modeltraining

Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.

An interactive Machine Learning-powered web application that predicts whether a person is diabetic based on key health parameters. Built with Python, Scikit-learn, and Streamlit, this app aims to make early diabetes risk detection simple and accessible.

Predict future stock prices using a pre-trained LSTM deep learning model. Upload a CSV file with historical stock data or use a sample to visualize trends and forecast closing prices.

Handwritten Digit Classifier using a trained MNIST model. Draw or upload a digit (0-9) and get its predicted value instantly.

A machine learning model that predicts house prices based on features like size, location, and amenities. Helps buyers and sellers estimate property values accurately.

GPU-accelerated drug discovery, graph analytics, agentic workflows with a friendly face

This repository contains a basic fraud detection system utilising supervised learning techniques to identify potentially fraudulent credit card transactions. The project establishes a baseline model that addresses the challenges of credit card fraud in financial institutions.

Flight Fare Predictor is a machine learning project that predicts flight ticket prices using historical flight data. The model analyzes key travel features to estimate fares accurately, helping users plan trips more efficiently.

Welcome to the Machine Learning Repository! This repository is a collection of notebooks showcasing various machine learning projects and implementations. It incluedes Decision tree algorithm, Random forest , Support vector machine etc.

SnapnCook is an AI-powered web app that helps you cook smarter, not harder. Simply upload a photo of your fridge or ingredients, and the app instantly suggests delicious recipes based on what it sees, no typing required!

Real-time household energy consumption forecasting system powered by XGBoost and FastAPI. Includes MLOps automation with DVC, MLflow, Prefect, and Streamlit dashboard.

威智慧眼（VisionWiz）是一款开源的AI工具软件，专为人工智能模型的高效开发和优化而设计。它集成了一整套功能，涵盖了模型训练、数据采集、数据标注以及模型测试等多个关键环节。 VisionWiz is an open-source AI tool designed for the efficient development and optimization of AI models. It includes a comprehensive set of features that cover several key processes, such as model training, data collection, data labeling, and model testing.

Brain Tumor MRI Classification is an end‑to‑end deep learning project that trains multiple models (ResNet50, VGG16, a custom CNN, SVM, and Random Forest) to automatically detect and classify brain tumors from MRI scans into four classes: glioma, meningioma, pituitary, and no tumor.

This project implements Google Cloud's Vertex AI to develop a machine learning model that predicts loan repayment risks using a tabular dataset. It encompasses data preparation, model training, evaluation, deployment, and prediction processes.

🎬An 🌟intelligent movie recommendation system🎬using unsupervised machine learning with🌟🌟DBSCAN clustering algorithm🌟🌟

AI-driven 3D character design using Stable Diffusion and LoRA. Covers Blender modeling, Unity export, data generation, and training with Protogen v2.2. Achieves accurate renders from text prompts while exploring challenges like data selection, tuning, and artistic limitations.

This Hand gesture recognition project using mediapipe is developed to recognize various hand gestures. The user can custom train any number of various hand gestures to train a model.

A deep learning project that classifies seven types of skin lesions using the HAM10000 dataset. Among four tested models, Swin Transformer achieved the best accuracy of 88.9%, showing AI’s potential in early skin cancer detection.

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

Machine Learning Workshop 2 – CSE 2794 LAB ASSIGNMENT-6: Object Detection and Segmentation using YOLOV8