Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
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Updated
Jul 18, 2024 - Python
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
Generate predictive model using supervised learning method to enhanced coupon acceptance rate using python.
In this project, we have to create a predictive model which allows the company to maximize the profit of the next marketing campaign
WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.
Explainable Landscape-Aware Optimization Performance Prediction
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.
This project was developed during the course Laboratory of Computational Physics
Experimenting with SHAP values to explain how a given Machine Learning model works.
Modelo de boosting que a partir de los datos del usuario de una fintech predice si activaría la tarjeta de debito que ofrece la misma empresa, y en cuantos dias lo haría
Loan-Default-Prediction
XAI analytics to understand the working of SHAP values
Financial distress prediction from Kaggle
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
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.
Repo for Manzano Analytics HTML website
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.
Prediction if patients with symptoms have COVID-19 based on clinical variables (blood related variables, urine related variables, age, etc)
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