Linear Regression
This project utilizes a dataset on beer consumption in a university region of São Paulo, Brazil. The goal of this project is to estimate a Machine Learning model using Linear Regression to demonstrate the impact of various variables on beer consumption. The dataset includes Date, Average Temperature (°C), Minimum Temperature (°C), Maximum Temperature (°C), Precipitation (mm), Weekend (1 = Yes; 0 = No), and Beer Consumption (liters). The final model is a function dependent on Maximum Temperature, Precipitation, and Weekend to predict beer consumption.
The project aims to explore and model the beer consumption data using Linear Regression, providing insights into how different factors influence consumption.
The project focuses on the following:
- Use visualizations to understand data distribution
- Differentiate between dependent and explanatory variables in the data
- Learn to separate training and testing data
- Model with linear regressions
- Understand errors through residuals and metrics
- Compare and save the best models