This project builds a regression model to predict the daily revenue of a coffee shop based on various operational features such as customer count, average order value, marketing spend, and foot traffic. Using a dataset from Kaggle, we explore the data, preprocess it, visualize patterns, and train a machine learning model to forecast revenue.
- Source: Kaggle-Coffee Shop Daily Revenue
- License: CC0 (Public Domain)
- You can also download it directly from this repo under the data/ folder.
- Understanding the Data
- Reading and Exploring the Data
- Visualizing Key Patterns
- Preprocessing (Scaling & Splitting)
- Model Training (Linear Regression)
- Model Evaluation using R² Score
A multiple linear regression model is trained to predict revenue based on the available features. Evaluation is done using the R² metric on a test set.
This project is licensed under the MIT License — see the LICENSE file for details.