This project analyzes restaurant tipping behavior using Excel. The goal is to predict tip amounts based on customer and order details such as total bill, gender, time, day, and group size.
Build a regression model to understand what factors influence how much a customer tips, and use it to predict future tips.
- Microsoft Excel
- Pivot Tables
- Data Analysis ToolPak (Correlation & Regression)
- Charts (Pie, Bar, Scatter, Histogram)
- Data cleaning and encoding of categorical variables
- Pivot analysis by gender and time
- Correlation analysis between tip and other features
- Multiple linear regression model built in Excel
- Calculation of predicted tips and residuals
- Dashboard with summary insights and visuals
- Tip increases by ~₹0.094 for every ₹1 in the total bill
- Group size has a small positive effect
- Gender, smoking status, time, and day are not significant
- Model explains ~47% of tip variability (R² = 0.469)
| Dashboard | Regression Summary |
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Restaurant_Tips_Analysis.xlsx– Contains raw data, regression model, predictions, charts, and dashboard.
This project is licensed under the MIT License.