Implementation of classical machine learning algorithms for learning purposes

Web app for used car price predictions

Code for the definition and testing of three new fairness-aware algorithms: Fair Decision Tree, Fair Genetic Pruning, and Fair LightGBM (FDT, FGP, FLGBM), completed for my Master's thesis.

Machine learning algorithms for many-body quantum systems

This is the repository that cover all the thing related to machine learning that i covered . This repository is bascially a collection from a lot of resouce and any person using this can have a clear idea about machine learning . I have covered all the type of mahcine learning supervised , unsupervised ,etc .

A deep learning agent to play Sekiro: Shadow die twice and possibly other video games

Real-time explainable machine learning for business optimisation

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

A collection of information for quantitative finance, insurance, mathematics and AI

Estimate future qualifying results using ML

PyMilo: Python for ML I/O

EasyTPP: Towards Open Benchmarking Temporal Point Processes

Here are 50 Python-related algorithms and programming problems that focus on data science. Solving these will help you develop the skills needed for data manipulation, algorithm design, and basic machine learning tasks

A curated list of awesome Machine Learning frameworks, libraries and software. With repository stars⭐ and forks🍴

Contains my project code for two CNN models, one trained for binary classification while the other made for multi-class classification. It utillises the CIFAR-10 dataset.

The Loan Default Prediction App is a robust web-based tool developed to assess the risk of loan default by analyzing borrower-specific features using machine learning algorithms. Built on a Random Forest model, this app enables financial institutions and credit analysts to make data-driven decisions when evaluating loan applications.

– Implemented ML algorithms for soil moisture analysis, improving prediction accuracy by 25%. – Developed AI-based irrigation models, cutting water usage by 20% and boosting crop yield. – Integrated IoT sensors for real-time data collection, enhancing data efficiency and response time.