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

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

Financial distress prediction from Kaggle

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.

Classification model using Gradient Goosted Trees with feature selection and explainability

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

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

Repo for Manzano Analytics HTML website

ML-solution of the case of the District hackathon Leaders of Digital 2023. The task was to predict accidents (accidents, pipe ruptures, fires) based on the weather forecast for each of the urban districts. Gradient boosting (macro f1), cross-validation, shap values.

Loan-Default-Prediction

This project was developed during the course Laboratory of Computational Physics

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

Predicción del precio de venta de las viviendas en venta y de las viviendas en alquiler de Barcelona.

Explainable Landscape-Aware Optimization Performance Prediction

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

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

Android malware detection using machine learning.

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.

🐍 Mental Maps Related to Contents in Data Science 🐍