a DUMMY for the SHAB theme wich will be there

Explainable prediction of next year GDP Growth using the Kaggle World Development Indicators

Built an ensemble model on the Spotify dataset to determine the popularity of songs and study feature importance using SHAP.

This project explores the Framingham Heart disease dataset with the objective to predict its risk in 10 years. Various methods for handling missing values and outliers are explored as iterations. After analysing the dataset, important and necessary features are selected. Seven ML models are implemented, with evaluation on the basis of Test Recall.

A project in which a model has been developed that predicts which passengers of the Titanic have the greatest opportunity to survive after the disaster

This repository contains the code for machine learning models designed to predict the outcomes of horse races, with SHAP (SHapley Additive exPlanations) interpretation incorporated for enhanced model interpretability.

This project dives deep into customer sales data to uncover valuable insights for business decision-making. It leverages machine learning and time-series forecasting to predict customer churn, forecast product demand, and segment customers based on their purchasing behavior.

Semana omniStack 7.0 #rocketseat [ back-end ]

A take on highly imbalanced fraud classification using permutation importance to select top features and explaining the model using SHAP.

Final Year Project KCL

Exploration of SHAP visualisations with Keras Multi Layered Perceptron (MLP), Classifiers, and Regresors using seaborn datasets.

Through exploratory data analysis, predictive analytics and explainable AI, this project aims to provide valuable feedback regarding the reasons that customers churn, thus providing useful insight for the company to minimize customer churn.

Streamlit dashboard frontend (user interface) to deploy a machine learning model to the web

Car dealership web application that is enhanced with online machine learning and interpretable machine learning.

This a classic Credit Card Default Prediction project where based on customer profile we want to predict whether the borrower is likely to default in the next 2 years or not having a delinquency of more than 3 months.

Предсказание стоимости автомобилей.

Replicate Power BI Key Influencer visual in python

Multi-class classification of drug resistance in MTB clinical isolates

Estudo transversal que analisou dados retrospectivos de gestantes e puérperas com diagnóstico de Síndrome Respiratória Aguda Grave (SRAG) entre janeiro de 2016 e novembro de 2021.

This project provides a performance evaluation of credit card default prediction. Thus different models are used to test the variable in predicting the credit default and we found Random Forest Classifier performs the best with a recall of 0.95 on the test set.