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Assignment 1

Due: Friday, April 17

Consider the data collected by a hypothetical video store for 50 regular customers. This data consists of a table which, for each customer, records the following attributes: Gender, Income, Age, Rentals (total number of video rentals in the past year), Avg. per visit (average number of video rentals per visit during the past year), Incidentals (whether the customer tends to buy incidental items such as refreshments when renting a video), and Genre (the customer's preferred movie genre). This data is available as an Excel spreadsheet.

1. Explore the general characteristics of the data, by computing the means and standard deviations of the numerical attributes, as well as the the distributions of male and female customers, the preferred movie genres, etc.
2. Perform the the following data preparation steps on the data (for each add a new column to the original table for comparison purposes)
1. Use smoothing by bin means to smooth the values of the Age attribute. Use a bin depth of 4.
2. Use min-max normalization to transform the values of the Income attribute onto the range [0.0-1.0].
3. Use z-score normalization to standardize the values of the Rentals attribute.
4. Discretize the (original, non-normalized) Income attribute based on the following categories: High = 60K+; Mid = 25K-59K; Low = less than \$25K.
3. Convert the original table (not the results of part 2) into the standard spreadsheet format. Note that this requires converting each categorical attribute into multiple attributes (one for each values of the categorical attribute) and assigning binary values corresponding to the presence or not presence of the attribute value in the original record). For example, the Gender attribute will be transformed into two attributes, "Genre=M" and "Genre=F". The numerical attributes will remain unchanged. This process should result in a new table with 12 attributes (one for Customer ID, two for Gender, one for each of Income, Age, Rentals, Avg. Per Visit, two for Incidentals, and three for Genre).
4. Using the standardized data set (from part 3), perform basic correlation analysis among the attributes. Discuss your results by indicating any strong correlations (positive or negative) among pairs of attributes. You need to construct a complete Correlation Matrix. Be sure to first remove the Customer ID column before creating the correlation matrix.
5. Perform a cross-tabulation of the two "gender" variables versus the three "genre" variables. Show this as a 2 x 3 table with entries representing the total counts. Then, use a graph or chart that provides the best visualization of the relationships between these sets of variables. [See Slide 24 in Understanding Characteristics of Data for an example. Also review Chapter 4 of Berry and Linoff.] Can you draw any significant conclusions?
6. Select all "good" customers with a high value for the Rentals attribute ( a "good customer is defined as one with a Rentals value of greater than or equal to 30). Then, create a summary (e.g., using means, medians, and/or other statistics) of the selected data with respect to all other attributes. Can you observe any significant patterns that characterize this segment of customers? Explain. Note: to know whether your observed patterns in the target group are significant, you need to compare them with the general population  using the same metrics.
7. Suppose that because of the high profit margin, the store would like to increase the sales of incidentals. Based on your observations in previous parts discuss how this could be accomplished (e.g., should customers with specific characteristics be targeted? Should certain types of movies be preferred? Etc.). Explain your answer based on your analysis of the data.
8. Use WEKA to perform the following tasks on the original data set (use the Comma Separated version of the above data set: Video_Store.csv). Load the data into WEKA Explorer (the Preprocessing module). Remove the Customer ID attribute. Review basic statistics for different attributes by clicking on the name of each one in "attribute" panel.  Next, use the unsupervised attribute "Discretize" filter to discretize the Age attribute. Finally, use the unsupervised attribute "Normalize" filter to convert all of the remaining numerical attribute into [0,1] scale. Save the resulting data set into an ARFF formatted file and submit with your answers for the above questions.