Implementation of deep learning and Causal ML approaches to predict customer churn on dummy dataset.
Project aimed at differentiating between positive and negative reviews using the fastai's ULMFiT implementation method.
Interact with the data from two hotels in Portugal to see the affect of altering customer markets and lead time on cancellation rates.
A repository of projects completed through the Springboard career track program.
Predicted rider retention for a taxi service and identified most significant factors that contributed to it. Achieved an 80% accuracy with a catboost model, which was chosen for its interpretability.
Identified the most important factors contributing to user adoption for a product. Achieved a 96% accuracy with an SVM model, and found user length and opting into the mailing list as the most significant predictors.
Recommendation engine with a .97 AUC achieved using clustering techniques to create user features. Data represents Olist marketplace transactions and was retrieved from kaggle.com.
Created prediction algorithm for determining if a customer will cancel at the moment of booking. After eliminating numerous data leakage sources, we achieved a 90% AUROC with a catboost classification model.