Prototyping a Machine Learning Application with Streamlit, FastAPI, Hugging Face and Docker

Customer Segmentation using KMeans clustering with a full ML pipeline including data preprocessing, model training, API serving via FastAPI, visualization with Streamlit dashboard, logging, error handling, and Docker deployment.

An end-to-end ML solution for used-car price prediction, offering easy-to-use interface with Streamlit, scalable API with FastAPI, and ready-to-run Docker containers.

Customer Segmentation using KMeans clustering with a full ML pipeline including data preprocessing, model training, API serving via FastAPI, visualization with Streamlit dashboard, logging, error handling, and Docker deployment.

Job Market Salary Predictor - Predicts IT sector salaries in Germany using RandomForest ML on Adzuna data. Dockerized stack with FastAPI, Streamlit UI, PostgreSQL/MongoDB, and Airflow for daily ingestion/retraining.

A production-grade, full-stack machine learning platform combining a powerful FastAPI backend with an intuitive Streamlit frontend. Upload CSV data, train 12+ ML models, compare results, and visualize predictions—all through an interactive web interface or REST API.

NaijaEstateAI is an ML-powered real estate platform for Nigeria that improves pricing accuracy and transparency. It cleans and engineers features from property listings, trains Linear Regression, Random Forest, and XGBoost, evaluates with CV/metrics, and serves the best model through a Streamlit app and FastAPI.

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Deploy a ML model into production with FastAPI, Streamlit, Docker and GCP. Hackathon given for ISAE-SDD in 2020-2021

customer churn prediction system build with fastapi CRUD api and streamlit ,json storage and real time ml inference

AI-Powered Contract Analysis & Risk Assessment System - Production-ready ML pipeline with FastAPI, Streamlit, and comprehensive contract clause detection

Multimodal AI/ML Toolkit (Audio, Image, Video, Text). Algorithms &Utils(C++ and Python) with Streamlit UI, FastAPI, JavaScript&TypeScript/React web, SQLite, gtest, pytest, etc.

🫀 Production-ready ML system for heart disease risk assessment with 88.5% accuracy. Features FastAPI REST API, Streamlit dashboard, SHAP explainability, MLflow tracking, and Docker deployment. Demonstrates end-to-end ML engineering from notebook to production-grade application.

Real-time predictive maintenance platform. Kafka streaming, LSTM + Random Forest ML, FastAPI inference with MC Dropout uncertainty, automated drift detection & retraining, multi-channel alerting, and Streamlit + Grafana dashboards. Production-grade async Python on TimescaleDB.

The Customer Churn Prediction System is an end-to-end ML application that identifies customers likely to leave a telecom service. It combines an optimized XGBoost model with a FastAPI backend and an interactive Streamlit UI for real-time and batch predictions.

CredVibe is an ML credit scorecard system achieving 95%+ default recall with explainable predictions for loan risk assessment. Features KS/Gini validation, Optuna tuning, FastAPI + Streamlit deployment. Generates CIBIL-like scores, and converts to business rules for BRE integration.

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ML-Model-Deployment-with-FastAPI-and-Streamlit

ML Model Deployment Platform End-to-end ML pipeline with MLflow, FastAPI, Streamlit, and Docker

Deep Learning ML service for diabetes prediction with Streamlit UI and FastAPI backend`

Production-grade ML system with FastAPI, Streamlit, and K-Means clustering for real-time customer intelligence.

Dockerized ML inference system with FastAPI and Streamlit, implementing a Linear Regression–based house price prediction model

End-to-end ML project predicting manufacturing production rate using CatBoost with FastAPI backend and Streamlit frontend deployment.

Employee performance analytics with 9-box grid, clustering, causal inference (DoWhy), SHAP explainability, and ML prediction. FastAPI + Streamlit.

End-to-end adaptive ML system with real-time prediction, monitoring, drift detection, and automated retraining using FastAPI and Streamlit

A FastAPI-based backend serving an ML model to predict insurance premium categories (High, Medium, Low). Includes a Streamlit frontend for user interaction. The project demonstrates integration of machine learning with FastAPI and Streamlit.

Personalized ML-powered study planner that predicts topic risk, explains recommendations with SHAP, and generates a 7-day schedule via FastAPI + Streamlit.

🎬 Intelligent movie recommendation system with FastAPI backend, Streamlit frontend, and collaborative filtering ML. Rate movies, get personalized suggestions, and enjoy automatic model retraining.

Real-time API security monitoring system that detects anomalies and classifies cyber threats using Isolation Forest ML. Built with FastAPI, scikit-learn, and Streamlit.

A hybrid ML and DL powered credit scoring system with a FastAPI backend and Streamlit frontend for real-time applicant risk prediction and explainable decisions.