Please help with this Weka problem. I have attached the file as well.

File:

3. The marketing department of a financial firm keeps records on customers, including demographic information and the type of accounts. When launching a new product, such as a "Personal Equity Plan" (PEP), a direct mail piece, advertising the product, is sent to existing customers, and a record kept as to whether that customer responded and bought the product. Based on this store of prior experience, the managers decide to use data mining techniques to build customer profile models. In this problem we are interested only in deriving (quantitative) association rules from the data. The data contains the following fields: sex id a unique identification number age age of customer in years (numeric) MALE/FEMALE region inner_city/rural/suburban/town income income of customer (numeric) married is the customer married (YES/NO) children number of children (numeric) does the customer own a car (YES/NO) save_acct does the customer have a saving account (YES/NO) current_acct does the customer have a current account (YES/NO) mortgage does the customer have a mortgage (YES/NO) did the customer buy a PEP (Personal Equity Plan) after | the last mailing (YES/NO) car The data is contained in the file bank-data.csv. Each record is a customer description where the "pep" field indicates whether that customer bought a PEP after the last mailing. Your goal is to perform Association Rule discovery on the data set using the Weka package. Note: Association rule mining requires discretization of continuous variables. This task can be performed in the data transformation step or (in some cases) by the mining program. WEKA is a full data mining suite which includes various preprocessing modules (filters). When using WEKA, you will first apply the relevant preprocessing filters to transform the data before you perform association rule discovery. First perform the necessary preprocessing steps required for association rule mining. Specifically, the "id" field will need to be removed and the numerical attributes must be discretized. a. (2 points) Now perform association rule discovery on the transformed data. Experiment with different parameters so that you get at least 20-30 strong rules (e.g., rules with high lift and confidence which at the same time have relatively good support). In WEKA Apriori algorithm interface set "outputItemsets" to "True" so that you can also view the frequent items sets of different sizes in addition to the rules. Indicate what those strong rules are b. (3 points) Select the top 5 most "interesting" rules and for each specify the following: 1. an explanation of the pattern and why you believe it is interesting based on the business objectives of the company, 2. any recommendations based on the discovered rule that might help the company to better understand behavior of its customers or in its marketing campaign. Note: The top 5 most interesting rules are most likely not the top 5 in the result set of the Apriori algorithm. They are rules that, in addition to having high support, lift, and confidence, also provide some non-trivial, actionable knowledge based on the underlying business objectives. age sex NO id ID12101 ID12102 ID12103 ID 12104 ID12105 ID 12106 ID 12107 ID 12108 ID12109 ID12110 ID12111 ID12112 ID12113 ID 12114 ID 12115 ID12116 ID12117 ID12118 ID12119 ID12120 ID12121 ID12122 ID12123 ID12124 ID12125 ID12126 ID12127 ID12128 ID12129 ID12130 ID12131 ID12132 ID12133 ID12134 ID12135 ID12136 ID12137 ID12138 ID 12139 ID 12140 ID 12141 ID 12142 ID 12143 ID12144 ID 12145 ID 12146 ID12147 ID12148 ID12149 ID12150 ID12151 ID12152 ID12153 ID12154 ID12155 ID 12156 ID 12157 region income married 48 FEMALE INNER_CH 17546.0 NO 40 MALE TOWN 30085.1 YES 51 FEMALE INNER_CH 16575.4 YES 23 FEMALE TOWN 20375.4 YES 57 FEMALE RURAL 50576.3 YES 57 FEMALE TOWN 37869.6 YES 22 MALE RURAL 8877.07 NO 58 MALE TOWN 24946.6 YES 37 FEMALE SUBURBA 25304.3 YES 54 MALE TOWN 24212.1 YES 66 FEMALE TOWN 59803.9 YES 52 FEMALE INNER_Ch 26658.8 NO 44 FEMALE TOWN 15735.8 YES 66 FEMALE TOWN 55204.7 YES 36 MALE RURAL 19474.6 YES 38 FEMALE INNER_CH 22342.1 YES 37 FEMALE TOWN 17729.8 YES 46 FEMALE SUBURBA 41016.0 YES 62 FEMALE INNER_CH 26909 2 YES 31 MALE TOWN 22522.8 YES 61 MALE INNER_CH 57880.7 YES 50 MALE TOWN 16497.3 YES 54 MALE INNER_CH 38446.6 YES 27 FEMALE TOWN 15538.8 NO 22 MALE INNER_CH 12640.3 NO 56 MALE INNER CH 41034.0 YES 45 MALE INNER_CH 20809.7 YES 39 FEMALE TOWN 20114.0 YES 39 FEMALE INNER_CH 29359.1 NO 61 MALE RURAL 24270.1 YES 61 FEMALE RURAL 22942.9 YES 20 FEMALE TOWN 16325.8 YES 45 MALE SUBURBA 23443.2 YES 33 FEMALE INNER_Ch 29921.3 NO 43 MALE SUBURBAI 37521.9 NO 27 FEMALE INNER_CH 19868.0 YES 19 MALE RURAL 10953.0 YES 36 FEMALE RURAL 13381.0 NO 43 FEMALE TOWN 18504.3 YES 66 FEMALE SUBURBA 25391.5 NO 55 MALE TOWN 26774.2 YES 47 FEMALE INNER_CH 26952.6 YES 67 MALE TOWN 55716.5 NO 32 FEMALE TOWN 27571.5 YES 20 MALE INNER_CH 13740.0 NO 64 MALE INNER_CH 52670.6 YES 50 FEMALE INNER_Ch 13283.9 NO 29 MALE INNER_CH 13106.6 NO 52 MALE INNER_CH 39547.8 NO 47 FEMALE RURAL 17867 3 YES 24 MALE TOWN 14309.7 NO 36 MALE TOWN 23894.8 YES 43 MALE TOWN 16259.7 YES 48 MALE SUBURBA 29794.1 NO 63 MALE TOWN 56842.5 YES 52 FEMALE RURAL 47835.8 NO 58 FEMALE INNER_CH 24977.5 NO children car 1 NO 3 YES O YES 3 NO O NO 2 NO O NO O YES 2 YES 2 YES O NO O YES 1 NO 1 YES O NO O YES 2 NO O NO O NO O YES 2 NO 2 NO O NO O YES 2 YES O YES O NO 1 NO 3 YES 1 NO 2 NO 2 NO 1 YES 3 YES O NO 2 NO 3 YES O YES O YES 2 NO O NO O YES 2 YES O YES 2 YES 2 NO 1 YES 2 NO 2 YES 2 YES 2 YES O NO 1 NO 1 NO O NO 3 NO O NO save_act current_act mortgage pep NO NO NO YES NO YES YES NO YES YES NO NO NO YES NO NO YES NO NO NO YES YES NO YES NO YES NO YES YES YES NO NO NO NO NO NO YES YES NO NO YES YES NO NO YES YES YES NO YES YES YES YES YES YES YES YES YES YES YES NO YES YES YES NO NO NO YES NO YES NO YES NO YES NO NO YES YES YES NO NO YES NO NO YES YES YES NO NO YES YES NO NO YES YES YES NO YES YES NO NO YES YES YES YES YES YES NO NO YES NO YES NO YES YES NO NO NO YES YES YES NO NO YES NO NO NO YES YES NO YES YES NO NO NO YES YES NO YES YES NO NO YES YES NO NO YES YES YES YES NO NO NO YES NO NO NO YES YES YES NO YES NO NO YES NO NO YES NO YES YES NO YES YES YES NO YES YES YES YES YES YES NO YES YES YES YES YES NO YES NO YES YES NO NO NO YES NO NO NO NO NO NO NO YES YES NO YES YES YES NO YES YES YES YES NO YES NO NO YES NO YES NO YES YES YES NO NO 3. The marketing department of a financial firm keeps records on customers, including demographic information and the type of accounts. When launching a new product, such as a "Personal Equity Plan" (PEP), a direct mail piece, advertising the product, is sent to existing customers, and a record kept as to whether that customer responded and bought the product. Based on this store of prior experience, the managers decide to use data mining techniques to build customer profile models. In this problem we are interested only in deriving (quantitative) association rules from the data. The data contains the following fields: sex id a unique identification number age age of customer in years (numeric) MALE/FEMALE region inner_city/rural/suburban/town income income of customer (numeric) married is the customer married (YES/NO) children number of children (numeric) does the customer own a car (YES/NO) save_acct does the customer have a saving account (YES/NO) current_acct does the customer have a current account (YES/NO) mortgage does the customer have a mortgage (YES/NO) did the customer buy a PEP (Personal Equity Plan) after | the last mailing (YES/NO) car The data is contained in the file bank-data.csv. Each record is a customer description where the "pep" field indicates whether that customer bought a PEP after the last mailing. Your goal is to perform Association Rule discovery on the data set using the Weka package. Note: Association rule mining requires discretization of continuous variables. This task can be performed in the data transformation step or (in some cases) by the mining program. WEKA is a full data mining suite which includes various preprocessing modules (filters). When using WEKA, you will first apply the relevant preprocessing filters to transform the data before you perform association rule discovery. First perform the necessary preprocessing steps required for association rule mining. Specifically, the "id" field will need to be removed and the numerical attributes must be discretized. a. (2 points) Now perform association rule discovery on the transformed data. Experiment with different parameters so that you get at least 20-30 strong rules (e.g., rules with high lift and confidence which at the same time have relatively good support). In WEKA Apriori algorithm interface set "outputItemsets" to "True" so that you can also view the frequent items sets of different sizes in addition to the rules. Indicate what those strong rules are b. (3 points) Select the top 5 most "interesting" rules and for each specify the following: 1. an explanation of the pattern and why you believe it is interesting based on the business objectives of the company, 2. any recommendations based on the discovered rule that might help the company to better understand behavior of its customers or in its marketing campaign. Note: The top 5 most interesting rules are most likely not the top 5 in the result set of the Apriori algorithm. They are rules that, in addition to having high support, lift, and confidence, also provide some non-trivial, actionable knowledge based on the underlying business objectives. age sex NO id ID12101 ID12102 ID12103 ID 12104 ID12105 ID 12106 ID 12107 ID 12108 ID12109 ID12110 ID12111 ID12112 ID12113 ID 12114 ID 12115 ID12116 ID12117 ID12118 ID12119 ID12120 ID12121 ID12122 ID12123 ID12124 ID12125 ID12126 ID12127 ID12128 ID12129 ID12130 ID12131 ID12132 ID12133 ID12134 ID12135 ID12136 ID12137 ID12138 ID 12139 ID 12140 ID 12141 ID 12142 ID 12143 ID12144 ID 12145 ID 12146 ID12147 ID12148 ID12149 ID12150 ID12151 ID12152 ID12153 ID12154 ID12155 ID 12156 ID 12157 region income married 48 FEMALE INNER_CH 17546.0 NO 40 MALE TOWN 30085.1 YES 51 FEMALE INNER_CH 16575.4 YES 23 FEMALE TOWN 20375.4 YES 57 FEMALE RURAL 50576.3 YES 57 FEMALE TOWN 37869.6 YES 22 MALE RURAL 8877.07 NO 58 MALE TOWN 24946.6 YES 37 FEMALE SUBURBA 25304.3 YES 54 MALE TOWN 24212.1 YES 66 FEMALE TOWN 59803.9 YES 52 FEMALE INNER_Ch 26658.8 NO 44 FEMALE TOWN 15735.8 YES 66 FEMALE TOWN 55204.7 YES 36 MALE RURAL 19474.6 YES 38 FEMALE INNER_CH 22342.1 YES 37 FEMALE TOWN 17729.8 YES 46 FEMALE SUBURBA 41016.0 YES 62 FEMALE INNER_CH 26909 2 YES 31 MALE TOWN 22522.8 YES 61 MALE INNER_CH 57880.7 YES 50 MALE TOWN 16497.3 YES 54 MALE INNER_CH 38446.6 YES 27 FEMALE TOWN 15538.8 NO 22 MALE INNER_CH 12640.3 NO 56 MALE INNER CH 41034.0 YES 45 MALE INNER_CH 20809.7 YES 39 FEMALE TOWN 20114.0 YES 39 FEMALE INNER_CH 29359.1 NO 61 MALE RURAL 24270.1 YES 61 FEMALE RURAL 22942.9 YES 20 FEMALE TOWN 16325.8 YES 45 MALE SUBURBA 23443.2 YES 33 FEMALE INNER_Ch 29921.3 NO 43 MALE SUBURBAI 37521.9 NO 27 FEMALE INNER_CH 19868.0 YES 19 MALE RURAL 10953.0 YES 36 FEMALE RURAL 13381.0 NO 43 FEMALE TOWN 18504.3 YES 66 FEMALE SUBURBA 25391.5 NO 55 MALE TOWN 26774.2 YES 47 FEMALE INNER_CH 26952.6 YES 67 MALE TOWN 55716.5 NO 32 FEMALE TOWN 27571.5 YES 20 MALE INNER_CH 13740.0 NO 64 MALE INNER_CH 52670.6 YES 50 FEMALE INNER_Ch 13283.9 NO 29 MALE INNER_CH 13106.6 NO 52 MALE INNER_CH 39547.8 NO 47 FEMALE RURAL 17867 3 YES 24 MALE TOWN 14309.7 NO 36 MALE TOWN 23894.8 YES 43 MALE TOWN 16259.7 YES 48 MALE SUBURBA 29794.1 NO 63 MALE TOWN 56842.5 YES 52 FEMALE RURAL 47835.8 NO 58 FEMALE INNER_CH 24977.5 NO children car 1 NO 3 YES O YES 3 NO O NO 2 NO O NO O YES 2 YES 2 YES O NO O YES 1 NO 1 YES O NO O YES 2 NO O NO O NO O YES 2 NO 2 NO O NO O YES 2 YES O YES O NO 1 NO 3 YES 1 NO 2 NO 2 NO 1 YES 3 YES O NO 2 NO 3 YES O YES O YES 2 NO O NO O YES 2 YES O YES 2 YES 2 NO 1 YES 2 NO 2 YES 2 YES 2 YES O NO 1 NO 1 NO O NO 3 NO O NO save_act current_act mortgage pep NO NO NO YES NO YES YES NO YES YES NO NO NO YES NO NO YES NO NO NO YES YES NO YES NO YES NO YES YES YES NO NO NO NO NO NO YES YES NO NO YES YES NO NO YES YES YES NO YES YES YES YES YES YES YES YES YES YES YES NO YES YES YES NO NO NO YES NO YES NO YES NO YES NO NO YES YES YES NO NO YES NO NO YES YES YES NO NO YES YES NO NO YES YES YES NO YES YES NO NO YES YES YES YES YES YES NO NO YES NO YES NO YES YES NO NO NO YES YES YES NO NO YES NO NO NO YES YES NO YES YES NO NO NO YES YES NO YES YES NO NO YES YES NO NO YES YES YES YES NO NO NO YES NO NO NO YES YES YES NO YES NO NO YES NO NO YES NO YES YES NO YES YES YES NO YES YES YES YES YES YES NO YES YES YES YES YES NO YES NO YES YES NO NO NO YES NO NO NO NO NO NO NO YES YES NO YES YES YES NO YES YES YES YES NO YES NO NO YES NO YES NO YES YES YES NO NO