🧠 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 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.

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.

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

Brain Tumor Classification

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

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.