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AI-powered medical imaging system for multi-disease chest X-ray detection,built with EfficientNet deep learning, a FastAPI backend, and an interactive Streamlit dashboard. Deployed on Render for real-time healthcare diagnostics, detecting conditions like Atelectasis, Edema and more.An end-to-end project demonstrating model training,API development.
An end-to-end regression project trained on the Boston Housing dataset, featuring data analysis, Random Forest modeling, model evaluation, and real-time prediction through a REST API built with FastAPI.
End-to-end MLOps project for predictive maintenance using engine sensor data. Includes data versioning on Hugging Face, MLflow experiment tracking, CI/CD with GitHub Actions, and Dockerized Streamlit deployment for real-time engine failure classification.
A Python-based handwritten character recognition system that uses a CNN trained on self-collected data to accurately recognize mouse-drawn characters. Includes a unified desktop UI for real-time prediction, dataset management, and model retraining.
An end-to-end machine learning project built on the UCI Heart Disease dataset, covering data preprocessing, feature engineering, model training, evaluation, and deployment. The project includes Streamlit app that supports both single-patient and batch predictions, ensuring reproducibility through a well-structured pipeline and saved model artifacts
This project builds a predictive model to estimate visa approval likelihood using candidate and job-related features. It showcases an end-to-end machine learning workflow with EDA, feature engineering, and model tuning to automate parts of the visa evaluation process.
An end-to-end machine learning project to predict the sale price of bulldozers. This repository details a full data science workflow, including data preprocessing, model training with scikit-learn pipelines, hyperparameter tuning, and model evaluation.
An end-to-end Machine Learning system for detecting credit card fraud using imbalanced classification techniques. Includes data preprocessing, SMOTE handling, Random Forest model training, evaluation metrics, and a real-time Streamlit dashboard with live fraud risk scoring and interactive analytics.
✈️ End-to-end ML web app that predicts Indian domestic flight ticket prices. Built with Python, scikit-learn & Flask — covers data cleaning, feature engineering (34 features from 10K+ records), model comparison (Lasso, Ridge, SVR & more), and a responsive UI for real-time predictions.
This project delivers a seamless recommendation experience by blending machine learning similarity models with The Movie Database (TMDB) API for real-time posters, summaries, and release details. Lightweight, fast, and designed for production-grade deployment.