Model interpretability and understanding for PyTorch

SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.

Beta Machine Learning Toolkit

⛳️ This is the final project within the course Sports Analytics, TDDE64, at Linköping University which analyzes the impact of various golf performance metrics on player outcomes using machine learning techniques and regression analysis

Prediction of students' dropout using classification models. Data visualisation, feature selection, dimensionality reduction, model selection and interpretation, parameters tuning.

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

Malicious URL detector built with deep exploration on feature engineering.

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

The repository contains comprehensive assessment reports and Jupyter Notebook files aimed at addressing key questions related to predicting wireless churn and identifying the features driving churn.

Content: Root node, Decision node & Leaf nodes, Attribute Selection Measure (ASM), Feature Importance (Information Gain), Gini index

ML modeling and feature importance analysis conducted to identify/inform company practices related work interference due to mental health.

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.

Analyzing Fater company's diaper market potential and enhancing revenue estimation for Naples stores: A Socio-Demographic, Territorial, and Points of Interest Perspective

[TNNLS 2022] Significance tests of feature relevance for a black-box learner

Sector based classification with feature engineering and tsfresh. Looking 3 months momentum of stocks.

House-Price-Prediction-App

A Python Package that computes Target Permutation Importances (Null Importances) of a machine learning model.

This repository contains a Decision Tree Regression model developed to predict house sale prices based on various predictor variables, aiming to provide accurate predictions and insights into regional differences in real estate values.

An exploratory analysis of Chicago community areas.