Developed a generalized conflict management framework for multi-channel power control in O-RAN xApps. The project implements DRL-based solutions for optimizing network power control and energy efficiency, featuring an evaluation framework using Network Digital Twin (NDT) that achieved significant energy savings.

This project implements a Deep Q-Network (DQN) to train an AI agent to play the classic Snake game. The agent learns to play the game by interacting with the environment, observing the state, and learning from rewards and penalties. The implementation uses PyTorch for the neural network, Gym for the environment interface, and Pygame for rendering the game.

Developed and analyzed heart disease prediction models using Python and R, interpreting decision trees for actionable insights. Integrated Prolog for knowledge representation and created a Java interface for user-friendly predictions.

This repository showcases an extensive exploration of used car prices using the detailed 'autos.csv' dataset. Through comprehensive data analysis, it investigates the impact of car features such as make, model, year, mileage, and price, providing valuable insights for both buyers and sellers in the used car market.

A Python-based machine learning project for identifying and filtering spam emails using a Naive Bayes Classifier. The classifier is trained on a labeled dataset to distinguish between spam and legitimate emails, achieving high accuracy and precision in predictions.

Explore Data Analysis for key insights from three pivotal papers on R-based data analysis techniques. This project delves into statistical methods, data visualization, and model building using R, providing a comprehensive understanding of advanced data analysis practices.