Loan-Default-Prediction

Using machine learning, predict which companies will default on their loans and explain how different features impact the predictions.

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Context

• This was a take home assessment for a job interview.

Steps

• Data processing with special focus on handling class imbalance with SMOTE and Random Undersampling
• Exploratory data analysis
• Feature engineering
• Model building and evaluation. Select appropriate performance metric for binary classification task i.e. F2 score (more importance for recall)
• Model tuning and optimal threshold identification
• Compare performance of models
• Feature Importance
• Test data prediction

Files/Folders

• code.ipynb: Notebook for the complete ML pipeline
• data: Folder containing raw train and test data
• output.csv: output of test predictions