Suppose you are a business analyst for a clothing chain that is planning an aggressive marketing campaign. To help target this marketing, you have been asked to mine the data available in the more than 10,000 records from the Spending and Bankruptcy Data Set file, linked in the Resources. Specifically, you have been asked to identify and predict the characteristics of the men and women who spend the most on clothing. You decide to approach this request by developing a multiple regression model to predict clothing expenditure as a function of the various demographic information in the file.
For this assignment, complete this analysis and prepare a report of the results. Include the following:
• Describe which data you used from the Spending and Bankruptcy Data Set, and what modifications you had to make to that data.
• Fit a predictive model based on linear regression that could be used to predict expenditures.
• Partition the more than 10,000 data records into training and validation sets. Describe how your partition was established.
• Describe the best predictive model that could be developed, and how well it predicts expenditures.
• Evaluate the predictive accuracy of the model by examining its performance on the validation set.
• Based on the predictive model, analyze the implications of the results from this model for the planned advertising campaign.