Loan's Approval Prediction

• Implement a classification model to predict whether or not a loan application is approved using Decision Tree Classifier
• Explore various techniques for preventing overfitting in decision trees
• Apply undersampling and ensemble methods (bagging, boosting) to combat the imbalanced dataset
• Find out the best parameters for each classifier with gridsearch
• Compare the optimized models with baseline models
• Evaluate models with accuracy score, precision, recall and F1-score

Steps

• Load dataset into pandas dataframe
• Explore features and select the relavant ones to train model
• One hot encoding categorical features into numeric
• Analysis the proportion of safe and risky loan in dataset
• Undersampling train data/validation data to make sure data is balance, then train Decision Tree Classifier (baseline, optimized parameters) and evaluation
• Boosting: The motivation is to combine several weak models to produce a powerful ensemble. Train Decision Tree Classifier wrapped in AdaBoostClassifier and evaluation
• Bagging Classifier: Fit train data to Decision Tree Classifier wrapped in BaggingClassifier

Requirements:

Softwares:

• Python >= 3.7
• Jupyter Notebook

Dependencies

• pandas
• numpy
• scikit-learn

Reference:

Machine Learning Specialization by University of Washington (Coursera)