A selection of my work in Machine Learning, NLP, and Data Analytics. Each project highlights the problem, my approach, tools used, and results.

🧠 Memory Support Chatbot for Pregnant Women

  • Problem: Pregnant women can experience cognitive memory issues and need guidance.
  • Approach: Developed a GPT-2-based chatbot to provide conversational support.
  • Deployment: Streamlit and Gradio
  • Technologies: Python GPT-2 Streamlit Gradio
  • Results: Provided a user-friendly interface to assist memory-related questions. View on GitHub

🥗 Food Choices Streamlit App

  • Problem: Understanding food preferences of college students for better nutrition insights.
  • Approach: Built an interactive Streamlit app to collect, analyze, and visualize food choice data.
  • Dataset: “Food choices and preferences of college students” (126 responses, Kaggle)
  • Technologies: Python Streamlit Pandas
  • Results: Allowed users to explore patterns and trends in food preferences interactively. View on GitHub

🏠 Housing Price Prediction

  • Problem: Predicting home prices to support buyers and sellers with informed decisions.
  • Approach: Built a Random Forest regression model with EDA and feature engineering.
  • Technologies: Python Scikit-learn Pandas Matplotlib
  • Results: Accurate predictions for housing prices based on key features. View on GitHub

❓Question Answering System

  • Problem: Extract answers from large text datasets efficiently.
  • Approach: Fine-tuned a Hugging Face transformer model on the SQuAD dataset for extractive QA.
  • Deployment: Gradio
  • Technologies: Python Hugging Face Gradio
  • Results: Enabled fast and accurate question answering from textual content. View on GitHub

✨ For more projects, visit my GitHub repositories