The first picture is the question and the second one is the outputs

In most populations the prevalence of obesity is greater in women than in men greater in people who are having family history of obesity Your rosk to conduct an experiment using Apriori algorithm to discover association rules thor help in finding out if there is relation between obesity levels gender & fomily history with overweight and validating the two hypotheses Obesity is greater in women than in men Obesity is greater in people who are having family history of obesity. Question 1 - Discover association rules between obesity level, gender & family history with overweight 1) Load Parts-ObesityDataSetarli 2) The following Apriori execution parameters should be applied in order to obtain the required rules Minsupport threshold downBound in Support parameteo:02 Minconfidence threshold metric Type and in Metric parameters: 075 Number of rules extracted rules parameters View the Generated sets of large comes 5) Save the result in text file-file Name PARI-3-APRIOR-GOumbers Question 2. Based on the given results of the Apriori execution answer the following question Minsupport threshold flowerBoundMin Support paramete: 0.15 Minconfidence threshold menetype and in Metrie parametes:09 Number of rules extracted tumules parameters: 15 View the Generated sets of large demsets 1) Interpret the output of the Generated sets of forge itemsets & Best tules? I Market Output (A): Click or tap here to enter text Output (8): Click or tap here to enter to Output (C): Click or tap here to enter text Output (D): Click or tap here to entertext Output (E): Pero Click or tap here to enter te Output (F): Click or tap here to enter text 2) From the results of the Apriori execution explain your findings by interpreting the result of the Apriori trecution "Best rules found Identity the relation between obesity levels. gender & family history with overweight and validate the two hypotheses Obesity is greater in women than in men. 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Fany history with overweight yet besty Level-esity_toe_11134 Gendereferale ) con 12.113 let.cm 1164) 02.03 6. Obesity Levity Tye_111 114) Generale Faily Ntory with vertigtes 113 COM(3) (3.45) devi(0.1) (18) 7. City Level Obesity Type_11286 -> Fly Na story with verweight-yes 2 con[:(1.17) Sev:(0.02) (4) Com21.10) 1. Fanily story with regte besity Lewbesity Type_ I! 2 > Gender Jule confc>.09) Sex (0.00) (67.523 . Obesity levelsttype_11216. General cond(0.7) 11/01 (0) 1334.17) 10. Obesity Level-lytys_11286) Generale fanily history with verweltes 264 con (0.90 (223) lev (0.5) (156) (2.1) 11. Obesity Level-esity_type] > Fly Niutery with verveligt conf{.08) :1.1) decu) (6.1% In most populations the prevalence of obesity is: greater in women than in men. greater in people who are having family history of obesity. Your task to conduct an experiment using Apriori algorithm to discover association rules that help in finding out if there is relation between obesity levels gender & family history with overweight and validating the two hypotheses: Obesity is greater in women thon in men. Obesity is greater in people who are having family history of obesity. Question 1 - Discover association rules between obesity levels, gender & family history with overweight 1) Load Part3-ObesityDataSetarff 2) The following Apriori execution parameters should be applied in order to obtain the required rules 1/4 Marks) Minsupport threshold (lowerBound Min Support parameter): 02 Minconfidence threshold (metric Type and minMetric parameters): 0.75 Number of rules extracted (numRules parameters): 5 View the Gonorated sets of large itemsets 5) Save the result in text file-file Name: PART-3-APRIORI-GroupNumber 1 Question 2. Based on the given results of the Apriori execution answer the following questions: Minsupport threshold (lower BoundMin Support parameter):0.15 Minconfidence threshold (metricType and minMetric parameters): 09 Number of rules extracted (numRules parameters): 15 View the Generated sots of large itemsets 1) Interpret the output of the Generated sets of large itemsets & Best rules? 1 Marks) Output (A): Click or tap here to enter text. Output (B): Click or tap here to enter toxt. Output (C): Click or tap hero to enter toxt. Output (D): Click or tap here to enter text. Output (E): Page 7 of 8 Click or tap here to enter text. Output (F): Click or top here to enter text 2) From the results of the Apriori execution, explain your findings by interpreting the result of the Apriori execution "Best rules found"? Identity the relation between obesity levels, gender & family history with overweight and validate the two hypotheses: Obesity is greater in women than in men, Obesity is greater in people who are having family history of obesity. In most populations the prevalence of obesity is: greater in women than in men. greater in people who are having family history of obesity. Your task to conduct an experiment using Apriori algorithm to discover association rules that help in finding out if there is relation between obesity levels gender & fomily history with overweight and validating the two hypotheses: Obesity is greater in women than in men. Obesity is greater in people who are having family history of obesity. Question 1 - Discover association rules between obesity levels, gender & family history with overweight: 1) Load Part3-ObesityDataSet.arff 2) The following Apriori execution parameters should be applied in order to obtain the required rules: 14 Marks) Minsupport threshold (lowerBoundMin Support parameter: 02 Minconfidence threshold (metricType and minMetric parameters): 0.75 Number of rules extracted (numRulos parameters): 5 View the Generated sets of largo itomsets 5) Save the result in text file - file Name: PART-3-APRIORI-GroupNumber.ba Question 2. Based on the given results of the Apriori execution answer the following questions: Minsupport threshold (lowerBoundMin Support parameter): 0.15 Minconfidence threshold (metnicType and minMetric parameters): 09 Number of rules extracted (numRules parameters): 15 View the Generated sets of large itemsets 1) Interpret the output of the Generated sets of large itemsets & Best rules? 1 / Marks) Output (A): Click or tap here to enter text. Output (B): Click or tap here to enter text Output (C): Click or tap hero to enter toxt. Output (D): Click or tap here to enter text, Output (E): Click or tap here to enter text. Output (F): Click or tap here to enter text. 2) From the results of the Apriori execution, explain your findings by interpreting the result of the Apriori execution "Best rules found ? Identity the relation between obesity levels, gender & family history with overweight and validate the two hypotheses: Obesity is greater in women than in men. Obesity is greater in people who are having family history of obesity. I 4 Marks] Run information - Schen: wka associations.Apriori 1 N IS-TO-C0.9 D O.OS U 1.0 - 0.155 -1.0 - - Relation: part).cbesityostaser-wek (alters.supervised attribute. Renove12.4-12 Instances: 1775 Attributes :) Gender Family history with overweight Obesity level ... Associator nodel (full training set) ... forlor! A . Mindes support: 0.15 (260 Intantes) Minimun metric confidence) 0.0 Mumber of cycles performedi 17 Generated sets of large itensets Site of set of large tenets (1) Large Itensets (1) Genderafenale Genderle 04 ily Nistory with overweight-yes 1512 obesity Level ornal_Mele 2) Obesity level Obesity Type 1 342 besity level-Obesity Type 11 286 coesity Level-Obesity Type_111 114 coesity Level Orweight su Size of set of large Itemets (2) D Large Itensets (2) Gendur fenale Family history with overweight-yes 710 Gender female coexity Level.obesity. Type_111 1) Genderuole family history with overweight-yos 702 Genderarlo coesity Level-coesity Type 11 204 Genderlo coesity Level-Overweight 320 Fanily history with overweight-yes Obesity level.cbesity Type_1"} Fantly history with overwelt-yos Obesity Level.coesity_type_11 20 santly history with overweight-yes Obesity Leveluesity Type_111334 Fastly history with overweight-yeu Obesity Loved-overweight 400 Size of set of large Itemets ); Large Itensets L(): Cenderafenale fanily history with overweight you coesity Level-Obesity_typo_111 31 Gender etalo Family history with over you Obelity Lovel-Obesity-Type_!! 284 JE Best rules found 1. Coesity Level-600-1ty_type_111 314 ..Family History with overweight-ye: 334 111:(1.17) Tev (0,0)) (46) Con (46.38) Ganderl010 Obesity level.Obesity type 11 280 .. fantly history with overweight-yos 284 coor(> 117(1.17) lev:(0.02) (47) convi(47.08) 4. coesity Level Obesity Type_111 314 .-> Gender.fenale Gender Feaale 113 conf:(1)> 117(2.11) lev:(0.09) [164) CONVI(12.08) 6. Obesity Level-Obesity. Type_11134 ) Gender female Fantly history with overweight-yes 11) conft(1)> 117(2.40) levi (0.1) (185) conv! (09.49) 7. coelity Level-esity_type_IT 20 --> Fanlly Nistory with overweight-yes 20 conf (1)> 111: (1.17) lov:(0.02) (41) Conv! (21.10) A family history with overeight-yes Obesity Level Obesity_type_11 28 --> Gender Mule 284 11t:(1.09) lev:(0.08) (134) conv:(67.52) 9. Doesity Lovel-Obesity Type_11 286.) Genderile 204 11t:(2.2)) dev:(0.09) (156) Conv! (5.a) 11. coesity Lovel-coctity_type_1 142 ..) Family history with overweighty Fly Na story with verweight-yes 2 con[:(1.17) Sev:(0.02) (4) Com21.10) 1. Fanily story with regte besity Lewbesity Type_ I! 2 > Gender Jule confc>.09) Sex (0.00) (67.523 . Obesity levelsttype_11216. General cond(0.7) 11/01 (0) 1334.17) 10. Obesity Level-lytys_11286) Generale fanily history with verweltes 264 con (0.90 (223) lev (0.5) (156) (2.1) 11. Obesity Level-esity_type] > Fly Niutery with verveligt conf{.08) :1.1) decu) (6.1% In most populations the prevalence of obesity is: greater in women than in men. greater in people who are having family history of obesity. Your task to conduct an experiment using Apriori algorithm to discover association rules that help in finding out if there is relation between obesity levels gender & family history with overweight and validating the two hypotheses: Obesity is greater in women thon in men. Obesity is greater in people who are having family history of obesity. Question 1 - Discover association rules between obesity levels, gender & family history with overweight 1) Load Part3-ObesityDataSetarff 2) The following Apriori execution parameters should be applied in order to obtain the required rules 1/4 Marks) Minsupport threshold (lowerBound Min Support parameter): 02 Minconfidence threshold (metric Type and minMetric parameters): 0.75 Number of rules extracted (numRules parameters): 5 View the Gonorated sets of large itemsets 5) Save the result in text file-file Name: PART-3-APRIORI-GroupNumber 1 Question 2. Based on the given results of the Apriori execution answer the following questions: Minsupport threshold (lower BoundMin Support parameter):0.15 Minconfidence threshold (metricType and minMetric parameters): 09 Number of rules extracted (numRules parameters): 15 View the Generated sots of large itemsets 1) Interpret the output of the Generated sets of large itemsets & Best rules? 1 Marks) Output (A): Click or tap here to enter text. Output (B): Click or tap here to enter toxt. Output (C): Click or tap hero to enter toxt. Output (D): Click or tap here to enter text. Output (E): Page 7 of 8 Click or tap here to enter text. Output (F): Click or top here to enter text 2) From the results of the Apriori execution, explain your findings by interpreting the result of the Apriori execution "Best rules found"? Identity the relation between obesity levels, gender & family history with overweight and validate the two hypotheses: Obesity is greater in women than in men, Obesity is greater in people who are having family history of obesity. In most populations the prevalence of obesity is: greater in women than in men. greater in people who are having family history of obesity. Your task to conduct an experiment using Apriori algorithm to discover association rules that help in finding out if there is relation between obesity levels gender & fomily history with overweight and validating the two hypotheses: Obesity is greater in women than in men. Obesity is greater in people who are having family history of obesity. Question 1 - Discover association rules between obesity levels, gender & family history with overweight: 1) Load Part3-ObesityDataSet.arff 2) The following Apriori execution parameters should be applied in order to obtain the required rules: 14 Marks) Minsupport threshold (lowerBoundMin Support parameter: 02 Minconfidence threshold (metricType and minMetric parameters): 0.75 Number of rules extracted (numRulos parameters): 5 View the Generated sets of largo itomsets 5) Save the result in text file - file Name: PART-3-APRIORI-GroupNumber.ba Question 2. Based on the given results of the Apriori execution answer the following questions: Minsupport threshold (lowerBoundMin Support parameter): 0.15 Minconfidence threshold (metnicType and minMetric parameters): 09 Number of rules extracted (numRules parameters): 15 View the Generated sets of large itemsets 1) Interpret the output of the Generated sets of large itemsets & Best rules? 1 / Marks) Output (A): Click or tap here to enter text. Output (B): Click or tap here to enter text Output (C): Click or tap hero to enter toxt. Output (D): Click or tap here to enter text, Output (E): Click or tap here to enter text. Output (F): Click or tap here to enter text. 2) From the results of the Apriori execution, explain your findings by interpreting the result of the Apriori execution "Best rules found ? Identity the relation between obesity levels, gender & family history with overweight and validate the two hypotheses: Obesity is greater in women than in men. Obesity is greater in people who are having family history of obesity. I 4 Marks] Run information - Schen: wka associations.Apriori 1 N IS-TO-C0.9 D O.OS U 1.0 - 0.155 -1.0 - - Relation: part).cbesityostaser-wek (alters.supervised attribute. Renove12.4-12 Instances: 1775 Attributes :) Gender Family history with overweight Obesity level ... Associator nodel (full training set) ... forlor! A . Mindes support: 0.15 (260 Intantes) Minimun metric confidence) 0.0 Mumber of cycles performedi 17 Generated sets of large itensets Site of set of large tenets (1) Large Itensets (1) Genderafenale Genderle 04 ily Nistory with overweight-yes 1512 obesity Level ornal_Mele 2) Obesity level Obesity Type 1 342 besity level-Obesity Type 11 286 coesity Level-Obesity Type_111 114 coesity Level Orweight su Size of set of large Itemets (2) D Large Itensets (2) Gendur fenale Family history with overweight-yes 710 Gender female coexity Level.obesity. Type_111 1) Genderuole family history with overweight-yos 702 Genderarlo coesity Level-coesity Type 11 204 Genderlo coesity Level-Overweight 320 Fanily history with overweight-yes Obesity level.cbesity Type_1"} Fantly history with overwelt-yos Obesity Level.coesity_type_11 20 santly history with overweight-yes Obesity Leveluesity Type_111334 Fastly history with overweight-yeu Obesity Loved-overweight 400 Size of set of large Itemets ); Large Itensets L(): Cenderafenale fanily history with overweight you coesity Level-Obesity_typo_111 31 Gender etalo Family history with over you Obelity Lovel-Obesity-Type_!! 284 JE Best rules found 1. Coesity Level-600-1ty_type_111 314 ..Family History with overweight-ye: 334 111:(1.17) Tev (0,0)) (46) Con (46.38) Ganderl010 Obesity level.Obesity type 11 280 .. fantly history with overweight-yos 284 coor(> 117(1.17) lev:(0.02) (47) convi(47.08) 4. coesity Level Obesity Type_111 314 .-> Gender.fenale Gender Feaale 113 conf:(1)> 117(2.11) lev:(0.09) [164) CONVI(12.08) 6. Obesity Level-Obesity. Type_11134 ) Gender female Fantly history with overweight-yes 11) conft(1)> 117(2.40) levi (0.1) (185) conv! (09.49) 7. coelity Level-esity_type_IT 20 --> Fanlly Nistory with overweight-yes 20 conf (1)> 111: (1.17) lov:(0.02) (41) Conv! (21.10) A family history with overeight-yes Obesity Level Obesity_type_11 28 --> Gender Mule 284 11t:(1.09) lev:(0.08) (134) conv:(67.52) 9. Doesity Lovel-Obesity Type_11 286.) Genderile 204 11t:(2.2)) dev:(0.09) (156) Conv! (5.a) 11. coesity Lovel-coctity_type_1 142 ..) Family history with overweighty