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Train a Decision Tree based on provided dataset.
Preprocess the dataset by handling missing values and encoding categorical variables.
Split the dataset into training and testing sets.
Train the Decision Tree model on the training set.
Evaluate the model's performance on the testing set using metrics like accuracy or F1 score.
Feature selection can be done using techniques like filter methods, wrapper methods, and embedded methods.
Filter methods involve selecting features based on statistical measures like correlation, chi-squared test, etc.
Wrapper methods use a specific machine learning algorithm to evaluate the importance of features through iterative selection.
Embedded methods incorporate feature selection within the model training proces...
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