🛒 Sales and Demand Forecasting for Retail Chains

📊 Predictive Sales Analytics with Time Series Modeling

This project aims to forecast weekly sales for retail stores using historical sales and economic data. By applying advanced time series forecasting models, we enable better inventory management, demand planning, and revenue optimization for retail chains. The project includes both traditional statistical models and deep learning techniques, along with a live dashboard for business insights.

📚 Table of Contents

• Problem Statement
• Objective
• Challenges
• Project Lifecycle
• Tools and Technologies
• Success Criteria
• Expected Outcome
• References
• Connect With Me

📌 Problem Statement

Retail chains must anticipate customer demand accurately to avoid overstocking or understocking. This project uses historical sales, store information, and economic indicators to build forecasting models that can predict weekly sales across multiple retail stores.

🎯 Objective

• Predict weekly sales for retail stores accurately.
• Use statistical and deep learning time series models.
• Build an end-to-end pipeline from preprocessing to forecasting.
• Provide an interactive dashboard to visualize sales trends and forecasts.

⚠️ Challenges

• Handling seasonality, promotions, and holidays in sales data.
• Integrating multiple datasets (sales, store info, economic indicators).
• Comparing traditional and deep learning forecasting models.
• Ensuring model performance on unseen future data.

🛠️ Project Lifecycle

1. Data Collection & Understanding
   • Dataset from Walmart's Kaggle competition including historical sales and store metadata.
2. Data Preprocessing
   • Merging datasets, handling missing values, creating new features (e.g. month, holiday flag).
3. Exploratory Data Analysis (EDA)
   • Sales trends, outliers, seasonality patterns, and feature correlations.
4. Model Building
   • Train and test time series models:
     • ARIMA (Statistical)
     • Facebook Prophet (Additive Seasonality)
     • LSTM (Deep Learning)
5. Model Evaluation
   • Compare models using RMSE, MAE, and visual forecasts.
6. Model Deployment (Optional)
   • Streamlit dashboard to interactively visualize predictions by store and department.

💻 Tools and Technologies

✔️ Success Criteria

• Achieve high accuracy and low forecasting error.
• Robust models that generalize well.
• Clear, actionable insights via visualizations.
• Easy-to-use forecasting pipeline and dashboard.

📈 Expected Outcome

• Reliable weekly sales forecasts by store.
• Visual reports highlighting trends and forecasts.
• Reusable code modules for future forecasting projects.

🔗 References

• Kaggle Sales Forecasting Dataset: https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting

🤝 Connect With Me