An end-to-end Data Science + Machine Learning solution designed to optimize retail and supply chain operations through demand forecasting, customer segmentation, and inventory optimization.
This project leverages historical sales data to build a smart inventory management system that:
- Forecasts future demand using advanced ML/DL models
- Segments customers and products for targeted insights
- Recommends products using collaborative filtering
- Optimizes inventory to reduce cost and prevent stockouts
- Visualizes insights via an interactive Power BI dashboard
- 📉 15–20% reduction in holding costs
- 📦 25% reduction in stockouts
- ⚡ 50% cost optimization using linear programming (simulation-based)
- Source: Kaggle Superstore Sales Dataset
- Size: ~2,800 rows (sample used for demo)
- Features: Sales, Customers, Products, Regions, Dates
- Missing value handling
- Trend analysis (monthly, weekly, regional)
- Top customers & products
- RFM (Recency, Frequency, Monetary) segmentation
Implemented and compared multiple models:
- Linear Regression (baseline)
- LSTM (Deep Learning sequence model)
- Prophet (seasonality-aware, best performer)
📌 Best Model: Prophet (MAE ≈ 850)
- K-Means clustering for product segmentation
- Insights based on price vs. sales volume
- Collaborative filtering
- Personalized product suggestions
- Linear Programming (SciPy
linprog) - Economic Order Quantity (EOQ)
- Cost minimization with constraints
📌 Example:
- Optimal Inventory: 199 units
- Cost reduced from $400 → $196/month
- Built using Power BI
- Includes:
- KPIs
- Forecast trends
- Inventory alerts
- Customer insights
- Generates PDF reports with insights, charts, and tables
Languages & Libraries
- Python (Pandas, NumPy)
- Scikit-learn
- TensorFlow / Keras
- Prophet
- SciPy
Visualization
- Matplotlib, Seaborn
- Power BI
Environment
- Jupyter Notebook / Google Colab
same as shown in github main branch
git clone https://github.com/yourusername/intelligent-forecasting-inventory.git
cd intelligent-forecasting-inventory2️⃣ Create Virtual Environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
3️⃣ Install Dependencies
pip install -r requirements.txt
Run modules step-by-step:
🔹 1. Data Cleaning & EDA
jupyter notebook eda.ipynb
🔹 2. Forecasting
jupyter notebook forecasting.ipynb
🔹 3. Clustering & Recommendations
jupyter notebook insights.ipynb
🔹 4. Inventory Optimization
jupyter notebook optimization.ipynb
🔹 5. Generate Report
📈 Sample Results
📊 Monthly Sales Peak: $5,205
📅 Forecast (Next Month): ~$5,500
📉 Cost Optimization: 50% savings
👥 Customer Segment: Majority "Champions"
📸 Screenshots
Add result in .csv formet
Monthly Trends Forecast Graph Dashboard Overview Inventory Optimization 🔗 Project Link
🤝 Contributing
Contributions are welcome!
Fork the repo Create a new branch Commit changes Open a Pull Request 📜 License
MIT License
📬 Contact
📧 Email: nawalkumar4810167@gmail.com
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