AI & ML Projects

This report presents the implementation of Cooperative Multi-Agent Deep Reinforcement Learning using the MADDPG algorithm in a virtual tennis environment. The objective was to train two agents to work together and keep a ball in play over a net. The agents controlled rackets and received rewards for successful hits and penalties for misses. Through the utilization of neural networks and advanced techniques such as experience replay and target networks, the agents were able to achieve the desired average score, demonstrating the effectiveness of MADDPG in training agents to collaborate in complex environments.

The Railway Chatbot project involves the development of an intelligent chatbot system for a railway website. The chatbot utilizes web scraping to gather data and offers customer support by providing answers to queries and frequently asked questions (FAQs). It also recommends the most affordable train options for specific destinations. The project aims to enhance the user experience by streamlining customer support services and delivering accurate and up-to-date information.

The Medical Trial Chatbot project develops an intelligent chatbot system for medical trials. It determines user eligibility and provides comprehensive trial information using natural language processing (NLP). It streamlines participant screening, facilitates recruitment, and advances healthcare research.

The Python script enables PDF Question Answering using BERT embeddings. It extracts text from a PDF file, preprocesses it using spaCy and BERT tokenizer, generates sentence embeddings with a pre-trained BERT model, and allows users to input queries related to the document. The script finds similar sentences to the query and utilizes the OpenAI API to provide answers based on the most relevant sentences.

This project aims to develop a machine learning-based system for detecting anomalies in network traffic data within a Docker environment. By analyzing diverse metrics such as CPU usage, throughput, request latency, memory usage, and limit, the system can identify abnormal patterns and potential security breaches or network failures. The project utilizes the Isolation Forest Algorithm to isolate anomalies and provides network administrators and security teams with valuable analysis for early detection and mitigation of network anomalies.