Muhammad Aun Ali

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Resume Analyzer is a prototype web application that allows users to upload multiple resumes and compare them against a job description using vectorization and cosine similarity. The project is built using Python, Flask, and scikit-learn.

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An AI-powered chatbot that answers questions about the Constitution of Pakistan using RAG (Retrieval Augmented Generation) with Pinecone vector database and Google Gemini.

The system is implemented as a fully automated, serverless pipeline designed for continuous AQI prediction and model improvement.

Developed a prototype of smart glasses aimed at supporting blind and visually impaired individuals through real time object detection and text reading capabilities.

Electricity theft is a major issue in regions like Karachi, where unauthorized consumption of electricity leads to significant losses for utility companies. This project provides a solution for detecting electricity theft using machine learning models. By analyzing various factors such as electricity usage, voltage fluctuations, and historical data

A simple desktop application built using Python and Tkinter that lets you convert between popular currencies in real time using RapidAPI's Currency Converter API.

I and my team member has created this MediBot Fine Tune on Medical Question/Answer Dataset From Hugging Face

The Laptop Price Predictor, a web-based application developed using Streamlit. This tool leverages advanced machine learning algorithms to estimate the market price for laptops

A prototype RAG-based AI project that allows cricket fans to explore and ask questions about previous editions of the Champions Trophy using PDFs as data. Built using ChromaDB and Hugging Face models, this solution enables real-time querying without retraining the model — combining fast retrieval with powerful generative responses.

"Developed a machine learning model to predict stock prices using historical data and market indicators. Leveraged techniques like time series analysis and regression. Utilized Python libraries for data preprocessing, model training, and evaluation, achieving reliable accuracy for informed investment decisions."

"Developed a machine learning model to predict stock prices based on historical data and key market indicators. Implemented time series analysis, regression models, and deep learning algorithms to identify trends and forecast future prices. Utilized Python libraries like pandas, scikit-learn, and TensorFlow for data preprocessing, model training