0   1.0     1.0    0.0     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index=False)"],"metadata":{"id":"UdJnIoYLLwQX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","X_train.shape, X_test.shape, y_train.shape, y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kHRQ_gd5HHb9","executionInfo":{"status":"ok","timestamp":1661345742849,"user_tz":-120,"elapsed":19,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"2fc8c26e-bdd3-4c6a-ae79-ac8aeb6a839a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((483, 12), (121, 12), (483,), (121,))"]},"metadata":{},"execution_count":248}]},{"cell_type":"markdown","source":["Let's check the dataset's balance."],"metadata":{"id":"Hstn3QCX0Ulk"}},{"cell_type":"code","source":["sns.countplot(x=\"DEPRESSED\", data=df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"Vv5sEP9CU4VU","executionInfo":{"status":"ok","timestamp":1661345742850,"user_tz":-120,"elapsed":17,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"3fb9d371-7148-465d-ad4b-ce0742027c31"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fab6add9d90>"]},"metadata":{},"execution_count":249},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Using smote,  I downsampled the dataset to balance it out (downsampling would yield better results than upsampling: https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/)"],"metadata":{"id":"w1RB3jat0dRL"}},{"cell_type":"code","source":["from imblearn.over_sampling import SMOTE\n","\n","sm = SMOTE(random_state=42)\n","X_res, y_res = sm.fit_resample(X_train, y_train)"],"metadata":{"id":"gsyVMDotU3Kq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["np.bincount(y_res)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uJ05QmoBXpO3","executionInfo":{"status":"ok","timestamp":1661345742851,"user_tz":-120,"elapsed":13,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"3296df9f-bc35-4316-d2e2-bc88b157c63f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([320, 320])"]},"metadata":{},"execution_count":251}]},{"cell_type":"code","source":["X_res.shape, y_res.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pzOu4XbuWO91","executionInfo":{"status":"ok","timestamp":1661345742851,"user_tz":-120,"elapsed":10,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"956695db-806a-42b1-b5b0-64582a1fc5e3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((640, 12), (640,))"]},"metadata":{},"execution_count":252}]},{"cell_type":"code","source":["y_res = y_res.to_numpy()\n","y_test = y_test.to_numpy()"],"metadata":{"id":"m53eT6Rw7M8T"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#2 Classical classifier"],"metadata":{"id":"St_LEyfk0wk1"}},{"cell_type":"markdown","source":["The model I wish to use is Gradient Boosting."],"metadata":{"id":"Atsc9Ham20sS"}},{"cell_type":"code","source":["from sklearn.pipeline import make_pipeline\n","from sklearn.ensemble import GradientBoostingClassifier\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import GridSearchCV, cross_val_score\n","from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, plot_confusion_matrix"],"metadata":{"id":"XEeGGCBFHjU7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["The dataset doesn't contain any missing values, so there is no use to implement the KNN Imputer. Furthermore, as Gradient Boosting belongs to the family of Decision Tree-based algorithms, they never use multiple features at the same time, making feature scaling not necessary, unlike in other Machine Learning algorithms (Géron, p.725)."],"metadata":{"id":"J_uJVbrQ06ro"}},{"cell_type":"code","source":["booster = GradientBoostingClassifier(random_state=42)"],"metadata":{"id":"NeVqO9nJL8-w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["booster.get_params()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PPgh_cwpIgLm","executionInfo":{"status":"ok","timestamp":1661345817514,"user_tz":-120,"elapsed":2,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"e69b3d83-e743-4d06-8c15-28eb125528ad"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'ccp_alpha': 0.0,\n"," 'criterion': 'friedman_mse',\n"," 'init': None,\n"," 'learning_rate': 0.1,\n"," 'loss': 'deviance',\n"," 'max_depth': 3,\n"," 'max_features': None,\n"," 'max_leaf_nodes': None,\n"," 'min_impurity_decrease': 0.0,\n"," 'min_samples_leaf': 1,\n"," 'min_samples_split': 2,\n"," 'min_weight_fraction_leaf': 0.0,\n"," 'n_estimators': 100,\n"," 'n_iter_no_change': None,\n"," 'random_state': 42,\n"," 'subsample': 1.0,\n"," 'tol': 0.0001,\n"," 'validation_fraction': 0.1,\n"," 'verbose': 0,\n"," 'warm_start': False}"]},"metadata":{},"execution_count":256}]},{"cell_type":"markdown","source":["Set up a grid search."],"metadata":{"id":"Z5g8KxO45jYS"}},{"cell_type":"code","source":["param_grid = {\"n_estimators\": [30, 50, 60, 100],\n","              \"learning_rate\": [0.05, 0.1, 0.2, 0.25, 0.3],\n","              \"max_depth\": [3, 4, 5]}"],"metadata":{"id":"bf5ckSf6MBtz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["gs = GridSearchCV(booster, param_grid, cv=10, scoring='f1_macro', n_jobs=-1, verbose=1, refit=True)"],"metadata":{"id":"ZFnYF7A9OgtW"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We fit the data not to the original train data, but to the resized data performed by SMOTE."],"metadata":{"id":"43ARHM7I5msB"}},{"cell_type":"code","source":["gs.fit(X_res, y_res)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fmt82OGiOzzB","executionInfo":{"status":"ok","timestamp":1661345864688,"user_tz":-120,"elapsed":42395,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"efddd8c2-393b-49f5-a0a7-ed02e20d71d1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 10 folds for each of 60 candidates, totalling 600 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=10, estimator=GradientBoostingClassifier(random_state=42),\n","             n_jobs=-1,\n","             param_grid={'learning_rate': [0.05, 0.1, 0.2, 0.25, 0.3],\n","                         'max_depth': [3, 4, 5],\n","                         'n_estimators': [30, 50, 60, 100]},\n","             scoring='f1_macro', verbose=1)"]},"metadata":{},"execution_count":259}]},{"cell_type":"code","source":["gs.best_score_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tNOVmFk_Q4HE","executionInfo":{"status":"ok","timestamp":1661345864688,"user_tz":-120,"elapsed":16,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"4f24e6bb-0f61-4db2-9d7a-11ea681d2d34"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9043772196884824"]},"metadata":{},"execution_count":260}]},{"cell_type":"code","source":["gs.best_estimator_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ESdp9ojORIA-","executionInfo":{"status":"ok","timestamp":1661345864689,"user_tz":-120,"elapsed":14,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"661c0771-a03f-4b4c-cdd4-2f121394d0f1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["GradientBoostingClassifier(learning_rate=0.25, n_estimators=30, random_state=42)"]},"metadata":{},"execution_count":261}]},{"cell_type":"code","source":["predictions = gs.predict(X_test)\n","print(classification_report(y_test, predictions))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RiwCaafFTIhq","executionInfo":{"status":"ok","timestamp":1661345864690,"user_tz":-120,"elapsed":10,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"dd2b0e05-47f3-46c7-ecad-b48979740b83"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","         0.0       0.81      0.89      0.85        44\n","         1.0       0.93      0.88      0.91        77\n","\n","    accuracy                           0.88       121\n","   macro avg       0.87      0.88      0.88       121\n","weighted avg       0.89      0.88      0.89       121\n","\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import ConfusionMatrixDisplay\n","#help(ConfusionMatrixDisplay)\n","ConfusionMatrixDisplay.from_predictions(y_test, predictions)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"5ukvb5hPTyxM","executionInfo":{"status":"ok","timestamp":1661345865158,"user_tz":-120,"elapsed":14,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"0ac2f738-fb2a-49d8-d412-3e56b9e9e263"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fab6af4f510>"]},"metadata":{},"execution_count":264},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#2.1 Error analysis\n","\n","Let's plot the value of importance the model had given to the features."],"metadata":{"id":"Syy0t4pz57eQ"}},{"cell_type":"code","source":["feat_import = gs.best_estimator_.feature_importances_\n","import_feat = pd.DataFrame(index=X.columns, data=feat_import, columns=[\"Importance\"])\n","import_feat = import_feat.sort_values(\"Importance\")\n","\n","sns.barplot(data=import_feat, x=import_feat.index, y=\"Importance\")\n","plt.xticks(rotation=45);"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"EX70Sy_IoHB6","executionInfo":{"status":"ok","timestamp":1661345865616,"user_tz":-120,"elapsed":466,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"08c3d4ac-0f0a-435e-df88-f5a9bcfe396d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["We'll separate the errors the model made from the correct predictions it performed."],"metadata":{"id":"8DRK80aS66Z-"}},{"cell_type":"code","source":["x_error = X_test[predictions != y_test]\n","y_error = y_test[predictions != y_test]\n","p_error = predictions[predictions != y_test]\n","\n","x_correct = X_test[predictions == y_test]\n","y_correct = y_test[predictions == y_test]\n","p_correct = predictions[predictions == y_test]"],"metadata":{"id":"uyMzKU3-dAGU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sns.barplot(data=x_error)\n","plt.xticks(rotation=45)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":334},"id":"FJeEbPqlrBoH","executionInfo":{"status":"ok","timestamp":1661345866073,"user_tz":-120,"elapsed":463,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"60240166-a2da-452c-d815-a82ebc84143e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n"," <a list of 12 Text major ticklabel objects>)"]},"metadata":{},"execution_count":267},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["The above plot shows that TSSN (time spent on social network) had the most weight in making incorrect predictions. Possibly this is due to fact that it was ordinally encoded; with higher ranking numbers putting more weight in the overall decision to label a person as depressed or not."],"metadata":{"id":"IXl29bhJ7IA_"}},{"cell_type":"markdown","source":["# Neural model"],"metadata":{"id":"-AbtFi-7DSW-"}},{"cell_type":"code","source":["from tensorflow.keras.callbacks import EarlyStopping"],"metadata":{"id":"TJM-Q8ZMhwAm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["early_stop = EarlyStopping(monitor=\"val_loss\", mode = \"min\", patience=10)"],"metadata":{"id":"0zrHeU7rj5aR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.preprocessing import MinMaxScaler\n","\n","scaler = MinMaxScaler()\n","\n","X_res = scaler.fit_transform(X_res)\n","#X_test = scaler.transform(X_test)"],"metadata":{"id":"y4B633gQiT7i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["type(y_res)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ff7eadU35yWc","executionInfo":{"status":"ok","timestamp":1661345909654,"user_tz":-120,"elapsed":347,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"b95d7384-5a24-48ac-8147-09030e7033e6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["numpy.ndarray"]},"metadata":{},"execution_count":277}]},{"cell_type":"code","source":["print(X_res.shape, y_res.shape, X_test.shape, y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gyBvpZB2nuXF","executionInfo":{"status":"ok","timestamp":1661345866334,"user_tz":-120,"elapsed":13,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"db050982-19b1-4bf0-b23f-d9cab1f4d235"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(640, 12) (640,) (121, 12) (121,)\n"]}]},{"cell_type":"code","source":["model = keras.models.Sequential()\n","\n","#model.fit(X_res, y_res)\n","model.add(keras.layers.Dense(100, activation='relu', name='hidden1'))\n","model.add(keras.layers.Dropout(0.5))\n","model.add(keras.layers.Dense(50, activation='relu', name='hidden2'))\n","model.add(keras.layers.Dropout(0.5))\n","model.add(keras.layers.Dense(25, activation='relu', name='hidden3'))\n","model.add(keras.layers.Dropout(0.5))\n","model.add(keras.layers.Dense(1, activation='sigmoid', name='affine'))\n","\n","sgd = keras.optimizers.Adam(learning_rate=0.001)\n","model.compile(optimizer=sgd,\n","              loss='binary_crossentropy',\n","              metrics=['accuracy'])\n","\n","num_epochs = 100\n","history = model.fit(X_res, y_res, epochs=num_epochs,\n","                    validation_data=(X_test, y_test),\n","                    callbacks = early_stop)\n","\n","model.summary() # explain params"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IOUrh46DDVea","executionInfo":{"status":"ok","timestamp":1661346082870,"user_tz":-120,"elapsed":7042,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"74d20fe9-1757-4afd-f404-bfb72bbd15de"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","20/20 [==============================] - 1s 17ms/step - loss: 0.7083 - accuracy: 0.5188 - val_loss: 0.6835 - val_accuracy: 0.6364\n","Epoch 2/100\n","20/20 [==============================] - 0s 10ms/step - loss: 0.6636 - accuracy: 0.5734 - val_loss: 0.6268 - val_accuracy: 0.8347\n","Epoch 3/100\n","20/20 [==============================] - 0s 10ms/step - loss: 0.6224 - accuracy: 0.6594 - val_loss: 0.5496 - val_accuracy: 0.8843\n","Epoch 4/100\n","20/20 [==============================] - 0s 12ms/step - loss: 0.5624 - accuracy: 0.7359 - val_loss: 0.4575 - val_accuracy: 0.8926\n","Epoch 5/100\n","20/20 [==============================] - 0s 15ms/step - loss: 0.5263 - accuracy: 0.7641 - val_loss: 0.3852 - val_accuracy: 0.8926\n","Epoch 6/100\n","20/20 [==============================] - 0s 10ms/step - loss: 0.4353 - accuracy: 0.8078 - val_loss: 0.3122 - val_accuracy: 0.8926\n","Epoch 7/100\n","20/20 [==============================] - 0s 7ms/step - loss: 0.3905 - accuracy: 0.8438 - val_loss: 0.2788 - val_accuracy: 0.8843\n","Epoch 8/100\n","20/20 [==============================] - 0s 11ms/step - loss: 0.3768 - accuracy: 0.8484 - val_loss: 0.2583 - val_accuracy: 0.8926\n","Epoch 9/100\n","20/20 [==============================] - 0s 13ms/step - loss: 0.3328 - accuracy: 0.8828 - val_loss: 0.2598 - val_accuracy: 0.8926\n","Epoch 10/100\n","20/20 [==============================] - 0s 13ms/step - loss: 0.3301 - accuracy: 0.8687 - val_loss: 0.2580 - val_accuracy: 0.8926\n","Epoch 11/100\n","20/20 [==============================] - 0s 12ms/step - loss: 0.3244 - accuracy: 0.8922 - val_loss: 0.2546 - val_accuracy: 0.8843\n","Epoch 12/100\n","20/20 [==============================] - 0s 10ms/step - loss: 0.3129 - accuracy: 0.8687 - val_loss: 0.2483 - val_accuracy: 0.8843\n","Epoch 13/100\n","20/20 [==============================] - 0s 13ms/step - loss: 0.3258 - accuracy: 0.8719 - val_loss: 0.2260 - val_accuracy: 0.8926\n","Epoch 14/100\n","20/20 [==============================] - 0s 9ms/step - loss: 0.3240 - accuracy: 0.8703 - val_loss: 0.2392 - val_accuracy: 0.8926\n","Epoch 15/100\n","20/20 [==============================] - 0s 16ms/step - loss: 0.3254 - accuracy: 0.8828 - val_loss: 0.2392 - val_accuracy: 0.8926\n","Epoch 16/100\n","20/20 [==============================] - 0s 14ms/step - loss: 0.3056 - accuracy: 0.8797 - val_loss: 0.2365 - val_accuracy: 0.8926\n","Epoch 17/100\n","20/20 [==============================] - 0s 18ms/step - loss: 0.2942 - accuracy: 0.9016 - val_loss: 0.2469 - val_accuracy: 0.8926\n","Epoch 18/100\n","20/20 [==============================] - 0s 13ms/step - loss: 0.3052 - accuracy: 0.9016 - val_loss: 0.2346 - val_accuracy: 0.9008\n","Epoch 19/100\n","20/20 [==============================] - 0s 21ms/step - loss: 0.2953 - accuracy: 0.9000 - val_loss: 0.2429 - val_accuracy: 0.8926\n","Epoch 20/100\n","20/20 [==============================] - 0s 10ms/step - loss: 0.2909 - accuracy: 0.8844 - val_loss: 0.2272 - val_accuracy: 0.8926\n","Epoch 21/100\n","20/20 [==============================] - 0s 9ms/step - loss: 0.2842 - accuracy: 0.8953 - val_loss: 0.2540 - val_accuracy: 0.8843\n","Epoch 22/100\n","20/20 [==============================] - 0s 14ms/step - loss: 0.2688 - accuracy: 0.9031 - val_loss: 0.2434 - val_accuracy: 0.8843\n","Epoch 23/100\n","20/20 [==============================] - 0s 10ms/step - loss: 0.2883 - accuracy: 0.8953 - val_loss: 0.2402 - val_accuracy: 0.8843\n","Model: \"sequential_23\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," hidden1 (Dense)             (None, 100)               1300      \n","                                                                 \n"," dropout_42 (Dropout)        (None, 100)               0         \n","                                                                 \n"," hidden2 (Dense)             (None, 50)                5050      \n","                                                                 \n"," dropout_43 (Dropout)        (None, 50)                0         \n","                                                                 \n"," hidden3 (Dense)             (None, 25)                1275      \n","                                                                 \n"," dropout_44 (Dropout)        (None, 25)                0         \n","                                                                 \n"," affine (Dense)              (None, 1)                 26        \n","                                                                 \n","=================================================================\n","Total params: 7,651\n","Trainable params: 7,651\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["epochs = range(1, num_epochs + 1)\n","train_loss = history.history['loss']\n","dev_loss = history.history['val_loss']\n","\n","plt.plot(train_loss, 'b', label='train')\n","plt.plot(dev_loss, 'r', label='dev')\n","plt.title('Loss curves')\n","plt.xlabel('Epochs')\n","plt.legend()\n","plt.ylabel('Binary Crossentropy');"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"8KzJs4KlYVZt","executionInfo":{"status":"ok","timestamp":1661346087900,"user_tz":-120,"elapsed":5034,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"81497125-cad8-4334-ba8e-fa31091da1db"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["predictions = model.predict(X_test)\n","print(classification_report(y_test, predictions))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":342},"id":"XSzm6LeCZQle","executionInfo":{"status":"error","timestamp":1661346087900,"user_tz":-120,"elapsed":7,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"23ba747a-0eab-4268-8b5e-d9b8d52a1e20"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-281-1588a3cd274c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\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[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[1;32m   2108\u001b[0m     \"\"\"\n\u001b[1;32m   2109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2110\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\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   2111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2112\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\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;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     93\u001b[0m         raise ValueError(\n\u001b[1;32m     94\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[0;32m---> 95\u001b[0;31m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m             )\n\u001b[1;32m     97\u001b[0m         )\n","\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of binary and continuous targets"]}]},{"cell_type":"code","source":["predictions"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5D42mCZngdP5","executionInfo":{"status":"ok","timestamp":1661340278199,"user_tz":-120,"elapsed":838,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"42a4ff79-73c4-4a45-940a-975abe2b18a6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.07934877],\n","       [0.16899198],\n","       [0.1942749 ],\n","       [0.06256127],\n","       [0.9910799 ],\n","       [0.77184415],\n","       [0.9492662 ],\n","       [0.9876423 ],\n","       [0.04184747],\n","       [0.7107842 ],\n","       [0.578508  ],\n","       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them\n","\n","agerng = ['16-20','21-25', '26-30', '31-35', '36-40',\n","          '41-45', '46-50','51-55', '56-60', '61+']\n","edu = ['Post Graduate', 'Graduate', 'HSC', 'SSC']\n","prof = ['Other', 'Businessman', 'Service holder (Private)',\n","        'Service holder (Government)', 'Student', 'Unemployed']\n","marsts = ['Married', 'Unmarried', 'Divorced']\n","resdpl = ['City', 'Town', 'Village']\n","phyex = ['Regularly', 'Sometimes' 'Never']\n","avgslp = ['More than 8 hours', '8 hours', '7 hours',\n","          '6 hours', '5 hours', 'Below 5 hours']\n","tssn = ['Less than 2 hours', '2-4 hours a day', '5-7 hours a day',\n","        '8-10 hours a day', 'More than 10 hours a day']\n","wrkpre = ['No Pressure', 'Mild', 'Moderate', 'Severe']\n"],"metadata":{"id":"EpVtjyUX9hGQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.preprocessing import OrdinalEncoder\n","\n","cat_encoder = OrdinalEncoder(categories=[edu, phyex, avgslp, tssn, wrkpre])"],"metadata":{"id":"F6ZHMc6o9fxb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[[\"EDU\", \"PHYEX\", \"AVGSLP\", \"TSSN\", \"WRKPRE\"]] = cat_encoder.fit_transform(df[[\"EDU\",\n","                                                    \"PHYEX\", \"AVGSLP\", \"TSSN\", \"WRKPRE\"]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":342},"id":"fLL9Y755MhqH","executionInfo":{"status":"error","timestamp":1660914773160,"user_tz":-120,"elapsed":323,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"5f053046-4c8e-44af-cd09-43ea6c54c556"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-84-638c4e372981>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m df[[\"EDU\", \"PHYEX\", \"AVGSLP\", \"TSSN\", \"WRKPRE\"]] = cat_encoder.fit_transform(df[[\"EDU\",\n\u001b[0;32m----> 2\u001b[0;31m                                                     \"PHYEX\", \"AVGSLP\", \"TSSN\", \"WRKPRE\"]])\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    850\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\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[1;32m    851\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 852\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\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    853\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\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/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m         \u001b[0;31m# `_fit` will only raise an error when `self.handle_unknown=\"error\"`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 886\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle_unknown\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_unknown\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"allow-nan\"\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    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_unknown\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"use_encoded_value\"\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/python3.7/dist-packages/sklearn/preprocessing/_encoders.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, handle_unknown, force_all_finite)\u001b[0m\n\u001b[1;32m     94\u001b[0m                 \u001b[0mcats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m                 \u001b[0mcats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mXi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\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     97\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mXi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m\"OUS\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m                     \u001b[0msorted_cats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcats\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Post Graduate'"]}]},{"cell_type":"code","source":["from sklearn.preprocessing import OrdinalEncoder\n","\n","cat_encoder = OrdinalEncoder()\n","\n","\n","df[[\"AGERNG\",\"EDU\",\"PROF\", \"MARSTS\", \"RESDPL\",\"PHYEX\",\n","    \"AVGSLP\",\"TSSN\",\"WRKPRE\"]] = cat_encoder.fit_transform(df[[\"AGERNG\",\"EDU\",\n","                                                            \"PROF\", \"MARSTS\", \"RESDPL\",\n","                                                            \"PHYEX\",\"AVGSLP\",\"TSSN\",\"WRKPRE\"]])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"CvIMiUMi4TDY","executionInfo":{"status":"ok","timestamp":1660909251057,"user_tz":-120,"elapsed":238,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"b8064342-95a1-47a9-e955-ebe7fea34d40"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   AGERNG  GENDER  EDU  PROF  MARSTS  RESDPL  LIVWTH  ENVSAT  POSSAT  FINSTR  \\\n","0     2.0     1.0  2.0   5.0     2.0     1.0     1.0     1.0     1.0     0.0   \n","1     2.0     0.0  2.0   3.0     2.0     0.0     1.0     1.0     0.0     1.0   \n","2     1.0     0.0  1.0   4.0     2.0     0.0     1.0     1.0     1.0     0.0   \n","3     0.0     0.0  1.0   4.0     2.0     0.0     1.0     0.0     1.0     0.0   \n","4     1.0     0.0  0.0   4.0     2.0     1.0     1.0     0.0     1.0     1.0   \n","\n","   ...  ANXI  DEPRI  ABUSED  CHEAT  THREAT  SUICIDE  INFER  CONFLICT  LOST  \\\n","0  ...   1.0    0.0     0.0    0.0     0.0      0.0    0.0       1.0   0.0   \n","1  ...   1.0    1.0     1.0    0.0     0.0      0.0    1.0       0.0   0.0   \n","2  ...   1.0    1.0     0.0    0.0     0.0      0.0    0.0       0.0   0.0   \n","3  ...   1.0    1.0     0.0    1.0     0.0      0.0    0.0       0.0   0.0   \n","4  ...   1.0    1.0     0.0    0.0     0.0      0.0    1.0       1.0   0.0   \n","\n","   DEPRESSED  \n","0          0  \n","1          1  \n","2          0  \n","3          1  \n","4          1  \n","\n","[5 rows x 31 columns]"],"text/html":["\n","  <div id=\"df-0b3dfed3-114e-418a-b4d4-320f68d0a6c3\">\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>AGERNG</th>\n","      <th>GENDER</th>\n","      <th>EDU</th>\n","      <th>PROF</th>\n","      <th>MARSTS</th>\n","      <th>RESDPL</th>\n","      <th>LIVWTH</th>\n","      <th>ENVSAT</th>\n","      <th>POSSAT</th>\n","      <th>FINSTR</th>\n","      <th>...</th>\n","      <th>ANXI</th>\n","      <th>DEPRI</th>\n","      <th>ABUSED</th>\n","      <th>CHEAT</th>\n","      <th>THREAT</th>\n","      <th>SUICIDE</th>\n","      <th>INFER</th>\n","      <th>CONFLICT</th>\n","      <th>LOST</th>\n","      <th>DEPRESSED</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>...</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 31 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b3dfed3-114e-418a-b4d4-320f68d0a6c3')\"\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 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\"RESDPL\",\"PHYEX\",\"AVGSLP\",\"TSSN\",\"WRKPRE\"]]\n","df_feat.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"iSQK-D6MZ00V","executionInfo":{"status":"ok","timestamp":1660906182787,"user_tz":-120,"elapsed":249,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"fed14997-d047-4cb0-f072-78e329195392"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  AGERNG            EDU                      PROF     MARSTS RESDPL  \\\n","0  26-30  Post Graduate                Unemployed  Unmarried   Town   \n","1  26-30  Post Graduate  Service holder (Private)  Unmarried   City   \n","2  21-25            HSC                   Student  Unmarried   City   \n","3  16-20            HSC                   Student  Unmarried   City   \n","4  21-25       Graduate                   Student  Unmarried   Town   \n","\n","       PHYEX             AVGSLP              TSSN       WRKPRE  \n","0  Sometimes  More than 8 hours   2-4 hours a day  No Pressure  \n","1      Never            6 hours   5-7 hours a day     Moderate  \n","2      Never            8 hours  8-10 hours a day         Mild  \n","3  Sometimes            7 hours   2-4 hours a day       Severe  \n","4      Never            7 hours   5-7 hours a day         Mild  "],"text/html":["\n","  <div id=\"df-df025899-be5e-4c2f-a213-a070b4206387\">\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>AGERNG</th>\n","      <th>EDU</th>\n","      <th>PROF</th>\n","      <th>MARSTS</th>\n","      <th>RESDPL</th>\n","      <th>PHYEX</th>\n","      <th>AVGSLP</th>\n","      <th>TSSN</th>\n","      <th>WRKPRE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>26-30</td>\n","      <td>Post Graduate</td>\n","      <td>Unemployed</td>\n","      <td>Unmarried</td>\n","      <td>Town</td>\n","      <td>Sometimes</td>\n","      <td>More than 8 hours</td>\n","      <td>2-4 hours a day</td>\n","      <td>No Pressure</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>26-30</td>\n","      <td>Post Graduate</td>\n","      <td>Service holder (Private)</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>Never</td>\n","      <td>6 hours</td>\n","      <td>5-7 hours a day</td>\n","      <td>Moderate</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>21-25</td>\n","      <td>HSC</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>Never</td>\n","      <td>8 hours</td>\n","      <td>8-10 hours a day</td>\n","      <td>Mild</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>16-20</td>\n","      <td>HSC</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>City</td>\n","      <td>Sometimes</td>\n","      <td>7 hours</td>\n","      <td>2-4 hours a day</td>\n","      <td>Severe</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>21-25</td>\n","      <td>Graduate</td>\n","      <td>Student</td>\n","      <td>Unmarried</td>\n","      <td>Town</td>\n","      <td>Never</td>\n","      <td>7 hours</td>\n","      <td>5-7 hours a day</td>\n","      <td>Mild</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df025899-be5e-4c2f-a213-a070b4206387')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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random_state=42)\n","X_train.shape, X_test.shape, y_train.shape, y_test.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jwMSfza4nYt9","executionInfo":{"status":"ok","timestamp":1660897965636,"user_tz":-120,"elapsed":8,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"e887bdc8-e746-4f52-d5f4-1bb42d28ae16"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((483, 65), (121, 65), (483,), (121,))"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import GradientBoostingClassifier\n","\n","logreg = LogisticRegression(C=1, random_state=42)\n","logreg.get_params"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BOv-6PuuOpwZ","executionInfo":{"status":"ok","timestamp":1660902390086,"user_tz":-120,"elapsed":254,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"8febab45-e9b1-4adc-f5b8-4dccd43f80bc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<bound method BaseEstimator.get_params of LogisticRegression(C=1, random_state=42)>"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":[],"metadata":{"id":"sVXaeDcFf2DW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import GridSearchCV\n","\n","param_grid = [{\n","    'logisticregression__C': [0.01, 0.1, 1.0, 10, 100],\n","    'logisticregression__penalty': [\"l2\", \"none\"], #elasticnet is not supported by the default lbfgs solver\n","    'logisticregression__multi_class': [\"auto\", \"ovr\"],\n","\n","    'gradientboostingclassifier__n_estimators': [50, 100],\n","    'gradientboostingclassifier__learning_rate': [0.05, 0.1, 0.2],\n","    'gradientboostingclassifier__max_depth': [3, 4, 5]\n","    }]"],"metadata":{"id":"MM3FLrA0fBFo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Scaling numeric values\n","from sklearn.preprocessing import StandardScaler\n","# Machine Learning algorithm\n","from sklearn.linear_model import LogisticRegression\n","# Evaluating\n","from sklearn.metrics import classification_report, confusion_matrix, plot_confusion_matrix\n","\n","from sklearn.pipeline import make_pipeline\n","\n","scaler = StandardScaler()\n","logreg = LogisticRegression(random_state=42)\n","\n","pipe = make_pipeline(scaler, logreg)"],"metadata":{"id":"QRXVWAp-n1vv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pipe.fit(X_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ktPgVMxioIRs","executionInfo":{"status":"ok","timestamp":1660871267691,"user_tz":-120,"elapsed":198,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"5f9967b5-ec1e-44a2-d323-447dee2b4389"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Pipeline(steps=[('standardscaler', StandardScaler()),\n","                ('logisticregression', LogisticRegression(random_state=42))])"]},"metadata":{},"execution_count":50}]},{"cell_type":"code","source":["predictions = pipe.predict(X_test)\n","print(classification_report(y_test, predictions))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4AikuVG4pGVW","executionInfo":{"status":"ok","timestamp":1660871296974,"user_tz":-120,"elapsed":197,"user":{"displayName":"Jeroen Van Sweeveldt","userId":"07747401850940922729"}},"outputId":"0a44186e-f337-40fe-929e-f91e8d73c428"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.87      0.89      0.88        44\n","           1       0.93      0.92      0.93        77\n","\n","    accuracy                           0.91       121\n","   macro avg       0.90      0.90      0.90       121\n","weighted avg       0.91      0.91      0.91       121\n","\n"]}]}]}