Machine Learning

Loan Defaults prediction

This project, part of the Coursera Data Science Coding Challenge, aims to predict loan defaults based on various borrower-specific features. Understanding the patterns and correlations in the data allows lenders to better predict and mitigate potential loan defaults, ensuring a healthier portfolio and risk management.

Client: Coursera Data Science Coding Challenge
Role: Data Scientist

The Problem:

The task was to build a predictive model using a dataset that contained various borrower-specific features, such as their income, credit score, employment duration, and more. The goal? Predict if they would default on their loan.

My Achievement:

I’m elated to share that my solution ranked in the top 30% of all submissions. A considerable achievement given the complexity and competition!

Approach

Undertook the Coursera Data Science Coding Challenge; it was a highly educational and thrilling experience.
Emphasized the need for a systematic method, which involves:
- Initial data comprehension.
- Detailed data preprocessing.
- Conducting trials with various models.
Developed scikit-learn pipelines for efficient data processing.
Utilized a grid search strategy to identify the optimal model and fine-tune the hyper-parameters.
Tools Used:
Python
scikit-learn

Skills Developed:

Machine Learning
Python Programming

The GitHub Repository

I’ve meticulously documented my entire journey and the code on GitHub. For those curious to dive deeper, here’s the link to the repository.

Python Development

mlskeleton, the open-source python package

mlskeleton, the open-source python package, can help you create a professional and organized folder structure for your machine learning projects and streamline your workflow.