Assignment 2: EDA and Linear Regression for Traffic Volume Data

This report presents an analysis of weather and traffic volume data using exploratory data analysis (EDA) and linear regression modeling within the RapidMiner environment. The assignment involves a detailed examination of a .CSV dataset, including the identification of variable types, missing values, and key statistical characteristics such as mean, standard deviation, and mode. The EDA process is documented with screen captures, and the results are summarized in a table. The core of the analysis focuses on developing a linear regression model to predict traffic volume based on correlated variables. The report includes the model's intercept, standard errors, and p-values, providing insights into the statistical significance of the variables and the model's overall performance. References to relevant research papers support the methodology and findings. The assignment fulfills the requirements of Task 2, demonstrating the student's ability to apply data mining techniques to analyze real-world datasets and draw meaningful conclusions about traffic patterns influenced by weather conditions.