Personal Projects
Sticky Notes
A web application developed using Django, HTML, and CSS that allows users to post blogs and share stories related to Machine Learning and Data Science topics.
- Built with Django framework for backend functionality
- Created responsive UI using HTML and CSS
- Implemented user authentication and authorization
- Designed database schema for storing and retrieving blog posts
ChatQwen
A web application built with Streamlit, serving as a personal ChatGPT for answering basic coding and general queries. It leverages an LLM model fine-tuned on a Python queries dataset to provide accurate and context-specific responses.
- Developed using Streamlit for quick and effective UI development
- Integrated fine-tuned LLM model for answering coding queries
- Implemented context-based response generation
- Optimized for handling Python-specific programming questions
Heart Disease Prediction
A project developed using Tkinter, scikit-learn, pandas, and Matplotlib to predict whether a patient has heart disease based on their current medical details.
- Built GUI interface with Tkinter for easy data input
- Implemented machine learning models using scikit-learn
- Performed data analysis with pandas for preprocessing
- Created visualizations using Matplotlib to present results
Projects
Crop and Fertilizer Recommendation System
An ML-powered web application developed using Flask, HTML, CSS, scikit-learn, pandas, and MySQL. Users can input specific data, and the system provides recommendations for suitable crops and fertilizers based on the input parameters.
- Developed full-stack application with Flask backend and HTML/CSS frontend
- Implemented machine learning algorithms using scikit-learn for accurate recommendations
- Created a MySQL database for storing crop and fertilizer data
- Designed user-friendly interface for data input and result visualization
MCQs Generation using NLP
An NLP project developed using Django, Transformers, and a fine-tuned LLM. This web application allows users to input text and generate multiple-choice questions (MCQs) along with distractors based on the provided content.
- Built with Django framework for robust backend functionality
- Integrated Transformer models for NLP processing
- Implemented fine-tuned LLM for question generation
- Created algorithms for generating relevant distractors
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