Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

In this project, we have to create a predictive model which allows the company to maximize the profit of the next marketing campaign

🐍 Mental Maps Related to Contents in Data Science 🐍

Github Repository for the paper "Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study" - BIBM 2023

Android malware detection using machine learning.

The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.

This project uses Explainable AI (XAI) to interpret machine learning models for diagnosing faults in industrial bearings. By applying SVM and kNN models and leveraging SHAP values, it enhances the transparency and reliability of machine learning in industrial condition monitoring.

Prediction if patients with symptoms have COVID-19 based on clinical variables (blood related variables, urine related variables, age, etc)

WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.

This project was developed during the course Laboratory of Computational Physics

Experimenting with SHAP values to explain how a given Machine Learning model works.

Repo for Manzano Analytics HTML website

Explainable Landscape-Aware Optimization Performance Prediction

Jantahack : BigMart Sales Prediction using LGBM Regressor and Model interpretation using SHAP

Generate predictive model using supervised learning method to enhanced coupon acceptance rate using python.

XAI analytics to understand the working of SHAP values and applying it to the breast cancer dataset to get the explanation behind the predictions made.