🧠 Deep learning project for brain tumor classification using MRI images. Built with transfer learning (VGG16 + fine-tuning), TensorFlow/Keras, and deployed via Streamlit. Dataset & model loaded dynamically from KaggleHub. Includes training notebook, evaluation, and interactive web app.

This is project, developed a deep learning model using Tensorflow and CNNs to classify images into multi animal categories with high accuracy. Preprocessed image data, optimized model architecture and achieved effective generalization on unseen data.

This repository contains a Logistic Regression pipeline for predicting heart disease using a well-known Kaggle dataset. The project walks through data preprocessing, model training, evaluation, and improvements.

a modular python pipeline for analyzing, visualizing and predicting trends in synthetic car sales data

A content-based recommendation engine using LLMs, LangChain, Hugging Face, Gradio, and Python to generate vectorized book representations for semantic similarity matching, enabling personalized and context-aware book recommendations.

This repository is to experiment the integration with @ggml-org/whisper.cpp for offline STT + pyannote/speaker-diarization-3.1

Brain Tumor Classification

AI-powered plant disease detection system using CNN deep learning. Identifies 38+ crop diseases from leaf images with high accuracy. Built with TensorFlow/Keras and Streamlit for real-time agricultural diagnostics.

This project implements RAG (Retrieval-Augmented Generation) and LLM-based models on a Kaggle book dataset to build a recommendation system. Users can search and receive book suggestions tailored to their reading expectations.

This project aims to build and evaluate machine learning models to predict heart failure based on patient data. The project includes the implementation of 6 machine learning algorithms.

A custom wrapper on `kagglehub` for streamline usability.