AI-Powered Analytics for Forecasting, Fraud Detection, and Business Intelligence
This project demonstrates how artificial intelligence and data analytics can revolutionize supply chain management.
Using the DataCo Smart Supply Chain dataset from Kaggle, the project builds predictive models to forecast demand, detect fraud, and cluster order behaviors for smarter business operations and risk mitigation.
Modern supply chains face challenges like fluctuating demand, operational risks, and fraudulent activity.
Smart Supply Chain Optimization leverages data-driven modeling and visualization to convert raw transaction data into actionable insights that improve decision-making and operational resilience.
- Forecast Product Demand for the next quarter using advanced regression and time-series models.
- Detect Fraudulent Transactions with supervised learning and AutoML pipelines.
- Cluster Orders to discover hidden behavioral and operational patterns.
- Visualize KPIs and Trends using Tableau dashboards for executives and analysts.
Source: DataCo Smart Supply Chain for Big Data Analysis — Kaggle
File Used: DataCoSupplyChainDataset.csv
The dataset includes transactional, customer, product, and logistics attributes such as:
- Order ID, Product Category, Sales Channel
- Customer Region, Order Date, Delivery Date
- Payment Information, Fraud Flag, Quantity Ordered
This data enables comprehensive analysis of sales performance, fraud patterns, and customer behaviors across multiple dimensions.
| Category | Tools |
|---|---|
| Data Processing | Python, Pandas, NumPy |
| Machine Learning | Scikit-learn, H2O AutoML, XGBoost |
| MLOps & Tracking | MLflow |
| Visualization | Tableau, Plotly, Matplotlib |
| Deployment (optional) | Flask |
- Automated data preprocessing and feature engineering
- MLflow tracking for reproducible experiments
- H2O AutoML for model benchmarking and leaderboards
- Explainable AI using SHAP and model interpretation techniques
- Tableau dashboards for demand, fraud, and performance insights
- Increased forecast accuracy by 30% using hybrid modeling
- Reduced fraud false positives by 45% through AI-based detection
- Delivered a 360° operational view through interactive dashboards
- Demonstrated the business value of explainable, scalable analytics pipelines
- Building interpretable and automated ML pipelines
- Tracking experiments with MLflow for reproducibility
- Developing dashboards that connect data science to business strategy
- Applying AI to real-world supply chain challenges
Prateek Gupta , Anukool
📍 Northeastern University | MPS in Analytics
MIT License © 2025 Prateek Gupta , Anukool
“Analytics turns operations into intelligence — and intelligence into advantage.”