Telco Customer Churn

Used Machine Learning models to find out which customers would churn.

  • Tools used: Python, XGBoost, CatBoost
  • Category: Classification, Supervised Learning
  • Model Score: 0.80
  • Year: 2023

Credit Default Prediction

Built a user-friendly Python app that shows predicted defaulters, as well as characteristics visualisations.

  • Tools used: Python, XGBoost, LightGBM, Logistic Regression, CatBoost, Streamlit
  • Category: Classification, Supervised Learning, Machine Learning deployment
  • Model Score: 0.87
  • Year: 2023

Customer Segmentation Prediction

Using K-Means Clustering, customers without labels are grouped. A user-friendly app is made showing the characteristics of each group.

  • Tools used: Python, K-Means, Streamlit
  • Category: Classification, Unsupervised Learning, Machine Learning deployment
  • Model Score: 0.84
  • Year: 2023

Breast Cancer Prediction

Created a simple Neural Networks model predcting cancer status.

  • Tools used: Python, Keras Sequential Model
  • Category: Classification, Supervised Learning
  • Model Score: 0.96
  • Year: 2023
Photo credit: Google