Decision Tree classifier

The Decision Tree Classifier was employed to predict the values in the "Healthy" column of a dataset consisting of four categorical columns ('Food preference', 'Smoker?', 'Living in?', 'Any hereditary condition?') and eleven numerical columns ('Specific ailments', 'Age', 'BMI', 'Follow Diet', 'Physical activity', 'Regular sleeping hours', 'Alcohol consumption', 'Social interaction', 'Taking supplements', 'Mental health management', 'Illness count last year'). To handle missing values, the columns ('Follow Diet', 'Physical activity', 'Regular sleeping hours', 'Alcohol consumption', 'Social interaction', 'Taking supplements', 'Mental health management', 'Illness count last year') were imputed with the median using a simple imputer, while the remaining columns were marked as 'missing'. The Decision Tree Classifier is a popular and interpretable machine learning algorithm that builds a tree-like model of decisions and their consequences. It splits the data based on the features' values, resulting in a hierarchical structure where the root node represents the most significant feature. Each subsequent node represents a feature split, leading to a leaf node that represents the predicted class label. The predictions made by the Decision Tree Classifier were saved in a CSV file named "submission.csv." This file contains the predicted values for the "Healthy" column based on the input data and the trained Decision Tree Classifier model.