Predicting the Likelihood to Purchase a Financial Product Following a Direct Marketing Campaign

A data-driven tool to identify the best candidates for a marketing campaign and optimize it.

Predictive State Propensity Subclassification (PSPS): A causal deep learning algoritm in TensorFlow keras

Propensity model to predict a customer's likelihood of purchasing a product from an online store based on past behaviour

A repo with functions for building various COMs and GCOMs quickly.

Propensity Modelling and RFM Analysis to predict users' likelihood of making a purchase.

The feature of interest is whether or not a customer buys a caravan insurance, based on socio-demographic factors and ownership of other insurance policies; and to build profile of a typical customer.

Website Traffic & Conversion Prediction: Regression and churn analysis for a UK heat pump company to optimize the conversion funnel using user behavior data.

ML model to predict which customers are likely to purchase a Mutual Fund

Sales prediction for a segment of product.

Predicting customer conversion likelihood for bank term deposit campaigns using a calibrated propensity model to optimise telemarketing outreach.

Explores the relationship between population demographics, various crime rates, and shall carry gun laws across different regions of the United States between 1977-1999 using a propensity weighted mixed linear effects model.

Propensity modeling project for ShopNow to predict 30‑day purchase likelihood, compare Ridge/Lasso/Random Forest/XGBoost, and optimize marketing profit using a cost‑based evaluation framework.

A predictive analytics project developed during my MSc AI at BSBI. This project implements a binary classification pipeline to predict customer purchase behavior based on demographic and interaction data. It covers the full ML lifecycle: from Exploratory Data Analysis (EDA) and feature engineering to model selection and performance benchmarking.

Customer response modeling, segmentation, and propensity-based campaign targeting strategy.

A self-hosted BigQuery ML pipeline that predicts purchase propensity from GA4 events and pushes the result back to GA4 as a user property via the Measurement Protocol. Built for Google Ads remarketing. Consent-aware, cost-capped, and production-hardened.

Causal inference analysis using Propensity Score Matching to measure competitor impact on store sales. Isolates true treatment effect from confounding factors with statistical rigor.