This code project involves the analysis of sales data for Carseats, focusing on predicting sales using various machine learning techniques. It includes the application of regression trees, cross-validation for model selection, pruning, bagging, and random forests. The goal is to evaluate and compare the performance of these models for sales prediction, ultimately providing insights into the most effective modeling approach for the given dataset.

This project involves the analysis of a dataset related to Orange Juice (OJ) purchases. It begins by creating a training set and a test set, followed by fitting a decision tree model to the training data with the goal of predicting OJ purchase decisions. The project explores various aspects of decision tree modeling, including model summary, visualization, and predictive performance evaluation. Key steps in the project include determining the optimal tree size through cross-validation, producing a pruned decision tree, and comparing the training and test error rates between the pruned and unpruned trees. The main objective is to understand the effectiveness of decision tree models in classifying OJ purchases and to optimize model performance.

Lab5_Greeshma_Ganji_Part2

Lab5_Greeshma_Ganji