👨‍💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛

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

Foursquare - Location Matching part of 53/1079 solution

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

We've developed a powerful binary dog and cat image classifier, driven by advanced deep learning techniques, and enhanced its transparency using Local Interpretable Model-agnostic Explanations (LIME). Witness the magic as the model accurately predicts dog and cat images while LIME reveals the intricate decision-making process behind each result.

Developed an efficient system to empower retailers with profitable insights & maintain a competitive edge in the dynamic retail industry.

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.

Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.

A machine learning implementation of an interpretable model for membrane separation performance prediction of COFs materials.

Semana omniStack 7.0 #rocketseat [ back-end ]

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.

Football Positions: A Multi-class Classification Problem

Предсказание стоимости автомобилей.