Python sdk for zero-shot time-series forecasting
End-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day forecasts with confidence intervals. Reproducible scripts and visualizations for portfolio showcase.
A project focused on YouBike optimization, including improvement of dispatch strategies and prediction of potential demand.
Bike sharing prediction based on neural nets
Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions
Minimize forecast errors by developing an advanced booking model using Python
End-to-end café inventory project: clean transaction data, build daily item-level demand series, backtest strong baseline forecasters, generate next-30-day demand forecasts, convert forecasts into safety stock + reorder points, and validate policies with Monte Carlo stockout-risk simulations, wrapped in a Streamlit dashboard.
Applying a structural time series approach to California hourly electricity demand data.
AI-Powered Bookkeeping & Demand Forecasting
An end-to-end MLOps platform leveraging distributed orchestration, experiment tracking, and containerized microservices to deliver real-time demand forecasting for Boston's Bluebikes network. The system implements a multi-algorithm ensemble architecture with automated hyperparameter tuning.
ARIMA ML Model - Oil and Gas Supply Chain Demand Forecasting with LLM Analysis using AWS Bedrock Foundational Model
E-commerce pricing optimization & dynamic user profiling
AI-powered pharmaceutical supply chain management using LangGraph agents, OpenAI GPT-4, and optimization algorithms.
Forecasting e-commerce product demand using PySpark MLlib. Includes data preprocessing, feature engineering, Random Forest modeling, and evaluation via Mean Absolute Error.
A demand forecasting pipeline deployed on Azure and AWS
A comprehensive Streamlit dashboard for optimizing supply chain operations. Features interactive analytics for demand forecasting, inventory management, and supplier performance.
🌌 ChaosChain-AI: Next-Gen Supply Chain AI Simulator Advanced AI control tower combining chaotic demand modeling, Monte Carlo simulations, and multi-factor risk scoring. Features real-time monitoring, predictive analytics, and automated mitigation across global supply chains. 🔬 Research | 🏭 Supply Chain AI | 🤖 Machine Learning |📊 Simulation
Demand forecasting ML project using XGBoost + feature engineering to predict customer orders, with Streamlit web app deployment.
End-to-end Supply Chain Demand Forecasting using Machine Learning and Streamlit with interactive dashboard and feature importance analysis.
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