My Projects
My Projects
Here are some of the key projects I’ve worked on (or am currently developing), showcasing my skills in data science, machine learning, and AI-powered applications:
Here are some of the key projects I’ve worked on (or am currently developing), showcasing my skills in data science, machine learning, and AI-powered applications:
1. Smart Retail Analytics Dashboard
1. Smart Retail Analytics Dashboard
Technologies: Python, Pandas, Plotly, Dash, Scikit-learn Developed a full-stack data dashboard that helps retail businesses understand customer behavior, sales patterns, and stock performance. Integrated machine learning models to forecast demand and identify underperforming products. Key Features: Real-time sales tracking and product analytics Predictive model for inventory management Interactive dashboard with filtering and data drill-downs
Technologies: Python, Pandas, Plotly, Dash, Scikit-learn Developed a full-stack data dashboard that helps retail businesses understand customer behavior, sales patterns, and stock performance. Integrated machine learning models to forecast demand and identify underperforming products. Key Features: Real-time sales tracking and product analytics Predictive model for inventory management Interactive dashboard with filtering and data drill-downs
2. Mental Health Detection Using NLP
2. Mental Health Detection Using NLP
Technologies: Python, TensorFlow, NLTK, FastAPI Created an AI-powered web app that analyzes text input (such as journal entries or social media posts) to detect early signs of anxiety or depression using natural language processing and sentiment analysis. Key Features: Trained on anonymized mental health datasets Text classification using LSTM-based deep learning REST API for integration with external platforms
Technologies: Python, TensorFlow, NLTK, FastAPI Created an AI-powered web app that analyzes text input (such as journal entries or social media posts) to detect early signs of anxiety or depression using natural language processing and sentiment analysis. Key Features: Trained on anonymized mental health datasets Text classification using LSTM-based deep learning REST API for integration with external platforms
3. AI Chatbot for Academic Assistance
3. AI Chatbot for Academic Assistance
Technologies: Python, Transformers (Hugging Face), Flask, OpenAI GPT Built a virtual assistant that helps students understand complex engineering topics, especially in mathematics, physics, and computer science. Fine-tuned language models to deliver concise and relevant academic answers. Key Features: Natural language understanding and contextual memory Support for equations and coding questions Integrated into a minimalist web UI
Technologies: Python, Transformers (Hugging Face), Flask, OpenAI GPT Built a virtual assistant that helps students understand complex engineering topics, especially in mathematics, physics, and computer science. Fine-tuned language models to deliver concise and relevant academic answers. Key Features: Natural language understanding and contextual memory Support for equations and coding questions Integrated into a minimalist web UI
4. Satellite Image Classifier for Agriculture
4. Satellite Image Classifier for Agriculture
Technologies: Python, TensorFlow, OpenCV, Keras, GeoPandas Designed a deep learning pipeline that classifies satellite images to detect crop types and monitor agricultural health. The project aims to support smart farming practices in rural regions. Key Features: CNN model trained on multispectral image data Preprocessing pipeline with NDVI vegetation indices Results visualized on interactive maps
Technologies: Python, TensorFlow, OpenCV, Keras, GeoPandas Designed a deep learning pipeline that classifies satellite images to detect crop types and monitor agricultural health. The project aims to support smart farming practices in rural regions. Key Features: CNN model trained on multispectral image data Preprocessing pipeline with NDVI vegetation indices Results visualized on interactive maps