Amex-Default-Prediction (from kaggle)

This project aims to use Machine Learning methods to predict the Default rate of customers at American Express. The project is from Kaggle platform, which can be found at: https://www.kaggle.com/competitions/amex-default-prediction.

Contents:

1. Data Compression: Compress the dataset from 16GB --> 2GB;

2. Exploratory Data Analysis

3. Machine Learning Techniques:

• Feature Preprocessing:

  • Feature Engineering: Aggregate the data from Transaction level --> Customer_ID level(each customer_id has many transactions);
  • Feature Selection: Reduce the feature set from 940 features to 300 features;
  • Feature Transformation: Imputing and Encoding

• Model Training:

  • Trained LightGBM using Bayesian Optimization method;
  • Model Calibration

The Original dataset can be found in the Kaggle project link, and the Preprocessed dataset(trained on models) can be found at: https://www.kaggle.com/datasets/christal559/aml4995