Daniel Lesser, Joseph Standerfer, Ghazal Erfani Machine Learning and Problem Solving Case Study

A joint project to explore microloans from LendingTree. The data went through three phases of work: exploratory, cleaning, and machine learning algorithms. Classification techniques were used to identify whether loans were likely to default or not. Regression models were used to predict returns on loans.

Getting Started

This project requires you to install Anaconda 3.7 and make use of Jupyter Notebooks for Python. All packages should be installed based on the import statements at the top of the IPYNB file. All Python work is saved in Phase3/CS-Phase 3-Rev4.ipynb.

The data is too large to load into github, so please reach out to the author of this repository for access.

Built With

• Anaconda 3.7
• Python Jupyter Notebooks

Authors

Daniel Lesser, Joseph Standerfer, Ghazal Erfani, Carnegie Mellon University Machine Learning and Problem Solving Spring 2019

Acknowledgments

• Carnegie Mellon University Machine Learning and Problem Solving Spring 2019
• Leman Akoglu
• Shubhranshu Shekhar