This project repository hosts notebook, manifests and guides to deploy dog breed classification Machine Learning Web Application on Google Kubernetes Engine (GKE) Autopilot.

A repo Containing Colab Notebooks to temporarily Host Web Apps in Colab

Movie Recommendation Engine with Python,Jupyter Notebook & Pandas

Personal projects on various topics

This project is made to find Price of Used Cars by Various Factors made in Jupyter Notebook

The Weather Trend Analyzer is a data analysis project designed to explore and visualize weather trends using Jupyter Notebooks and Python.

Space Image Processing: A web app for lunar surface segmentation and space image colorization using FastAPI, Streamlit, and deep learning models. Includes interactive UI, model training notebooks, and Kaggle integration.

The "Customer Prediction Analysis Streamlit" GitHub repository contains all the files related to a project that analyzes customer data using a dummy dataset. The repository includes Jupyter notebooks for data preprocessing, exploratory data analysis, and model training.

Welcome to the Mushroom Prediction Model repository! This project aims to identify poisonous and edible mushrooms using machine learning techniques. Developed in a Jupyter Notebook and deployed with Streamlit, the model offers an interactive and user-friendly interface for mushroom classification.

The project aims to create a comprehensive web app using Streamlit, Anaconda, Jupyter Notebook, and Spyder, predicting Diabetes, Heart Disease, and Parkinson's. It uses ML models on diverse health data for personalized predictions, empowering users to manage health proactively.

This repository contain the Jupyter Notebook files which is based on AI/ML techniques and contain machine learning models based on two popular datasets 'Boston Housing Prices' and 'California Housing Prices'. The project introduces an web based Application which can be used to find pricing of house.

This project uses a TinyVGG16-based CNN to classify MRI scans for Alzheimer's Disease stages: Mild Impairment, Moderate Impairment, No Impairment, and Very Mild Impairment. It includes Jupyter notebooks for training and prediction, and a Streamlit app for easy inference. The model achieves high metrics in predicting Alzheimer's stages.