please check the attachment and answer the questions in a word file, you will have to use rapid miner to access some of these data sets. thank you
Homework 2 - Classification and prediction Assignment This assignment contains two tasks. Detailed instructions and the related datasets will be available in Blackboard Task 1: Boston Housing This dataset contains information collected by the US Census Bureau concerning housing prices in the area of Boston, Massachusetts. The dataset has 506 cases. Each case contains summary data for a housing tract (neighborhood). It was obtained from the StatLib archive (http://lib.stat.cmu.edu/datasets/boston). There are 14 attributes in each case of the dataset. They are: CRIM Per capita crime rate by town ZN Proportion of residential land zoned for lots over 25,000 sq.ft. INDUS Proportion of non-retail business acres per town. CHAS Charles River dummy variable (1 if tract bounds river; 0 otherwise) NOX Nitric oxides concentration (parts per 10 million) RM Average number of rooms per dwelling AGE Proportion of owner-occupied units built prior to 1940 DIS Weighted distances to five Boston employment centers RAD Index of accessibility to radial highways TAX Full-value property-tax rate per $10,000 PTRATIO Pupil-teacher ratio by town B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town LSTAT % lower status of the population MEDV Median value of owner-occupied homes ($000) The median value of owner-occupied homes (MEDV) is believed to be dependant upon all or some of the other thirteen variables a. Why should the data be partitioned into training and validation sets? For what will the training set be used? b. Fit a multiple linear regression model to the median house price (MEDV) as a function of the other thirteen variables. Please write down the equation for predicting the median house price from the thirteen variable. c. Fit a multiple linear regression model to the median house price (MEDV) as a function of CRIM, CHAS, and RM. Please write down the equation for predicting the median house price from these three variables. d. Using the model created in c to answer following question. What median house price is predicted for a tract in the Boston area that does not bound the Charles River, has a crime rate of 0.1, and where the average number of rooms per house is 6? The deliverables of this task include a WORD document with answers to above questions Task 2: Charles Book Club (CBC) A new title, "The Art History of Florence", is ready for release. CBC has sent a test mailing to a random sample of 4,000 customers from its customer base. The customer responses have been collated with past purchase data. The data could be randomly partitioned into 3 parts Training Data : initial data to be used to fit response models, Validation Data : holdout data used to compare the performance of different response models, and Test Data : data only to be used after a final model has been selected to estimate the likely accuracy of the model when it is deployed. The Sample Data are in file - CharlesBookClub.xls . Each row (or case) in the spreadsheet (other than the header) corresponds to one market test customer. Each column is a variable with the header row giving the name of the variable. The variable names and descriptions are given in Table 1, below. Table 1: List of Variables in CharlesI.xls Variable Name Description Seq# Sequence number in the partition ID# Identification number in the full (unpartitioned) market test data set Gender O=Male 1=Female M Monetary- Total money spent on books R Recency - Months since last purchase F Frequency - Total number of purchases FirstPurch Months since first purchase ChildBks Number of purchases from the category: Child books Youth Number of purchases from the category: Youth books CookBks Number of purchases from the category: Cookbooks DoItYBks Number of purchases from the category Do It Yourself books I RefBks Number of purchases from the category: Reference books (Atlases, Encyclopedias, Dictionaries) ArtBks Number of purchases from the category Art books GeoBks Number of purchases from the category: Geography books ItalCook Number of purchases of book title: "Secrets of Italian Cooking" ItalAtlas Number of purchases of book title: "Historical Atlas of Italy" ItalArt Number of purchases of book title: "Italian Art" Florence =1 'The Art History of Florence" was bought, = 0 if not Identify the target segment of customers to whom you will send the mailers, using Logistic Regression technique of Data Mining; Conduct four trials by selecting variables as follow. For each trial, report the performance information. 1. The full set of 15 predictors as independent variables and "Florence" as the dependent variable, 2. Best subset selection of six variables. Choose six variables that you think are important as independent variables and "Florence" as the dependent variable 3. Change the training data set such that it contains the same number of rows with Florence =1 as those with Florence = 0. Run the full set of 15 predictors as independent variables and "Florence" as the dependent variable 4. For the newly generated training data set, choose six variables that you think are important as independent variables and "Florence" as the dependent variable 5. Select one model from above generated models that you believe to be the best. Apply the model to the test data for prediction, identifying customers who will respond to your mail. The deliverables of this task include the classification matrix for each of the four models, the worksheet with the test data and the prediction from the model, and answers to following questions. 2. When we cut the size of the training data set such that it contains the same number of rows with Florence =1 as those with Florence =0 , what is the impact of this change on the model accuracy. Please explain why. 3. Please explain your choice of the best model for the identification of why you selected the model you selected for the task of identifying customers who will respond. 4. After using the model for prediction, please sort the "test" worksheet in descending order based on a person's probability of responding. Please report the number of responding customers the model recognizes if we set the cutoff probability at 95%, 75%, and 50%. 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