Sales Predictor Restaurants
INDUSTRY
Restaurant Sales Forecasting
DATE
March 2025
SERVICES
Retail, Food & Beverage Analytics
Sales Forecasting • Predictive Modeling • Business Insights
🧠 Project Overview
Accurate sales forecasting is critical for restaurants to optimize staffing, inventory, and pricing strategies. This project analyzed multi-store restaurant sales data to uncover demand patterns, customer preferences, and revenue drivers. Using advanced machine learning models, we built forecasts that improved accuracy and supported data-driven operational decisions.
95%
Reduction in forecasting errors vs. baseline
3.1x
Faster demand planning & inventory adjustments
48%
Improvement in accuracy using ML vs. traditional methods
🛠️ Methodology
• Imported and merged datasets with dates, store IDs, item IDs, item names, prices, sales counts, kcal values, and store details.
• Performed EDA to analyze:
— Overall sales patterns
— Weekly, monthly, and quarterly trends.
— Restaurant performance comparisons.
— Item popularity at store and chain level.
• Identified most expensive items per restaurant and mapped their calorie values.
• Engineered features: day of week, month, quarter, year, day of month.
• Built and compared Linear Regression, Random Forest, and XGBoost models.
• Used last 6 months as test data; evaluated with RMSE.
• Deployed the best-performing model to forecast sales for the next year.
• Performed EDA to analyze:
— Overall sales patterns
— Weekly, monthly, and quarterly trends.
— Restaurant performance comparisons.
— Item popularity at store and chain level.
• Identified most expensive items per restaurant and mapped their calorie values.
• Engineered features: day of week, month, quarter, year, day of month.
• Built and compared Linear Regression, Random Forest, and XGBoost models.
• Used last 6 months as test data; evaluated with RMSE.
• Deployed the best-performing model to forecast sales for the next year.
Key Achievements
✅ Identified seasonal sales trends by day, month, and quarter.
✅ Determined top-performing restaurants and bestselling items.
✅ Analyzed whether high sales volume = high revenue contribution.
✅ Improved forecasting accuracy with ensemble models.
✅ Delivered next-year forecasts with actionable insights for staffing, inventory & promotions.
✅ Determined top-performing restaurants and bestselling items.
✅ Analyzed whether high sales volume = high revenue contribution.
✅ Improved forecasting accuracy with ensemble models.
✅ Delivered next-year forecasts with actionable insights for staffing, inventory & promotions.
