Kaggle competition: Prudential Life Insurance Assessment
<filter><EOS> 
def Individual1(data):
p =(((np.cos(((data["Medical_History_40"] + data["Medical_Keyword_3"])/2.0)) +(( data["Medical_History_23"] >=(-(data["Medical_History_4"])) ).astype(float)))- np.maximum(( data["BMI"]),(np.tanh(( np.tanh(data["SumMedicalKeywords"])-(data["Product_Info_4"] - data["Medical_Keyword_3"])))))) + 
(np.tanh(((data["Medical_History_20"] +(( data["InsuredInfo_6"] +(((data["Medical_History_15"] -(data["InsuredInfo_7"] + data["Insurance_History_2"])) - data["Medical_History_30"])- data["Medical_History_5"])) /2.0)) /2.0)) -(( np.ceil(data["Product_Info_4"])< data["InsuredInfo_5"] ).astype(float)))+
np.tanh(((((data["Medical_History_11"] +(( data["Medical_History_27"] + np.maximum(( data["Medical_Keyword_41"]),(data["Family_Hist_4"])))/2.0)) /2.0)+(((( np.sin(np.ceil(data["Product_Info_2_N"])) + data["Insurance_History_5"])/2.0)+(((data["Medical_History_13"] - data["Medical_History_18"])+ data["Family_Hist_2"])/2.0)) /2.0)) /2.0)) + 
(( data["Medical_History_35"] * data["InsuredInfo_2"])+(-(((data["Medical_Keyword_38"] >=(-(((( data["Ins_Age"] * data["Medical_History_31"])+(( data["Medical_History_31"] <=(( data["Medical_History_7"] <= np.minimum(( data["Medical_History_28"]),(data["Employment_Info_3"])) ).astype(float)) ).astype(float)))/2.0)))).astype(float)))))+
np.minimum(( 0.318310),(np.maximum(((data["Medical_History_15"] *(( 0.138462 <(data["Medical_History_23"] + np.minimum(( data["Medical_History_15"]),(0.318310)))).astype(float)))) ,(np.maximum(( np.minimum(( data["Medical_History_4"]),(((data["Medical_History_24"] >= data["Medical_History_23"] ).astype(float))))),(data["Medical_History_15"])))))) + 
(((( data["Medical_History_38"] <= data["Medical_History_24"] ).astype(float)) + np.sin(np.minimum(((( np.maximum(( data["Medical_History_4"]),(data["Medical_History_23"])) +(data["Medical_History_4"] / 2.0)) /2.0)) ,(( np.minimum(( data["Medical_History_11"]),(( data["Product_Info_2_N"] + np.abs(data["Product_Info_2_C"])))) * 2.0)))))/2.0)+ 
(0.094340 *(np.maximum(( data["Medical_Keyword_25"]),(np.tanh(data["SumMedicalKeywords"])))-(((data["InsuredInfo_1"] -(( data["Medical_Keyword_22"] + data["Medical_Keyword_33"])/2.0)) - np.minimum(( data["Medical_History_17"]),(data["SumEmploymentInfo"])))-(( data["Medical_History_1"] + data["Medical_History_33"])/2.0)))) +
np.tanh(( data["Medical_History_5"] *(((-(( data["Medical_History_3"] +(( data["Medical_History_23"] > np.tanh(data["BMI"])).astype(float)))))+(((data["Medical_Keyword_9"] -(( data["Medical_History_4"] > data["BMI"] ).astype(float)))- data["Medical_Keyword_19"])- data["Medical_History_6"])) /2.0)))+ 
(((( 0.094340 *(np.minimum(( data["Medical_Keyword_3"]),(((data["BMI"] + data["SumMedicalKeywords"])/2.0)))* 2.0)) * np.abs(data["BMI"])) +(( np.round(( 1.0/(1.0 + np.exp(- np.sin(data["Medical_History_11"])))))<= np.minimum(( data["BMI"]),(data["Ins_Age"])) ).astype(float)))/2.0)+ 
(np.minimum(( np.tanh(data["Medical_History_31"])) ,(np.maximum(((data["Product_Info_4"] *(( data["BMI"] +(( data["Wt"] + 0.693147)/2.0)) /2.0))),(np.minimum(( data["Medical_History_1"]),(((data["Wt"] +(data["SumNAs"] / 2.0)) /2.0)))))))/ 2.0)+ 
(((data["Medical_History_15"] + data["Medical_History_4"])/2.0)*(((np.sin(( data["Medical_History_4"] *(( data["Medical_History_15"] > np.sinh(data["Employment_Info_5"])).astype(float)))) * data["Medical_History_15"])> data["Product_Info_3"] ).astype(float)))+ 
(0.367879 -(1.0/(1.0 + np.exp(-(((data["Medical_History_40"] +(( data["Ins_Age"] + data["Insurance_History_8"])/2.0)) /2.0)-(( data["Medical_History_4"] <=(( data["Medical_History_23"] <=(( data["BMI"] <=(1.0/(1.0 + np.exp(- data["Medical_History_40"])))).astype(float)) ).astype(float)) ).astype(float)))))))+
np.minimum(( np.minimum(( np.minimum(( 0.058823),(((( data["Employment_Info_1"] +(( data["Insurance_History_3"] < np.maximum(( data["Ins_Age"]),(data["SumMedicalKeywords"])) ).astype(float)))/2.0)/ 2.0)))) ,(np.round(np.round(np.maximum(( data["Medical_History_23"]),(data["SumMedicalKeywords"]))))))),(np.maximum(( data["Medical_History_39"]),(data["SumMedicalKeywords"])))) + 
(np.minimum(( np.tanh(((-(data["SumNAs"])) * np.minimum(( data["Medical_History_22"]),(data["Insurance_History_2"]))))),(data["Medical_Keyword_39"])) *(((((data["Medical_Keyword_2"] - data["Medical_History_19"])- data["Employment_Info_2"])- data["Employment_Info_2"])<= data["Medical_Keyword_37"] ).astype(float)))+ 
(0.138462 *(((((np.maximum(( data["Medical_History_21"]),(data["InsuredInfo_7"])) - data["InsuredInfo_2"])+ data["Medical_History_6"])/2.0)+ np.round(((( data["Product_Info_2_N"] <= data["SumMedicalKeywords"] ).astype(float)) *(((data["Family_Hist_3"] >= data["Ins_Age"] ).astype(float)) - data["Product_Info_4"])))) /2.0)) +
np.sin(np.sinh(np.minimum(( data["Medical_History_11"]),(np.sin(( data["Medical_History_5"] *(((( np.ceil(data["Medical_History_19"])+(( data["Medical_Keyword_9"] + data["Product_Info_1"])/2.0)) /2.0)>(data["SumMedicalHistory"] + np.sinh(0.720430)) ).astype(float)))))))) + 
(0.058823 *(((np.maximum(( np.maximum(( np.maximum(( data["Medical_Keyword_45"]),(np.maximum(( data["Medical_Keyword_34"]),(data["Medical_History_32"]))))),(data["Medical_Keyword_2"]))),(( data["Medical_Keyword_3"] * data["Medical_History_30"])))+ data["Product_Info_4"])+(data["Medical_Keyword_3"] * data["InsuredInfo_6"])) / 2.0)) +
np.minimum(((data["InsuredInfo_1"] * np.minimum(((data["Medical_History_20"] *(0.693147 -(1.0/(1.0 + np.exp(- np.cos(data["Insurance_History_5"]))))))),(( np.maximum(( data["Insurance_History_5"]),(( -(data["Insurance_History_3"])))) - data["Insurance_History_3"]))))),(( data["Insurance_History_5"] * data["Insurance_History_3"])))+
np.sin(( np.maximum(( data["Medical_History_28"]),(( data["Medical_History_24"] *(( data["Medical_Keyword_36"] + np.floor(( -(data["Medical_History_10"])))) /2.0)))) *(((( data["Medical_History_13"] >= data["Medical_History_10"] ).astype(float)) <= np.tanh(data["Medical_History_39"])).astype(float)))) +
np.tanh(((data["BMI"] *(((((data["InsuredInfo_7"] >= np.ceil(data["Product_Info_4"])).astype(float)) >= np.sin(np.maximum(((((( data["Ins_Age"] + data["InsuredInfo_2"])/2.0)< data["Medical_History_4"] ).astype(float))),(data["Medical_History_15"])))).astype(float)) / 2.0)) / 2.0)) +
np.minimum(( np.sin(( data["SumMedicalKeywords"] * np.tanh(((np.ceil(data["Product_Info_4"])<=(( data["Medical_History_30"] + data["Medical_History_18"])/2.0)).astype(float)))))) ,(( data["Product_Info_1"] *(( data["BMI"] >(( data["Medical_History_18"] < np.round(data["Medical_Keyword_9"])).astype(float)) ).astype(float)))))+
np.minimum(((0.138462 *(( data["Medical_History_1"] >= data["Medical_History_18"] ).astype(float)))) ,(np.tanh(((((((((data["Medical_History_1"] >= data["Medical_History_1"] ).astype(float)) <= data["Family_Hist_3"] ).astype(float)) +(( data["Medical_History_37"] <= np.floor(np.abs(data["Product_Info_2_N"])) ).astype(float)))/2.0)+ data["Medical_Keyword_27"])/2.0)))) +
np.minimum(( 0.094340),(( np.cos(data["Employment_Info_2"])* np.tanh(((data["Employment_Info_2"] >(( data["Medical_History_23"] <=(np.sin(( data["InsuredInfo_6"] - data["Medical_History_15"])) - data["Medical_History_39"])).astype(float)) ).astype(float)))))) +
np.minimum(((( data["Medical_History_15"] +(( data["Medical_Keyword_1"] >(( data["Medical_History_15"] +(( data["Medical_History_4"] >(( data["Medical_Keyword_10"] > data["Medical_Keyword_23"] ).astype(float)) ).astype(float)))/2.0)).astype(float)))/2.0)) ,(((((data["Medical_History_15"] > 8.0 ).astype(float)) >= np.maximum(( data["Medical_History_4"]),(data["Medical_History_15"])) ).astype(float)))) +
np.minimum(((np.sin(( data["Product_Info_5"] * np.ceil(((data["InsuredInfo_6"] * np.minimum(((data["Medical_History_4"] - data["Medical_Keyword_3"])) ,(data["Insurance_History_3"])))+ data["Medical_History_40"])))) / 2.0)) ,(((np.ceil(data["Medical_History_4"])< 0.094340 ).astype(float)))) +
np.sin(np.minimum(((( data["Insurance_History_2"] >= data["Medical_History_20"] ).astype(float))),(np.maximum(( data["Medical_Keyword_3"]),(( np.tanh(data["Medical_Keyword_24"])* np.minimum(((( data["Medical_History_4"] - data["Medical_Keyword_46"])+(data["Product_Info_3"] +(-(data["BMI"]))))),(data["Insurance_History_2"])))))))) +
np.minimum(((np.cos(np.maximum(((int(0.138462 >= 0.138462))),(np.minimum(( data["Ht"]),(2.212120)))))/ 2.0)) ,(((( data["SumMedicalKeywords"] + np.round(data["Ins_Age"])) /2.0)*(( data["Medical_History_5"] >=(( data["Medical_History_17"] + data["Medical_History_20"])/2.0)).astype(float)))))+ 
(np.minimum(((( data["Medical_History_38"] > data["Ins_Age"] ).astype(float))),(( 0.730769 -(1.0/(1.0 + np.exp(- np.maximum(( data["Employment_Info_1"]),(np.maximum(( np.maximum(( data["Medical_Keyword_47"]),(((data["Medical_History_38"] + data["Medical_History_12"])/2.0)))) ,(np.maximum(( data["Ins_Age"]),(np.cos(data["InsuredInfo_1"])))))))))))))/ 2.0)+ 
(np.ceil(data["Medical_History_4"])*(0.094340 * np.ceil(((((np.maximum(( data["Medical_Keyword_39"]),(( data["Product_Info_3"] * np.maximum(((data["Medical_History_23"] * 2.0)) ,(data["Medical_Keyword_21"])))))> data["Medical_History_23"] ).astype(float)) - np.ceil(data["InsuredInfo_2"])) / 2.0)))) + 
(np.round(np.minimum(((-(((data["Insurance_History_5"] >(( data["Insurance_History_7"] < np.tanh(((data["Insurance_History_5"] +(data["Medical_History_20"] * data["Medical_Keyword_31"])) /2.0)) ).astype(float)) ).astype(float))))),(((((2.409090 >= data["Ins_Age"] ).astype(float)) - data["InsuredInfo_6"])/ 2.0)))) * 2.0)+
np.maximum(( np.sinh(((np.cos(( data["Ht"] / 2.0)) < np.minimum(( data["Medical_Keyword_14"]),(data["Medical_Keyword_34"])) ).astype(float)))) ,(np.minimum(((( data["Medical_Keyword_32"] > np.minimum(((data["SumInsuredInfo"] + 1.584910)) ,(np.cos(data["Medical_Keyword_39"])))).astype(float))),(( data["Product_Info_2_C"] / 2.0)))))+ 
(( np.minimum(( data["Medical_History_10"]),(data["Medical_Keyword_14"])) >(-(((data["Medical_History_5"] *(data["Medical_History_5"] *(( data["InsuredInfo_6"] >= data["InsuredInfo_3"] ).astype(float)))) *(( data["InsuredInfo_3"] >=(data["SumEmploymentInfo"] +(data["Insurance_History_2"] + data["Medical_History_30"])) ).astype(float)))))).astype(float)) + 
(np.minimum(( np.maximum(((( data["InsuredInfo_2"] > np.maximum(( data["Medical_Keyword_25"]),(data["Medical_History_13"])) ).astype(float))),(data["Medical_Keyword_46"]))),(data["Medical_History_28"])) *(( data["InsuredInfo_2"] >= np.ceil(((( data["Medical_History_18"] *(-(data["InsuredInfo_1"])))+ data["Medical_History_22"])/2.0)) ).astype(float)))+
np.tanh(( data["Insurance_History_2"] *(( data["Medical_History_19"] -(data["Medical_Keyword_44"] - data["Medical_Keyword_31"])) *(((data["Medical_Keyword_17"] * data["Medical_History_20"])>(data["Medical_Keyword_17"] *(data["Medical_Keyword_30"] *(( data["Medical_Keyword_32"] > data["Medical_Keyword_39"] ).astype(float)))) ).astype(float)))))+ 
(-(np.minimum(((( data["Medical_History_27"] <= np.sin(( data["Medical_Keyword_21"] + data["Medical_History_10"])) ).astype(float))),(((data["Medical_Keyword_27"] >=(( -(np.round(np.minimum(( np.minimum(( data["Medical_History_15"]),(data["Medical_History_3"]))),(( data["Ins_Age"] / 2.0)))))) * 2.0)).astype(float)))))) +
np.maximum(( np.minimum(( data["InsuredInfo_5"]),(((data["Medical_Keyword_42"] > data["Medical_History_6"] ).astype(float))))),(( np.sin(np.maximum(( np.maximum(((( 2.212120 <=(data["SumNAs"] -(( data["Medical_Keyword_42"] > data["Employment_Info_2"] ).astype(float)))).astype(float))),(np.ceil(data["Medical_Keyword_26"])))) ,(data["Medical_Keyword_27"])))/ 2.0)))+ 
(((( data["Family_Hist_3"] >(( data["Medical_History_33"] +(5.428570 +(( data["Wt"] +(( data["Product_Info_3"] <= data["Product_Info_7"] ).astype(float)))/2.0)))/2.0)).astype(float)) +(( data["Family_Hist_2"] <(data["Ins_Age"] -(5.200000 + data["Family_Hist_1"])) ).astype(float)))/2.0)+ 
(np.maximum(( data["Medical_History_4"]),(( -(data["Medical_Keyword_5"])))) *(-(((np.tanh(((data["InsuredInfo_5"] + data["Medical_History_40"])/2.0)) > np.cos(((( np.minimum(( 9.869604),(data["Medical_Keyword_26"])) >= data["InsuredInfo_6"] ).astype(float)) / 2.0)) ).astype(float)))))+
np.floor(np.cos(((((data["SumFamilyHistory"] < data["InsuredInfo_6"] ).astype(float)) + np.minimum(( data["Product_Info_3"]),(((np.floor(( data["Medical_Keyword_14"] *(-(((data["Product_Info_1"] >= data["Medical_History_14"] ).astype(float)))))) / 2.0)-(data["Product_Info_3"] * data["Medical_History_11"])))))/2.0)))+ 
(( np.ceil(np.maximum(( np.maximum(( np.round(data["Insurance_History_9"])) ,(data["Medical_History_11"]))),(data["Employment_Info_6"])))==(((((((data["Medical_History_35"] < data["Medical_Keyword_3"] ).astype(float)) * data["Medical_History_39"])+ data["Medical_Keyword_3"])/2.0)>(( data["Medical_History_10"] < data["Medical_History_11"] ).astype(float)) ).astype(float)) ).astype(float)) +
np.sin(((((((( data["Product_Info_7"] + data["Medical_Keyword_31"])/2.0)> data["Medical_Keyword_28"] ).astype(float)) > np.cos(data["Medical_Keyword_40"])).astype(float)) * np.maximum(( data["Medical_Keyword_42"]),(( np.maximum(( data["Medical_Keyword_42"]),(data["Medical_History_40"])) / 2.0)))))+ 
(((( data["Medical_History_8"] + data["Medical_History_28"])< data["Medical_Keyword_15"] ).astype(float)) * np.sinh(( 0.058823 -(0.434294 *(((((( data["Medical_History_12"] - data["Medical_History_30"])- data["Medical_Keyword_3"])>= data["Medical_History_39"] ).astype(float)) >= data["Medical_History_40"] ).astype(float)))))) + 
(( np.cos(data["Medical_History_19"])< np.maximum(((((( np.minimum(( np.minimum(( data["Medical_History_21"]),(data["Medical_History_19"]))),(data["Medical_History_18"])) > np.cos(np.maximum(( data["Medical_History_9"]),(data["Medical_History_7"])))).astype(float)) > np.cos(data["Insurance_History_2"])).astype(float))),(np.minimum(( data["Insurance_History_2"]),(data["Medical_History_9"])))) ).astype(float)) +
np.minimum(((((((( data["SumInsuredInfo"] >= np.maximum(( data["Insurance_History_7"]),(data["BMI"])) ).astype(float)) * data["Medical_Keyword_34"])> 1.570796 ).astype(float)) / 2.0)) ,(( np.cos(data["Medical_History_29"])+(((data["Medical_Keyword_32"] < np.floor(data["Employment_Info_4"])).astype(float)) * 2.0)))) + 
(((data["Medical_History_29"] >(1.0/(1.0 + np.exp(- np.sinh(((((((data["Medical_History_29"] > data["Medical_Keyword_41"] ).astype(float)) -(( data["Medical_Keyword_29"] +(( data["Medical_History_35"] + data["Medical_Keyword_6"])/2.0)) /2.0)) > data["InsuredInfo_4"] ).astype(float)) -(data["Medical_Keyword_10"] * data["Medical_Keyword_1"])))))) ).astype(float)) / 2.0)+ 
(((( data["Medical_History_1"] > data["Product_Info_5"] ).astype(float)) < np.minimum(( np.maximum(( data["Medical_History_40"]),(data["BMI"]))),(( np.maximum(( data["Medical_Keyword_21"]),(data["Medical_Keyword_33"])) *(((( data["Product_Info_7"] > np.maximum(( data["Medical_Keyword_31"]),(data["Medical_Keyword_31"])) ).astype(float)) > data["Medical_History_40"] ).astype(float)))))).astype(float)) +
np.minimum(((( data["Medical_History_33"] * np.floor(np.abs(((data["Ins_Age"] + np.minimum(((( np.minimum(( data["Medical_Keyword_2"]),(data["Insurance_History_9"])) > data["Medical_Keyword_24"] ).astype(float))),(data["Medical_Keyword_41"])))/2.0)))) / 2.0)) ,(( data["Medical_Keyword_28"] *(( data["Medical_History_21"] > data["Medical_Keyword_38"] ).astype(float)))))+
np.sin(((data["Employment_Info_5"] *(((((data["Medical_History_19"] >(data["Medical_Keyword_6"] * data["Ins_Age"])).astype(float)) + data["Medical_History_32"])>(( data["Employment_Info_5"] > data["Medical_History_19"] ).astype(float)) ).astype(float)))*(( data["Medical_Keyword_6"] < data["Product_Info_4"] ).astype(float)))) +
np.maximum(( np.sin(np.minimum(( data["InsuredInfo_1"]),(((data["Medical_Keyword_8"] > data["Medical_History_40"] ).astype(float)))))) ,(( data["InsuredInfo_1"] * np.tanh(np.ceil(( -(( data["Medical_History_6"] *(( data["Medical_Keyword_48"] <= np.maximum(( data["Medical_History_5"]),(data["Product_Info_1"])) ).astype(float)))))))))) +
np.ceil(np.sin(np.round(np.maximum(( np.maximum(((data["Medical_Keyword_28"] * data["Medical_Keyword_20"])) ,(((data["Family_Hist_4"] * data["Medical_Keyword_43"])- data["Medical_Keyword_46"])))) ,(np.sinh(np.minimum(( data["Medical_Keyword_19"]),(((data["Medical_History_5"] >= np.cos(data["Medical_Keyword_26"])).astype(float)))))))))) +
np.tanh(( -(( data["Medical_Keyword_4"] *(( data["Medical_Keyword_23"] <=(data["InsuredInfo_7"] -(((data["Medical_Keyword_14"] * data["Medical_Keyword_15"])<(((data["Medical_History_37"] - data["Medical_Keyword_23"])<(( data["Medical_History_22"] > data["Medical_Keyword_12"] ).astype(float)) ).astype(float)) ).astype(float)))).astype(float)))))) +
np.minimum(( 0.138462),(((( np.abs(data["Ins_Age"])/ 2.0)>=(((( data["Ins_Age"] < np.round(((( data["Medical_History_12"] <= 0.730769 ).astype(float)) +(( data["InsuredInfo_3"] >= data["Medical_Keyword_10"] ).astype(float)))) ).astype(float)) != np.abs(np.floor(data["Product_Info_2_C"])) ).astype(float)) ).astype(float)))) +
np.sin(np.maximum(( data["Medical_Keyword_43"]),(( 0.094340 * np.minimum(((((data["Medical_Keyword_15"] < data["Medical_History_32"] ).astype(float)) +(data["Medical_History_26"] *(( data["Insurance_History_8"] <= data["Product_Info_2_C"] ).astype(float))))),(( data["BMI"] * np.tanh(data["Product_Info_4"])))))))) + 
(((((data["Medical_Keyword_41"] >= data["SumMedicalKeywords"] ).astype(float)) *(-(((((data["Medical_History_12"] >(data["Medical_Keyword_38"] * data["Medical_History_1"])).astype(float)) > np.sin(np.maximum(( np.cos(np.minimum(( data["Medical_History_13"]),(data["Medical_History_39"])))) ,(data["Medical_History_1"])))).astype(float)))))/ 2.0)/ 2.0)+
np.minimum(( np.maximum(( np.cos(data["Product_Info_2_N"])) ,(data["Medical_History_37"]))),(( 0.058823 *(((( data["Product_Info_2_N"] >= np.maximum(( 0.058823),(data["Medical_History_3"])) ).astype(float)) >= np.maximum(( np.maximum(( data["Product_Info_6"]),(data["SumInsuranceHistory"]))),(( data["Product_Info_2_N"] * data["Medical_History_4"])))).astype(float)))))+ 
(np.ceil(np.sin(((( np.minimum(( data["Medical_Keyword_38"]),(data["Medical_Keyword_13"])) >(1.0/(1.0 + np.exp(- data["Medical_History_7"])))).astype(float)) + np.minimum(( data["Medical_History_28"]),(((data["Medical_Keyword_38"] + data["Medical_Keyword_5"])*(1.0/(1.0 + np.exp(-(data["Medical_History_7"] + data["Product_Info_2_N"])))))))))) * 2.0)+ 
(data["BMI"] *(np.ceil(( data["Ins_Age"] -(( data["Insurance_History_3"] > data["InsuredInfo_1"] ).astype(float)))) * np.abs(((0.058823 + np.minimum(( 0.138462),(((data["Ins_Age"] >=(( data["InsuredInfo_1"] <= data["Ins_Age"] ).astype(float)) ).astype(float)))))/2.0)))) + 
(( np.tanh(data["Medical_Keyword_40"])*(( data["Medical_Keyword_38"] * np.maximum(( data["Product_Info_4"]),(((np.maximum(( data["Product_Info_4"]),(((data["Medical_History_29"] >= data["SumMedicalKeywords"] ).astype(float)))) >= data["SumMedicalKeywords"] ).astype(float)))))* np.abs(((data["Medical_History_3"] + data["Medical_History_12"])/2.0)))) / 2.0)+
np.minimum(((-(np.minimum(( data["Medical_Keyword_33"]),(((np.sin(data["Medical_Keyword_33"])>= np.cos(data["Medical_History_24"])).astype(float))))))),(np.floor(np.cos(( np.sin(data["Medical_Keyword_46"])-(( data["Medical_Keyword_23"] >(data["Medical_Keyword_43"] * data["Medical_Keyword_47"])).astype(float)))))))+ 
(0.138462 * np.sin(((( np.maximum(( np.minimum(( data["BMI"]),(data["BMI"]))),(np.round(data["Medical_Keyword_43"])))==(( data["Product_Info_4"] > data["Medical_History_40"] ).astype(float)) ).astype(float)) * data["SumMedicalKeywords"])))+
np.round(( np.minimum(((data["Ht"] + data["Product_Info_5"])) ,(((data["Employment_Info_5"] +(( data["Medical_Keyword_6"] +(data["Medical_History_7"] +(( data["Medical_Keyword_13"] + data["Employment_Info_5"])/2.0)))/2.0)) /2.0)))*(( data["Product_Info_5"] >(data["Wt"] + 2.212120)).astype(float)))) + 
(((( data["Medical_History_9"] > data["Medical_History_41"] ).astype(float)) <= np.minimum(( data["InsuredInfo_5"]),(((np.maximum(( data["InsuredInfo_1"]),(((np.maximum(( data["Medical_Keyword_12"]),(( data["Medical_History_9"] * data["Medical_History_37"])))+(( data["BMI"] + data["Medical_Keyword_12"])/2.0)) /2.0)))+ data["Medical_Keyword_1"])/2.0)))).astype(float)) +
np.tanh(((data["Medical_Keyword_12"] - 0.367879)*(( data["Medical_History_39"] >=(( 1.0 > np.minimum(( data["Ins_Age"]),(( data["Medical_History_18"] - np.minimum(( data["Medical_Keyword_14"]),(( np.maximum(( data["Medical_Keyword_14"]),(data["Medical_History_39"])) - data["Medical_History_32"])))))) ).astype(float)) ).astype(float)))) + 
(( -(((((data["Insurance_History_7"] >=(( -(data["Insurance_History_4"])) / 2.0)).astype(float)) ==(( data["Insurance_History_9"] < np.minimum(( data["Medical_Keyword_41"]),(((np.tanh(data["Insurance_History_9"])+(data["Medical_Keyword_13"] / 2.0)) / 2.0)))).astype(float)) ).astype(float)))) * 2.0))
return p+4.5
def Individual2(data):
p =(((((( data["Product_Info_4"] + data["Medical_History_4"])> data["Medical_Keyword_3"] ).astype(float)) -(((( data["SumMedicalKeywords"] + np.maximum(( data["BMI"]),(data["Medical_Keyword_3"])))/2.0)+ data["BMI"])/2.0)) + np.sin(((data["Medical_History_23"] >(( data["Medical_Keyword_3"] + data["BMI"])/2.0)).astype(float)))) + 
(((((((( data["Medical_History_15"] - data["InsuredInfo_5"])- data["Medical_History_5"])+(( data["Family_Hist_4"] - data["Insurance_History_2"])- data["InsuredInfo_7"])) /2.0)+(( data["Medical_History_40"] +(-(data["Medical_History_30"])))/2.0)) /2.0)+(( data["Medical_History_15"] + data["Medical_History_4"])/2.0)) /2.0)+
np.tanh(np.minimum(((data["Medical_History_20"] + data["Medical_History_11"])) ,(((np.ceil(np.minimum(( data["Product_Info_4"]),(((data["Medical_History_7"] +(data["Medical_History_27"] +(-(data["Medical_Keyword_38"])))) /2.0)))) +(( data["BMI"] + np.maximum(( data["InsuredInfo_6"]),(data["Product_Info_4"])))/2.0)) /2.0)))) + 
(((( data["Medical_History_24"] >= data["Medical_Keyword_41"] ).astype(float)) +(((((np.minimum(((data["Employment_Info_2"] * np.round(data["Medical_History_28"]))),(data["Product_Info_2_N"])) + data["Medical_History_13"])+(( data["Medical_Keyword_41"] + data["Family_Hist_2"])/2.0)) /2.0)+(( data["Insurance_History_5"] + data["Medical_Keyword_25"])/2.0)) /2.0)) /2.0)+ 
(0.138462 *(data["SumMedicalKeywords"] +(((((( data["Medical_History_31"] - data["Medical_History_35"])-(data["Medical_History_18"] - data["Medical_Keyword_33"])) - data["InsuredInfo_1"])+ np.maximum(( data["Medical_History_21"]),(data["InsuredInfo_6"])))/2.0)- data["Ins_Age"])))+
np.minimum(((( np.sin(np.maximum(( data["InsuredInfo_2"]),(data["Medical_History_15"])))+(( np.tanh(((((data["Medical_History_1"] + data["Medical_History_3"])/2.0)+ data["Medical_History_33"])/2.0)) / 2.0)-(( data["InsuredInfo_2"] >= data["Medical_History_17"] ).astype(float)))) /2.0)) ,(np.cos(( -(data["Ins_Age"])))))+
np.minimum(( np.sin(np.maximum(( np.abs(data["Medical_History_11"])) ,(( data["Medical_Keyword_9"] * 2.0))))),(( np.sin(np.abs(np.maximum(( np.maximum(( data["Medical_Keyword_15"]),(data["SumNAs"]))),(data["Medical_History_11"])))) +(( np.abs(data["Family_Hist_4"])+ data["SumNAs"])/2.0)))) +
np.tanh(np.sin(((data["Medical_History_15"] <=(( np.sin(((((((np.sin(( data["Product_Info_2_N"] * 2.0)) >= 0.840000 ).astype(float)) >=(data["Medical_History_4"] / 2.0)).astype(float)) > np.abs(data["Product_Info_4"])).astype(float)))/ 2.0)- 0.840000)).astype(float)))) + 
(((data["Medical_Keyword_47"] <(( data["Medical_History_15"] + data["Medical_History_22"])/2.0)).astype(float)) * np.minimum(((( data["Medical_History_15"] == 0.094340 ).astype(float))),(((( data["Medical_History_15"] +(( data["BMI"] + data["Medical_History_15"])+ data["Medical_History_15"])) /2.0)+ data["Medical_History_15"])))) + 
(((((( data["Medical_History_1"] >= data["Medical_History_30"] ).astype(float)) / 2.0)+(-(((data["Medical_History_6"] <=(( np.cos(data["Wt"])<=(((( data["Medical_History_18"] <= np.ceil(data["Product_Info_4"])).astype(float)) <=(( data["Medical_History_6"] <= data["Medical_History_30"] ).astype(float)) ).astype(float)) ).astype(float)) ).astype(float)))))/2.0)/ 2.0)+ 
(((((1.0/(1.0 + np.exp(- np.floor(data["Medical_History_15"])))) >(np.abs(data["Medical_History_15"])+(( data["Product_Info_4"] >(( data["Family_Hist_5"] != data["BMI"] ).astype(float)) ).astype(float)))).astype(float)) +(( np.minimum(( data["Ins_Age"]),(data["BMI"])) >(( data["BMI"] != data["Family_Hist_2"] ).astype(float)) ).astype(float)))/2.0)+
np.minimum(( 0.585714),(np.maximum(( np.minimum(( data["InsuredInfo_6"]),(data["Medical_Keyword_3"]))),(( np.cos(data["Product_Info_2_C"])*(((( 0.318310 <=(( data["Medical_Keyword_41"] + np.abs(data["Product_Info_2_C"])) /2.0)).astype(float)) +(( data["Medical_History_10"] + data["Product_Info_2_N"])/2.0)) /2.0)))))) +
np.minimum(((( np.floor(((data["Family_Hist_3"] +(( np.floor(((data["BMI"] + data["Medical_History_19"])/2.0)) <= data["Medical_History_39"] ).astype(float)))/2.0)) / 2.0)/ 2.0)) ,(np.cos(( data["Product_Info_4"] -(1.0/(1.0 + np.exp(-(( data["BMI"] <= np.cos(data["Family_Hist_3"])).astype(float)))))))))+
np.minimum(((data["SumMedicalKeywords"] * np.sinh(((data["Medical_History_18"] > data["Medical_History_39"] ).astype(float))))),(np.minimum(((( data["Medical_History_5"] > data["Medical_History_20"] ).astype(float))),(( data["SumMedicalKeywords"] * np.sinh(((np.sinh(((data["Medical_History_5"] > data["Medical_History_17"] ).astype(float)))> data["Medical_History_20"] ).astype(float)))))))) +
np.maximum(((0.058823 * data["Family_Hist_2"])) ,(np.tanh(np.minimum(( 0.301030),(np.maximum(((((((data["Medical_Keyword_34"] + data["InsuredInfo_7"])/2.0)+(( data["Medical_Keyword_19"] > np.cos(np.minimum(( data["SumInsuredInfo"]),(data["InsuredInfo_7"])))).astype(float)))/2.0)/ 2.0)) ,(data["Medical_Keyword_45"])))))))+ 
(( data["Ins_Age"] * np.sin(( np.sin(np.minimum(( 0.367879),(np.minimum(( np.maximum(( data["Wt"]),(data["Medical_Keyword_23"]))),(np.cos(data["Medical_History_18"])))))) / 2.0)))/ 2.0)+
np.minimum(((( np.sin(data["Medical_History_22"])+(((np.sin(np.cos(data["Ins_Age"])) *(( data["Family_Hist_5"] + data["Medical_History_29"])/2.0)) + data["Medical_Keyword_2"])/2.0)) /2.0)) ,(( data["Family_Hist_5"] *(0.094340 * np.cos(data["SumNAs"])))))+ 
(((np.minimum(( data["Medical_Keyword_6"]),(((( data["Employment_Info_1"] + data["Employment_Info_5"])+(( data["Employment_Info_2"] > data["Insurance_History_8"] ).astype(float)))/ 2.0)))/ 2.0)+(np.minimum(( 0.094340),(((data["InsuredInfo_6"] > data["Insurance_History_8"] ).astype(float)))) * 2.0)) /2.0)+ 
(((data["SumMedicalKeywords"] *(( data["InsuredInfo_2"] >=(data["SumMedicalKeywords"] *(( data["Medical_History_30"] >= np.cos(( data["InsuredInfo_6"] +(data["Medical_Keyword_15"] + data["Medical_History_23"])))).astype(float)))).astype(float)))+ np.sin(((-(data["Medical_History_38"])) / 2.0)))/2.0)+
np.ceil(np.minimum(( np.maximum(( np.sin(np.minimum(((data["Medical_Keyword_46"] * 8.0)) ,(data["InsuredInfo_6"])))) ,(np.sinh(np.minimum(( data["Medical_Keyword_34"]),(data["Medical_Keyword_14"])))))) ,(( data["Insurance_History_5"] + data["Insurance_History_1"])))) +
np.floor(((( 1.0/(1.0 + np.exp(- data["Medical_History_32"])))+(np.tanh(data["BMI"])/ 2.0)) + np.round(( data["BMI"] * np.tanh(np.minimum(((data["Medical_History_11"] * data["Medical_History_5"])) ,(np.cos(( 1.0/(1.0 + np.exp(- data["InsuredInfo_5"])))))))))))+ 
(((np.maximum(( data["Wt"]),(data["Medical_History_28"])) - np.cos(( data["BMI"] * 2.0)))-(( data["Employment_Info_4"] > data["Medical_Keyword_25"] ).astype(float)))* np.minimum(( data["Medical_History_35"]),(((data["Insurance_History_5"] > data["Wt"] ).astype(float)))))+
np.sin(np.maximum(((( data["Medical_Keyword_25"] + np.minimum(( data["InsuredInfo_5"]),(data["Medical_Keyword_1"])))/2.0)) ,(np.maximum(( data["Medical_Keyword_25"]),(( data["Medical_Keyword_11"] *(( data["Medical_Keyword_43"] > np.sin(np.round(((np.maximum(( data["Medical_Keyword_25"]),(data["Product_Info_5"])) + data["Medical_History_7"])/2.0)))).astype(float)))))))) + 
(((-(np.floor(data["Product_Info_4"])))* 0.138462)*(np.sin(data["Medical_History_4"])* np.tanh(np.minimum(( np.minimum(( np.sin(data["Medical_Keyword_42"])) ,(data["Medical_History_1"]))),(np.round(data["Medical_History_4"])))))) +
np.minimum(((( 0.058823 <= np.minimum(( data["Medical_History_30"]),(((data["Medical_Keyword_32"] + data["Medical_Keyword_3"])/2.0)))).astype(float))),(np.cos(np.minimum(( np.minimum(( data["BMI"]),(data["Medical_History_4"]))),(( -(( np.sin(np.sinh(np.sin(np.sinh(data["BMI"])))) * 2.0)))))))) + 
(np.minimum(( 0.094340),(((((np.maximum(( data["Medical_Keyword_37"]),(data["Medical_History_23"])) <= data["BMI"] ).astype(float)) + data["Insurance_History_2"])/2.0)))*(( data["Medical_Keyword_15"] +(((data["BMI"] * data["Insurance_History_2"])+ data["SumMedicalKeywords"])/2.0)) /2.0)) + 
(np.sin(np.minimum(((((( data["Medical_Keyword_12"] + data["Medical_Keyword_15"])/2.0)+(( data["SumEmploymentInfo"] <= data["Medical_History_13"] ).astype(float)))/2.0)) ,(( data["Medical_Keyword_15"] *(-(( data["Medical_Keyword_15"] *(-(((data["Medical_Keyword_12"] >= data["Medical_History_13"] ).astype(float)))))))))))/ 2.0)+
np.minimum(( np.tanh(( -(((2.212120 <(data["Ins_Age"] *(( data["Medical_History_33"] +(( data["Medical_History_14"] < data["BMI"] ).astype(float)))/2.0)) ).astype(float)))))) ,(np.tanh(( -(((2.212120 < data["Ins_Age"] ).astype(float)))))))+
np.maximum(( np.ceil(np.tanh(np.minimum(( data["InsuredInfo_5"]),(data["Medical_History_32"]))))),(np.maximum(( np.minimum(( data["Medical_Keyword_19"]),(((np.cos(data["Medical_Keyword_39"])<= data["BMI"] ).astype(float))))),(( data["Medical_History_17"] -(( data["Product_Info_2_C"] <= np.cos(data["Medical_Keyword_20"])).astype(float)))))))+
np.sin(np.maximum(( data["Medical_Keyword_25"]),(np.sin(np.maximum(( data["Medical_Keyword_29"]),(np.maximum(( data["Medical_Keyword_25"]),(( data["Medical_History_13"] *(( data["SumNAs"] >(2.409090 -(((data["Employment_Info_2"] >(2.409090 - data["Product_Info_7"])).astype(float)) * 2.0)) ).astype(float)))))))))))+ 
(0.094340 *(-(((((data["Medical_Keyword_37"] >=(-(((((data["Employment_Info_2"] >=(( 0.058823 >(data["Medical_History_19"] + np.maximum(( data["Employment_Info_2"]),(data["Medical_Keyword_48"])))).astype(float)) ).astype(float)) > data["Employment_Info_2"] ).astype(float)))) ).astype(float)) > np.abs(data["Employment_Info_5"])).astype(float)))))+
np.minimum(( np.abs(( data["Ins_Age"] * data["Medical_Keyword_17"]))),(np.minimum(((0.138462 *(( 0.693147 >=(((((( data["Medical_History_2"] < data["Medical_Keyword_11"] ).astype(float)) < data["Medical_History_2"] ).astype(float)) * 2.0)- data["Medical_History_2"])).astype(float)))) ,(np.cos(data["Medical_History_32"])))))+ 
(((data["Product_Info_2_N"] < data["Medical_Keyword_31"] ).astype(float)) * np.floor(np.floor(np.cos(((data["Product_Info_3"] +(((data["Medical_History_20"] + np.floor(data["Insurance_History_3"])) /2.0)*(( data["Medical_Keyword_31"] >(( data["Medical_History_27"] + data["Medical_Keyword_31"])/2.0)).astype(float)))) /2.0)))))+
np.minimum(( np.maximum(( data["Insurance_History_5"]),(((data["Insurance_History_5"] + data["Insurance_History_9"])/2.0)))) ,(( -3.0 *(( 0.367879 <=(data["Insurance_History_5"] -(( np.maximum(( data["Insurance_History_7"]),(np.floor(data["Insurance_History_9"])))<(( data["Insurance_History_4"] < 0.0 ).astype(float)) ).astype(float)))).astype(float)))))+ 
(((data["Product_Info_7"] >=(data["Medical_History_38"] * data["SumEmploymentInfo"])).astype(float)) *(((data["Medical_History_4"] >= data["Medical_History_5"] ).astype(float)) *(( data["Medical_History_13"] + data["Medical_Keyword_40"])/2.0)))+ 
(((data["BMI"] *(((data["InsuredInfo_7"] +(( -(np.maximum(( data["Medical_History_23"]),(( data["Medical_History_40"] * data["Medical_History_30"])))))*(( data["Product_Info_4"] + np.minimum(( data["Medical_History_20"]),(data["Medical_History_27"])))/2.0)))> data["Medical_History_31"] ).astype(float)))/ 2.0)/ 2.0)+ 
(-(( 0.138462 *(((np.cos(data["Medical_History_28"])* np.sin(((( data["Product_Info_2_N"] <=(( data["Ht"] == np.abs(0.636620)).astype(float)) ).astype(float)) * data["Ins_Age"])))< np.minimum(( data["Medical_History_12"]),(data["Product_Info_2_N"])) ).astype(float)))))+ 
(((((np.minimum(( data["Medical_History_28"]),(((data["Product_Info_7"] + data["Medical_Keyword_5"])/2.0)))> np.cos(np.minimum(( data["Medical_Keyword_3"]),(data["Medical_Keyword_40"])))).astype(float)) >(( np.cos(data["Medical_Keyword_39"])+ np.maximum(( data["Medical_Keyword_10"]),(np.cos(data["Medical_History_25"])))) /2.0)).astype(float)) / 2.0)+ 
(np.sin(( data["Medical_History_5"] *(( data["Medical_History_19"] >(data["Product_Info_1"] * np.minimum(( data["Medical_Keyword_47"]),(((data["Medical_History_19"] - 0.094340)- np.sinh(data["Product_Info_2_C"])))))).astype(float)))) * np.maximum(( data["Medical_History_3"]),(data["Medical_History_29"])))+ 
(0.094340 *(np.minimum(((( data["Medical_History_41"] >= data["Medical_History_3"] ).astype(float))),(np.ceil(((((data["Medical_History_4"] * 2.0)*(( data["Medical_Keyword_3"] >(( data["Medical_Keyword_23"] >= data["Medical_Keyword_48"] ).astype(float)) ).astype(float)))+ data["Medical_Keyword_23"])/2.0)))) * data["Medical_Keyword_3"])) + 
(((( data["SumEmploymentInfo"] > np.minimum(( data["Medical_Keyword_12"]),(data["Family_Hist_5"])) ).astype(float)) <(( np.minimum(( np.minimum(((-(data["Medical_History_7"]))),(data["Family_Hist_5"]))),(data["Medical_History_21"])) >=(1.0/(1.0 + np.exp(-(data["Medical_Keyword_8"] -(data["Medical_Keyword_12"] - data["Medical_Keyword_8"])))))).astype(float)) ).astype(float)) + 
(np.minimum(((( data["InsuredInfo_7"] >= data["Medical_History_32"] ).astype(float))),(((data["Medical_History_32"] >= data["Medical_History_20"] ).astype(float)))) +(((data["Medical_History_9"] >= np.cos(((np.ceil(data["Medical_History_34"])==(( data["Medical_History_32"] >= 20.750000 ).astype(float)) ).astype(float)))).astype(float)) * 2.0)) + 
(( -(((((data["Ht"] == data["Medical_Keyword_44"] ).astype(float)) > np.cos(((data["SumMedicalHistory"] - data["Medical_Keyword_44"])/ 2.0)) ).astype(float)))) * np.sinh(np.abs(((data["Product_Info_6"] <= data["BMI"] ).astype(float)))))+
np.sin(((( data["Medical_Keyword_12"] + data["Medical_Keyword_20"])/2.0)*(-(((data["Medical_Keyword_26"] >(data["Medical_Keyword_10"] *(data["Medical_Keyword_39"] *(( np.sin(data["Medical_Keyword_12"])<= data["Employment_Info_6"] ).astype(float)))) ).astype(float)))))) + 
(data["Medical_Keyword_38"] *(np.minimum(( data["Medical_History_30"]),(np.tanh(np.minimum(( data["Product_Info_3"]),(((np.sinh(( data["Medical_Keyword_45"] -(data["Medical_Keyword_3"] * data["Medical_Keyword_13"])))/ 2.0)/ 2.0)))))) / 2.0)) + 
(( data["SumNAs"] >=(5.200000 - np.maximum(((data["Medical_Keyword_39"] * data["Medical_Keyword_47"])) ,(np.maximum(((np.minimum(( data["Medical_History_15"]),(np.minimum(( 5.200000),(((data["SumFamilyHistory"] >= data["Medical_Keyword_3"] ).astype(float)))))) * data["Medical_Keyword_2"])) ,(np.abs(data["Ht"])))))) ).astype(float)) + 
(-(np.minimum(( np.minimum(((( data["Medical_Keyword_47"] >(data["Medical_Keyword_24"] * np.minimum(( data["Medical_History_28"]),(((( data["Product_Info_1"] > data["Medical_History_13"] ).astype(float)) * 2.0)))) ).astype(float))),(( 1.0/(1.0 + np.exp(- data["Medical_History_3"])))))) ,(((data["Insurance_History_8"] >= data["Medical_History_11"] ).astype(float)))))) +
np.floor(np.tanh(( data["Medical_Keyword_18"] *(data["Medical_Keyword_17"] *(((data["Medical_History_19"] >=(-(data["Medical_Keyword_45"])) ).astype(float)) -(data["InsuredInfo_7"] * np.tanh(((( data["Medical_History_19"] >(-(data["Medical_Keyword_4"])) ).astype(float)) / 2.0)))))))) + 
(-(((((data["Employment_Info_4"] <= data["Medical_Keyword_23"] ).astype(float)) > np.floor(((((np.sin(data["Medical_History_11"])<= data["Medical_Keyword_23"] ).astype(float)) >(( data["Medical_Keyword_46"] +(( data["Medical_History_11"] < data["Medical_Keyword_16"] ).astype(float)))/2.0)).astype(float)))).astype(float)))) +
np.maximum(((( np.minimum(( data["Ins_Age"]),(((( np.minimum(( data["Medical_History_30"]),(data["Medical_History_10"])) * data["Medical_History_10"])> np.cos(data["Family_Hist_1"])).astype(float)))) == np.ceil(data["Medical_History_6"])).astype(float))),(np.floor(np.minimum(( data["Employment_Info_3"]),(np.minimum(( data["Medical_Keyword_16"]),(data["Employment_Info_6"])))))))+
np.sinh(np.abs(((((( data["SumMedicalHistory"] *(((data["Employment_Info_1"] / 2.0)<= data["Employment_Info_4"] ).astype(float)))>(data["Medical_History_36"] +(9.869604 / 2.0)) ).astype(float)) > np.cos(np.minimum(( data["Medical_Keyword_36"]),(((data["Medical_History_28"] + data["SumMedicalHistory"])/2.0)))) ).astype(float)))) +
np.sin(( data["SumMedicalKeywords"] * np.minimum(((1.0/(1.0 + np.exp(- data["Medical_History_40"])))) ,(((data["InsuredInfo_5"] >=(data["SumMedicalKeywords"] * np.sinh(np.minimum(((1.0/(1.0 + np.exp(- np.floor(data["InsuredInfo_5"]))))),(((data["Medical_History_19"] >= data["Medical_History_31"] ).astype(float)))))) ).astype(float)))))) + 
(( np.minimum(( data["Medical_History_38"]),(data["Medical_Keyword_39"])) / 2.0)*(-(((data["Medical_Keyword_39"] >(((( np.round(((data["Medical_History_36"] + data["Product_Info_6"])/2.0)) + data["SumInsuredInfo"])/2.0)/ 2.0)/ 2.0)).astype(float)))))+ 
(((((((( data["Medical_History_12"] > data["Medical_History_13"] ).astype(float)) > np.floor(( data["InsuredInfo_1"] / 2.0)) ).astype(float)) >(( data["Medical_History_11"] < np.sin(np.minimum(( data["Medical_Keyword_43"]),(((data["SumEmploymentInfo"] > data["Medical_Keyword_9"] ).astype(float)))))).astype(float)) ).astype(float)) <(( data["Medical_Keyword_42"] > data["Medical_History_40"] ).astype(float)) ).astype(float)) + 
(-(((((((np.minimum(( data["Medical_Keyword_42"]),(data["Medical_Keyword_40"])) >(( data["Employment_Info_1"] +(1.0/(1.0 + np.exp(- data["Employment_Info_4"])))) /2.0)).astype(float)) >(( data["Employment_Info_4"] <=(data["Medical_Keyword_40"] * data["SumInsuredInfo"])).astype(float)) ).astype(float)) > np.ceil(data["Medical_Keyword_3"])).astype(float)))) + 
(np.tanh(((-(np.maximum(( data["Medical_History_4"]),(( data["Medical_Keyword_36"] / 2.0)))))*(( data["SumInsuranceHistory"] >= np.ceil(data["Medical_History_40"])).astype(float)))) *(((( data["SumInsuranceHistory"] >= np.ceil(data["Medical_History_40"])).astype(float)) >= np.abs(data["Medical_History_28"])).astype(float)))+ 
(( data["Medical_History_19"] < np.minimum(((data["Medical_History_19"] * data["Medical_History_15"])) ,(np.minimum(( data["Medical_Keyword_3"]),(( -(( 1.0/(1.0 + np.exp(- np.round(np.minimum(( np.minimum(( data["Medical_History_40"]),(np.minimum(( data["Medical_History_22"]),(( data["Medical_History_35"] * data["Medical_History_19"])))))) ,(data["BMI"])))))))))))) ).astype(float)) + 
(( 5.428570 <(data["Medical_History_19"] +(((data["Employment_Info_2"] < np.ceil(( data["Medical_History_9"] *(( data["Insurance_History_2"] >= np.maximum(( data["Medical_History_5"]),(data["Medical_Keyword_33"])) ).astype(float)))) ).astype(float)) *(( data["Insurance_History_2"] >=(data["Medical_History_7"] - data["Medical_History_5"])).astype(float)))) ).astype(float)) +
np.floor(( 1.0/(1.0 + np.exp(-(data["Product_Info_5"] +(data["Medical_History_7"] +(((data["Medical_Keyword_14"] + data["Medical_Keyword_32"])+ data["InsuredInfo_7"])* np.maximum(( data["Medical_Keyword_46"]),(( data["Medical_Keyword_39"] * data["SumInsuredInfo"])))))))))) + 
(( 1.0/(1.0 + np.exp(- data["Product_Info_6"])))*(np.floor(np.sin(np.floor(((np.sin(np.maximum(((data["Medical_History_13"] -(-(data["BMI"])))) ,(np.maximum(( data["Employment_Info_2"]),(np.maximum(( data["Employment_Info_1"]),(data["Medical_History_13"])))))))/ 2.0)/ 2.0)))) / 2.0)) + 
(data["Family_Hist_5"] *(((data["Medical_History_28"] * data["Medical_Keyword_13"])>=(1.0/(1.0 + np.exp(-(np.minimum(((data["Ins_Age"] * data["Medical_History_5"])) ,(data["InsuredInfo_3"])) +(data["Ins_Age"] * data["Medical_History_30"])))))).astype(float)))+
np.round(( np.sin(np.ceil(np.ceil(((((data["Wt"] - data["SumNAs"])<(((data["Medical_History_40"] < data["Wt"] ).astype(float)) * data["Employment_Info_1"])).astype(float)) * np.minimum(( data["Medical_Keyword_12"]),(data["Medical_Keyword_25"])))))) * 2.0)) + 
(np.minimum(( data["Medical_Keyword_44"]),(((np.minimum(( data["Medical_Keyword_44"]),(((data["Employment_Info_4"] > data["Wt"] ).astype(float)))) > data["Wt"] ).astype(float)))) *(( data["Medical_History_12"] >=(-(((np.minimum(((data["Medical_Keyword_44"] / 2.0)) ,(data["SumInsuredInfo"])) + data["Medical_Keyword_47"])/2.0)))).astype(float)))+ 
(0.138462 *(((data["Ins_Age"] * np.minimum(( np.maximum(( data["Medical_Keyword_27"]),(data["Medical_Keyword_22"]))),(np.cos(data["Medical_History_24"])))) +(np.minimum(( np.minimum(( data["Medical_History_20"]),(data["Medical_History_13"]))),(data["Medical_History_23"])) *(data["InsuredInfo_1"] / 2.0)))/2.0)))
 return p+4.5
def Individual3(data):
p =(((((( -(data["BMI"])) +(( data["Medical_History_4"] <(( data["BMI"] < data["Medical_History_23"] ).astype(float)) ).astype(float)))+(data["Medical_History_4"] - data["SumMedicalKeywords"])) /2.0)+ np.cos(((data["Medical_Keyword_3"] +(data["Medical_History_40"] - data["BMI"])) /2.0)))+ 
(((( data["InsuredInfo_6"] + data["Medical_History_15"])/2.0)+(( np.minimum(((np.ceil(np.minimum(( data["Product_Info_4"]),(( np.ceil(data["Product_Info_4"])* 2.0)))) * 2.0)) ,(( -(((data["Insurance_History_2"] +(data["Medical_History_30"] + data["InsuredInfo_7"])) /2.0)))))+ data["Product_Info_4"])/2.0)) /2.0)+
np.tanh(( np.minimum(((np.cos(data["Ins_Age"])- data["InsuredInfo_5"])) ,(np.minimum(( data["Medical_History_20"]),(data["Medical_History_27"])))) -(data["Medical_History_5"] *(data["Medical_Keyword_22"] +(( data["Family_Hist_4"] + data["SumMedicalKeywords"])+ data["Medical_History_5"])))))+
np.tanh(((( np.maximum(( data["Medical_Keyword_41"]),(data["SumMedicalKeywords"])) +(( data["Insurance_History_5"] +(-(np.maximum(( data["Medical_History_18"]),(np.maximum(( np.maximum(( data["Medical_Keyword_3"]),(data["Medical_History_30"]))),(np.maximum(( data["Medical_History_28"]),(( data["Medical_Keyword_38"] - data["Medical_History_11"])))))))))) /2.0)) /2.0)/ 2.0)) +
np.tanh(((data["Medical_History_31"] +(((((data["BMI"] * data["Ins_Age"])+ np.minimum(( data["Medical_History_13"]),(data["Product_Info_2_N"])))/2.0)+ np.minimum(( np.cos(data["Product_Info_4"])) ,(( np.cos(data["Product_Info_4"])+(data["Medical_History_1"] + data["Family_Hist_2"])))))/2.0)) /2.0)) + 
(((( data["Medical_History_24"] >= data["Medical_Keyword_18"] ).astype(float)) + np.minimum(( np.minimum(((((data["Medical_History_7"] + data["Employment_Info_2"])/2.0)/ 2.0)) ,(( data["Medical_History_35"] * data["Medical_History_24"])))) ,(np.minimum(( np.cos(np.round(data["Wt"]))),(( data["Medical_History_5"] * data["InsuredInfo_2"])))))) /2.0)+
np.minimum(( np.sin(( data["Medical_History_11"] * 2.0))),(np.maximum(( data["Medical_History_4"]),(( np.maximum(( data["Medical_History_24"]),(data["Medical_History_15"])) +(np.maximum(( data["Medical_History_24"]),(data["Medical_History_15"])) +(( data["Wt"] +(data["Medical_History_15"] - 0.058823)) /2.0)))))))+ 
(((((data["Medical_History_23"] +(((-(data["InsuredInfo_1"])) + data["Medical_History_3"])/2.0)) /2.0)+(((( data["SumMedicalKeywords"] + data["Medical_Keyword_25"])/2.0)+(( data["Medical_Keyword_33"] +(((data["Medical_History_17"] - data["InsuredInfo_1"])+ data["Medical_History_17"])/2.0)) /2.0)) /2.0)) /2.0)/ 2.0)+
np.sin(np.minimum(( data["Medical_History_33"]),(((( data["Medical_Keyword_28"] +(((( data["SumNAs"] +(np.cos(data["BMI"])- data["Medical_Keyword_9"])) /2.0)+(((( data["Medical_History_39"] +(data["Medical_Keyword_19"] + data["Medical_Keyword_32"])) /2.0)+ data["Medical_Keyword_11"])/2.0)) /2.0)) /2.0)/ 2.0)))) +
np.minimum(( np.ceil(np.minimum(((( data["Employment_Info_1"] / 2.0)/ 2.0)) ,(((2.0 * np.floor(np.cos(( data["Insurance_History_7"] + data["Insurance_History_5"])))) * 2.0))))),(( data["BMI"] *(( data["Medical_History_5"] >= np.cos(data["Medical_History_14"])).astype(float)))))+
np.minimum(( np.minimum(( 0.138462),(((data["Family_Hist_2"] <= np.round(( data["Medical_History_15"] + data["Product_Info_2_N"])) ).astype(float))))),(( data["Medical_History_32"] +(((( data["Insurance_History_1"] < np.round(np.sin(np.ceil(data["Product_Info_2_N"])))).astype(float)) + data["Medical_Keyword_19"])/2.0)))) +
np.minimum(( 0.301030),(np.maximum(( data["Medical_History_10"]),(((((((data["Product_Info_2_C"] >= np.round(( data["Product_Info_2_C"] - data["Medical_Keyword_45"])) ).astype(float)) >= np.abs(((data["Medical_Keyword_7"] <(np.cos(data["Medical_Keyword_2"])- data["Medical_Keyword_26"])).astype(float)))).astype(float)) >= data["Medical_History_40"] ).astype(float)))))) +
np.sin(np.minimum(((( data["Medical_History_13"] == data["Medical_History_18"] ).astype(float))),(((((data["Medical_History_27"] >(data["Medical_History_15"] * data["Medical_History_18"])).astype(float)) * 2.0)* data["Medical_History_15"])))) + 
(((( data["Medical_History_15"] < np.minimum(( np.sin(np.floor(np.minimum(((( data["Medical_History_15"] - data["Medical_History_18"])- data["Medical_History_18"])) ,(( data["Medical_History_15"] - data["Medical_History_18"])))))) ,(data["Medical_Keyword_41"])) ).astype(float)) +(( data["Medical_Keyword_34"] >= np.abs(data["Medical_History_22"])).astype(float)))/2.0)+ 
(np.minimum(( np.minimum(( np.maximum(( data["Medical_History_4"]),(( data["Medical_History_15"] / 2.0)))) ,(data["Medical_Keyword_43"]))),(np.sin(data["InsuredInfo_2"])))* np.sin(np.minimum(( np.minimum(( data["Medical_History_4"]),(( data["SumNAs"] + data["Medical_History_4"])))) ,(((data["Medical_Keyword_37"] > data["Medical_History_1"] ).astype(float)))))) + 
(( 0.058823 *(((data["Medical_History_6"] + np.minimum(((-(np.ceil(np.maximum(( data["Medical_History_19"]),(data["InsuredInfo_2"])))))) ,(data["Medical_History_22"])))+ data["Employment_Info_5"])/2.0)) + np.sin(np.cos(np.maximum(( 1.584910),(data["Ins_Age"])))))+ 
(((( 0.058823 +(((( data["Medical_History_18"] >= data["Medical_History_28"] ).astype(float)) <= data["Medical_History_13"] ).astype(float)))/2.0)+(-(((((data["SumEmploymentInfo"] >= data["Medical_History_18"] ).astype(float)) < np.round(((0.058823 <(( data["Medical_Keyword_5"] < data["Medical_History_28"] ).astype(float)) ).astype(float)))).astype(float)))))/2.0)+ 
(((((( data["Medical_Keyword_23"] * data["Medical_Keyword_46"])<= np.minimum(((data["Medical_Keyword_46"] - data["Medical_History_15"])) ,(((((data["Medical_History_20"] <=(data["Medical_History_5"] + data["Medical_Keyword_27"])).astype(float)) > data["Medical_History_20"] ).astype(float)))) ).astype(float)) * data["Ins_Age"])/ 2.0)/ 2.0)+ 
(((20.750000 <= data["Medical_History_32"] ).astype(float)) +(((data["InsuredInfo_6"] *(((( data["Insurance_History_7"] + data["InsuredInfo_6"])/2.0)< data["Medical_History_23"] ).astype(float)))* np.minimum(( data["Medical_Keyword_3"]),(np.cos(( 1.0/(1.0 + np.exp(- data["InsuredInfo_6"])))))))/ 2.0)) +
np.minimum(( np.minimum(((((( np.minimum(( data["Medical_Keyword_3"]),(data["Family_Hist_3"])) >=(data["Medical_Keyword_39"] * data["Medical_Keyword_6"])).astype(float)) / 2.0)/ 2.0)) ,(np.maximum(( data["SumMedicalKeywords"]),(data["Medical_History_39"]))))),(np.maximum(( data["SumMedicalKeywords"]),(((data["Medical_History_23"] + data["Medical_History_20"])/2.0)))))+
np.minimum(((data["Medical_History_15"] *(( np.ceil(data["Medical_History_15"])< np.sin(((data["Medical_History_15"] > data["Employment_Info_3"] ).astype(float)))).astype(float)))) ,(( data["Product_Info_2_N"] *(((np.minimum(( data["Ins_Age"]),(data["Medical_History_18"])) >=(1.0/(1.0 + np.exp(- data["Medical_Keyword_43"])))).astype(float)) / 2.0)))) + 
(0.094340 *(((data["Family_Hist_3"] +(( data["Medical_Keyword_15"] -(data["Medical_History_4"] + data["Ht"])) -(( data["SumEmploymentInfo"] >=(1.0/(1.0 + np.exp(-(data["Medical_History_15"] - data["Medical_History_36"])))) ).astype(float)))) /2.0)- np.minimum(( data["Medical_History_15"]),(data["Ins_Age"])))) + 
(-(np.sin(( np.abs(data["Medical_History_5"])*(( np.minimum(((data["Medical_History_39"] - data["BMI"])) ,(np.minimum(( data["Medical_History_37"]),(np.ceil(np.ceil(data["Product_Info_4"])))))) <=(( data["Medical_History_30"] * data["Medical_History_39"])- data["Medical_History_13"])).astype(float)))))) +
np.abs(( np.cos(data["Wt"])* np.tanh(( np.minimum(( data["SumInsuredInfo"]),(data["Medical_History_4"])) *(-2.0 *(data["Medical_History_4"] *(data["Medical_History_10"] *(( data["Medical_History_15"] <= np.cos(-2.0)).astype(float)))))))))+ 
(np.minimum(( np.maximum(( data["InsuredInfo_7"]),(( -(np.ceil(data["Product_Info_4"])))))) ,(((data["BMI"] +(( data["Medical_History_12"] <=(np.minimum(((data["Product_Info_4"] / 2.0)) ,(((data["Medical_History_17"] + data["InsuredInfo_7"])/2.0)))/ 2.0)).astype(float)))/2.0)))/ 2.0)+
np.sin(np.sinh(np.minimum(((1.0/(1.0 + np.exp(- data["BMI"])))) ,(( data["BMI"] * np.sin(np.round(np.round(((data["Medical_History_39"] -(data["BMI"] * np.sin(np.round(data["Medical_History_11"])))) +(data["Insurance_History_2"] / 2.0)))))))))) +
np.sin(( data["Medical_History_5"] *(((( data["Medical_History_24"] +(np.maximum(( data["Medical_Keyword_48"]),(data["Medical_Keyword_23"])) * data["Medical_History_20"])) /2.0)+(( data["Medical_Keyword_7"] >(( data["Medical_History_2"] +(1.0/(1.0 + np.exp(- data["Medical_Keyword_8"])))) /2.0)).astype(float)))/2.0)))+ 
(((data["Medical_History_20"] <(( np.ceil(data["Medical_Keyword_24"])<(((((-(data["InsuredInfo_5"])) < data["Medical_History_18"] ).astype(float)) >= data["Medical_History_17"] ).astype(float)) ).astype(float)) ).astype(float)) *(((((data["Medical_History_21"] >= data["Medical_Keyword_21"] ).astype(float)) >= data["Medical_History_40"] ).astype(float)) / 2.0)) +
np.minimum(((((data["Medical_History_15"] + data["InsuredInfo_1"])/2.0)*(np.ceil(data["Medical_History_23"])* 0.058823))),(((data["Medical_History_1"] +(( data["InsuredInfo_2"] <=(data["Medical_History_37"] * 2.0)).astype(float)))/2.0)))+
np.sin(( np.ceil(np.ceil(np.minimum(( np.round(((data["Medical_Keyword_12"] + np.minimum(( data["Product_Info_4"]),(data["Medical_Keyword_15"])))/2.0))),(data["Medical_History_40"])))) *(( data["Medical_Keyword_15"] <= np.minimum(( data["Medical_History_4"]),(( data["InsuredInfo_4"] - data["BMI"])))).astype(float)))) +
np.minimum(((0.058823 *(np.cos(data["Employment_Info_4"])- np.minimum(( data["Medical_History_3"]),(np.sinh(data["Ins_Age"])))))) ,(((((data["Medical_Keyword_11"] >= data["Product_Info_2_C"] ).astype(float)) +(-(((data["Medical_History_38"] >= 0.0 ).astype(float)))))/2.0)))+
np.sin(( data["Medical_History_11"] *(( data["Medical_History_37"] >=(((data["Medical_Keyword_25"] <=(data["Medical_History_8"] * data["Medical_Keyword_33"])).astype(float)) -(((data["Medical_Keyword_16"] + data["Medical_Keyword_5"])+(( data["Medical_Keyword_9"] + data["Medical_Keyword_29"])- data["Medical_History_18"])) /2.0)) ).astype(float)))) + 
(np.sin(np.sin(np.minimum(( data["SumMedicalKeywords"]),(data["InsuredInfo_5"])))) *(( data["SumInsuranceHistory"] >= np.ceil(((np.minimum(( data["Medical_History_17"]),(((((data["Medical_History_17"] >= np.abs(data["InsuredInfo_5"])).astype(float)) >= np.abs(data["SumMedicalKeywords"])).astype(float)))) + data["Medical_Keyword_19"])/2.0)) ).astype(float)))+
np.minimum(((( np.tanh(np.cos(((data["Ht"] + np.minimum(( data["Medical_Keyword_39"]),(data["Medical_Keyword_47"])))/2.0)))< np.minimum(( data["Medical_Keyword_14"]),(((data["Medical_Keyword_39"] + data["Medical_Keyword_34"])/2.0)))).astype(float))),(np.cos(np.minimum(( data["Product_Info_7"]),(((data["SumNAs"] + data["Medical_Keyword_42"])/2.0)))))) + 
(0.058823 * np.round(((( data["Medical_History_1"] >=(data["Medical_Keyword_47"] * 2.0)).astype(float)) - np.abs(np.sinh(((data["Product_Info_2_N"] + np.sinh(np.minimum(( data["Product_Info_2_N"]),(((data["Medical_Keyword_11"] + data["Medical_Keyword_47"])/2.0)))))/2.0)))))) +
np.minimum(( 0.138462),(np.maximum(((( data["Medical_Keyword_21"] + data["Medical_Keyword_16"])/2.0)) ,(np.maximum(( np.maximum(((( data["Medical_Keyword_20"] + data["Medical_Keyword_2"])/2.0)) ,(((((data["Medical_Keyword_29"] + data["Product_Info_7"])/2.0)+ data["Medical_Keyword_8"])/2.0)))) ,(((0.058823 >= np.cos(data["Family_Hist_5"])).astype(float)))))))) +
np.tanh(np.tanh(( data["Medical_Keyword_3"] *(((( data["Product_Info_5"] >= data["Medical_History_7"] ).astype(float)) >=(((data["Medical_History_22"] +(( np.ceil(data["Product_Info_4"])- data["Product_Info_5"])- data["Medical_History_35"])) /2.0)- data["Medical_Keyword_38"])).astype(float)))))+
np.minimum(((np.maximum(( np.ceil(data["Medical_History_6"])) ,(( data["Ht"] / 2.0)))/ 2.0)) ,(((2.718282 < np.maximum(((data["SumNAs"] -(( data["Medical_History_28"] > data["SumEmploymentInfo"] ).astype(float)))) ,(((data["Medical_History_19"] +(data["Medical_History_28"] * data["Medical_History_7"])) /2.0)))).astype(float)))) +
np.tanh(( np.minimum(( data["Medical_Keyword_24"]),(data["SumMedicalKeywords"])) *(( data["Medical_History_10"] >=(np.cos(data["InsuredInfo_1"])*(np.minimum(( data["Medical_History_27"]),(((data["Medical_History_18"] >= data["Medical_Keyword_24"] ).astype(float)))) * data["Medical_History_18"])) ).astype(float)))) + 
(( np.minimum(( np.ceil(np.minimum(( data["Product_Info_7"]),(data["Medical_History_6"])))) ,(data["Medical_Keyword_21"])) > np.cos(((np.minimum(( data["Medical_History_10"]),(( data["Medical_History_30"] - np.minimum(( data["Medical_History_22"]),(data["Medical_History_40"])))))* 2.0)- np.minimum(( data["Product_Info_7"]),(data["Medical_History_40"])))) ).astype(float)) +
np.ceil(np.minimum(( data["Medical_Keyword_13"]),(((( data["Medical_Keyword_38"] +(data["Medical_Keyword_11"] *(-(((data["Medical_Keyword_43"] >=(data["Medical_History_7"] *(data["Medical_History_22"] +(data["Medical_History_3"] * np.floor(data["Medical_History_28"])))) ).astype(float)))))) /2.0)/ 2.0)))) + 
(np.sin(np.sin(np.floor(np.sin(((data["Medical_History_20"] +(( np.abs(np.maximum(( np.abs(data["Medical_History_27"])) ,(data["Medical_Keyword_31"])))+ np.minimum(( data["Medical_History_24"]),(((( data["Medical_Keyword_4"] * data["Medical_History_22"])+ data["Medical_Keyword_31"])/2.0)))) /2.0)) /2.0)))))* 2.0)+ 
(( np.maximum(( data["Medical_History_28"]),(np.maximum(( data["Medical_History_27"]),(((np.cos(np.maximum(( np.maximum(( data["Medical_Keyword_23"]),(data["Wt"]))),(data["Medical_Keyword_17"])))>(np.maximum(( data["Medical_History_19"]),(data["Medical_Keyword_24"])) / 2.0)).astype(float)))))) <(data["Medical_Keyword_32"] * data["Medical_Keyword_46"])).astype(float)) + 
(((np.minimum(((( data["SumMedicalKeywords"] > data["Medical_History_22"] ).astype(float))),(((np.minimum(((( data["Medical_Keyword_3"] > data["Medical_History_22"] ).astype(float))),(((data["Medical_Keyword_40"] > data["Medical_History_31"] ).astype(float)))) > data["Medical_History_31"] ).astype(float)))) >= np.maximum(( data["Medical_History_20"]),(((data["Insurance_History_9"] < data["InsuredInfo_1"] ).astype(float)))) ).astype(float)) / 2.0)+ 
(((np.minimum(( np.sin(np.maximum(( data["SumMedicalKeywords"]),(np.ceil(np.maximum(((-(data["Medical_History_12"]))),(np.maximum(((data["Employment_Info_2"] * 5.200000)) ,(data["Medical_Keyword_3"]))))))))),(((data["Medical_History_37"] > np.abs(data["SumMedicalKeywords"])).astype(float)))) / 2.0)/ 2.0)/ 2.0)+
np.floor(np.sin(np.round(( data["Product_Info_1"] *(((((( data["Medical_History_12"] >(data["Medical_Keyword_41"] * data["Insurance_History_3"])).astype(float)) > np.cos(np.abs(data["Employment_Info_1"])) ).astype(float)) !=(data["Medical_History_8"] *(( data["Medical_Keyword_14"] >= data["Medical_History_7"] ).astype(float)))).astype(float)))))) +
np.minimum(( np.cos(data["SumEmploymentInfo"])) ,(np.minimum(( np.sin(np.round(data["Medical_History_35"]))),(np.cos(np.minimum(( data["Medical_History_10"]),(( data["Medical_Keyword_42"] + np.maximum(( data["Medical_History_3"]),(np.maximum(( np.floor(data["SumMedicalHistory"])) ,(data["Medical_History_35"])))))))))))) +
np.sin(np.maximum(((0.058823 *(( np.ceil(data["Product_Info_2_C"])< data["Ins_Age"] ).astype(float)))) ,(((np.minimum(( data["InsuredInfo_5"]),(( 0.058823 +(((( data["Medical_History_4"] > data["InsuredInfo_6"] ).astype(float)) > np.abs(data["Ins_Age"])).astype(float)))))* 2.0)* 2.0)))) + 
(( data["InsuredInfo_5"] *(((data["InsuredInfo_5"] * data["InsuredInfo_1"])>=(( data["SumInsuredInfo"] >= data["Medical_Keyword_24"] ).astype(float)) ).astype(float)))*(((( data["Medical_History_40"] <= np.cos(( data["SumInsuredInfo"] * 2.0)) ).astype(float)) / 2.0)/ 2.0)) + 
(np.minimum(( np.maximum(((( np.round(data["Ins_Age"])+ 0.693147)/2.0)) ,(( data["Medical_History_33"] - data["InsuredInfo_7"])))) ,(((( data["Medical_Keyword_46"] >= np.maximum(( data["Ins_Age"]),(data["Medical_History_39"])) ).astype(float)) * data["InsuredInfo_6"])))/ 2.0)+
np.minimum(((-(((data["Product_Info_3"] <= np.minimum(( -3.0),(np.minimum(((data["Ht"] +(-3.0 * data["InsuredInfo_3"]))),(( data["Medical_History_29"] + -3.0)))))).astype(float))))),(np.maximum(( data["Medical_History_11"]),(np.tanh(data["Product_Info_3"])))))+
np.sin(((1.197370 <= np.minimum(( np.abs(data["Employment_Info_2"])) ,(((((data["InsuredInfo_1"] + np.minimum(( data["Medical_Keyword_38"]),(data["Medical_History_28"])))+(( data["Medical_Keyword_30"] > data["Medical_Keyword_7"] ).astype(float)))+ np.minimum(( data["Medical_Keyword_1"]),(data["Medical_Keyword_16"])))/ 2.0)))).astype(float)))+ 
(( np.minimum(( data["Medical_Keyword_47"]),(np.maximum(( data["Medical_History_5"]),(np.maximum(( data["Medical_Keyword_2"]),(np.maximum(( data["Medical_Keyword_43"]),(np.maximum(( data["Medical_Keyword_26"]),(( data["Medical_Keyword_36"] + data["Product_Info_5"])))))))))))>=(np.abs(data["Medical_Keyword_14"])- np.minimum(( data["Medical_History_28"]),(data["Medical_Keyword_36"])))).astype(float)) + 
(0.094340 * np.floor(np.cos(((( data["Medical_History_18"] +(data["Medical_History_13"] +(data["Medical_Keyword_40"] *(( np.tanh(data["Medical_History_10"])!=(( data["Product_Info_2_C"] == data["Product_Info_2_C"] ).astype(float)) ).astype(float)))))/2.0)* 2.0)))) +
np.minimum(( 0.094340),(((((( data["Product_Info_2_C"] >(((((data["Medical_Keyword_22"] + data["Medical_History_37"])/2.0)+(( data["Medical_Keyword_21"] + data["Product_Info_6"])/2.0)) > data["Medical_Keyword_10"] ).astype(float)) ).astype(float)) + np.maximum(( data["Medical_History_10"]),(data["Medical_Keyword_11"])))/2.0)/ 2.0)))+ 
(((( data["Employment_Info_2"] +(-(((2.409090 < data["Ins_Age"] ).astype(float)))))<= data["Product_Info_4"] ).astype(float)) * np.sinh(np.ceil(np.minimum(((-(((2.409090 < data["Ins_Age"] ).astype(float))))),(( data["Wt"] / 2.0)))))) +
np.ceil(((( np.tanh(data["Medical_Keyword_42"])>=(( data["Medical_Keyword_20"] < data["Medical_History_11"] ).astype(float)) ).astype(float)) -(( np.minimum(( data["Medical_History_23"]),(data["Wt"])) >(((( data["Insurance_History_9"] <= data["Medical_History_10"] ).astype(float)) >= np.minimum(( data["Medical_Keyword_38"]),(data["Ht"])) ).astype(float)) ).astype(float)))) + 
(data["Insurance_History_1"] *(-(( data["Medical_Keyword_39"] *(np.round(np.sin(( data["Medical_History_38"] - np.minimum(((( data["Medical_History_9"] >(( data["Medical_Keyword_39"] > data["SumNAs"] ).astype(float)) ).astype(float))),(data["SumMedicalKeywords"])))))/ 2.0)))))+ 
(((( data["Insurance_History_1"] >= data["Medical_History_5"] ).astype(float)) <(((( data["Medical_Keyword_43"] >= data["Medical_History_7"] ).astype(float)) >(((data["Medical_Keyword_19"] +(( data["Medical_Keyword_43"] >= data["Wt"] ).astype(float)))<=(( data["Product_Info_6"] >= data["Insurance_History_1"] ).astype(float)) ).astype(float)) ).astype(float)) ).astype(float)) +
np.minimum(( np.maximum(((data["Medical_History_27"] -(data["Medical_Keyword_30"] *(data["Medical_History_7"] - data["Medical_History_19"])))) ,(data["Medical_Keyword_25"]))),(( data["Medical_Keyword_25"] *(data["Medical_Keyword_35"] *(( data["Medical_Keyword_12"] >= data["Medical_Keyword_18"] ).astype(float)))))) + 
(((data["Wt"] / 2.0)*(((data["Medical_Keyword_23"] *(( data["Medical_History_31"] <=(((( data["Medical_History_30"] + data["Medical_Keyword_31"])/2.0)+ data["Medical_Keyword_46"])/2.0)).astype(float)))<=(( data["Medical_History_30"] + data["Medical_Keyword_14"])/2.0)).astype(float)))/ 2.0)+
np.sin(np.maximum(( data["Medical_Keyword_43"]),(((((((data["Employment_Info_1"] < 0.693147 ).astype(float)) <(( data["Medical_Keyword_35"] > data["Medical_History_10"] ).astype(float)) ).astype(float)) == np.maximum(( data["Medical_History_27"]),(((( data["Medical_History_22"] * data["SumMedicalHistory"])<= data["Employment_Info_4"] ).astype(float)))) ).astype(float)))))+ 
(((( np.minimum(( np.floor(((data["Medical_History_14"] >= data["Insurance_History_5"] ).astype(float)))) ,(np.minimum(((( data["Medical_Keyword_16"] + np.ceil(data["Product_Info_7"])) /2.0)) ,(np.floor(( -(data["Insurance_History_5"])))))))==(( data["Medical_History_21"] >= data["Medical_History_24"] ).astype(float)) ).astype(float)) > np.cos(data["Medical_History_5"])).astype(float)) + 
(( 2.409090 <(( data["SumMedicalHistory"] +(np.minimum(( data["Medical_History_10"]),(((data["Medical_Keyword_14"] +(((np.abs(( data["Medical_Keyword_15"] * data["Medical_History_36"])) + data["Medical_Keyword_20"])+ data["Medical_Keyword_4"])/2.0)) /2.0)))* 2.0)) /2.0)).astype(float)))
 return p+4.5
def Individual4(data):
p =(( np.minimum(( data["Medical_History_40"]),(np.power(np.abs(( -2.0 + data["BMI"])) , data["Medical_History_39"])))+ np.power(np.abs(( -2.0 + data["BMI"])) ,(( data["Medical_History_4"] +(data["Product_Info_4"] +(((data["Medical_History_4"] * 2.0)+ data["Medical_History_23"])/2.0)))/2.0)))+ 
(np.sin(data["Medical_History_30"])-(np.maximum(((( np.power(np.abs(data["Product_Info_4"]), 31.006277)<= data["Medical_Keyword_3"] ).astype(float))),(((( np.abs(data["Medical_History_15"])<= np.minimum(( data["SumInsuranceHistory"]),(data["Medical_History_15"])) ).astype(float)) * 2.0)))+(( data["Medical_History_13"] >= data["Medical_History_15"] ).astype(float)))) + 
(np.ceil(((data["Medical_History_39"] < np.floor(data["InsuredInfo_6"])).astype(float)))-(( data["Medical_History_20"] < np.maximum(( np.floor(( data["InsuredInfo_5"] * data["Insurance_History_2"]))),(np.sinh(np.maximum(( np.maximum(( data["InsuredInfo_7"]),(data["Medical_History_5"]))),(( data["Ins_Age"] * 2.0)))))) ).astype(float)))+ 
(np.minimum(( data["Medical_History_18"]),(((( np.tanh(np.minimum(( data["Medical_History_24"]),(( data["Medical_History_6"] - data["Medical_History_15"])))) +(( data["Medical_History_18"] <=(( data["Medical_History_13"] > data["Medical_History_35"] ).astype(float)) ).astype(float)))/2.0)- data["Medical_Keyword_38"])))-(( data["Employment_Info_2"] < data["Medical_History_28"] ).astype(float)))+ 
(((data["Medical_Keyword_41"] + np.sin(( data["BMI"] / 2.0)))/2.0)-(((-(((data["Medical_History_27"] <= data["Medical_History_35"] ).astype(float)))) <(( data["Medical_History_7"] <=(data["Medical_History_5"] +(((data["BMI"] / 2.0)<= np.sin(data["Medical_History_11"])).astype(float)))).astype(float)) ).astype(float)))+ 
(((-(np.maximum(( data["Medical_Keyword_48"]),(np.floor(data["Product_Info_4"])))))+(( np.maximum(( data["Family_Hist_4"]),(np.sin(( data["InsuredInfo_6"] - data["InsuredInfo_1"])))) +(((data["Insurance_History_5"] +(data["InsuredInfo_6"] - data["InsuredInfo_1"])) + data["Family_Hist_2"])/2.0)) /2.0)) /2.0)+
np.sin(np.minimum(( np.cos(data["Medical_History_19"])) ,(((( data["Medical_History_40"] <=(( data["Medical_History_31"] <= data["Medical_History_39"] ).astype(float)) ).astype(float)) -(data["Medical_Keyword_9"] +(((data["Medical_History_31"] <= data["InsuredInfo_2"] ).astype(float)) -(( data["Medical_Keyword_48"] >= data["Medical_History_39"] ).astype(float)))))))) + 
(((( data["Medical_Keyword_33"] +(((( data["Medical_Keyword_25"] - np.sin(np.sin(data["Product_Info_2_N"])))+(( round(2.675680)< data["Medical_History_1"] ).astype(float)))/2.0)- data["Medical_Keyword_23"])) /2.0)+(data["Medical_History_17"] - np.round(2.675680)))/2.0)+ 
(((np.sin(((( data["Medical_Keyword_45"] +(np.sin(np.sinh(data["Product_Info_2_C"])) + data["Medical_Keyword_22"])) /2.0)* 2.0)) +(( data["Employment_Info_1"] >(( data["Medical_History_4"] >(( np.minimum(( data["Medical_History_4"]),(data["Medical_History_15"])) <= data["Medical_Keyword_45"] ).astype(float)) ).astype(float)) ).astype(float)))/2.0)/ 2.0)+
np.minimum(((( np.round(((data["BMI"] > 0.367879 ).astype(float)))- data["BMI"])* data["Product_Info_4"])) ,(np.minimum(((( data["Insurance_History_7"] < data["Product_Info_2_N"] ).astype(float))),(((data["BMI"] + np.floor(( data["Product_Info_4"] - 0.058823)))/2.0)))))+
np.minimum(( np.sin(( data["Medical_History_35"] + np.power(np.abs(-2.0), data["Medical_History_11"])))) ,(( np.sin(((((data["Medical_History_39"] <=(( data["Medical_Keyword_3"] < data["SumMedicalKeywords"] ).astype(float)) ).astype(float)) +(-(((data["Medical_History_39"] < np.sinh(data["Medical_Keyword_3"])).astype(float)))))/2.0)) * 2.0)))+ 
(((( -(((((data["SumInsuranceHistory"] - data["Medical_History_15"])< data["Medical_Keyword_37"] ).astype(float)) / 2.0)))-(( data["Medical_Keyword_37"] + data["Employment_Info_1"])/2.0)) + np.floor(np.power(np.abs(data["Medical_Keyword_41"]), np.power(np.abs(data["BMI"]), data["Medical_History_15"])))) /2.0)+ 
(np.minimum(((0.058823 * np.cos(data["SumEmploymentInfo"]))),(np.minimum(((((data["Medical_History_39"] < data["Product_Info_2_N"] ).astype(float)) / 2.0)) ,(np.minimum(( data["Employment_Info_1"]),(((data["BMI"] * 2.0)- np.sinh(data["Ins_Age"])))))))) * 2.0)+ 
(((((( 0.720430 <= data["Ins_Age"] ).astype(float)) * data["BMI"])+ np.power(np.abs(np.round(( data["Ins_Age"] * np.sinh(data["BMI"])))) , data["Medical_History_11"])) /2.0)-(( data["Medical_History_23"] <=(( data["Medical_Keyword_16"] >= data["SumMedicalKeywords"] ).astype(float)) ).astype(float)))+
np.minimum(( data["Medical_Keyword_38"]),(( data["Medical_Keyword_3"] + np.minimum(((( -(data["Medical_Keyword_18"])) / 2.0)) ,(np.sinh(np.floor(np.minimum(( data["Employment_Info_1"]),(np.floor(np.sinh(np.floor(( data["Insurance_History_5"] * np.round(np.cos(data["Insurance_History_7"])))))))))))))))+ 
(((( data["BMI"] <=(np.cos(( data["Medical_History_4"] *(1.0/(1.0 + np.exp(- data["Medical_History_11"])))))/ 2.0)).astype(float)) +(((data["Medical_Keyword_19"] +(-(((data["Product_Info_4"] < data["Medical_Keyword_24"] ).astype(float)))))+(( data["Product_Info_4"] >= data["Medical_History_4"] ).astype(float)))/2.0)) /2.0)+
np.minimum(( np.cos(((((data["Medical_History_20"] >= data["SumMedicalKeywords"] ).astype(float)) + data["Medical_History_30"])/2.0))),(np.minimum(((( -(np.power(np.abs(data["Ht"]), data["SumEmploymentInfo"])))/ 2.0)) ,(( -(((data["Ins_Age"] >= np.sin(((data["Medical_History_20"] > data["Medical_Keyword_31"] ).astype(float)))).astype(float)))))))) + 
(1.0/(1.0 + np.exp(-(np.maximum(( data["Insurance_History_2"]),(data["Insurance_History_1"])) -(np.power(np.abs(data["Medical_History_11"]), np.power(np.abs(data["Insurance_History_1"]), np.maximum(( np.round(data["BMI"])) ,(np.maximum(( data["Ins_Age"]),(data["SumMedicalKeywords"])))))) - np.minimum(( data["SumMedicalKeywords"]),(data["Family_Hist_3"])))))))+
np.minimum(((((( data["SumMedicalKeywords"] + data["Medical_History_15"])/2.0)+ np.cos(data["InsuredInfo_5"])) /2.0)) ,(( -(np.power(np.abs(data["Wt"]), np.maximum(( data["Medical_History_23"]),(((data["SumMedicalKeywords"] +(8.0 *(( data["Medical_History_15"] < 8.0 ).astype(float)))) /2.0)))))))) +
np.minimum(( np.power(np.abs(data["BMI"]), data["Medical_History_23"])) ,(((((np.power(np.abs(data["Medical_History_15"]), data["Product_Info_3"])>=(data["Medical_Keyword_28"] + np.round(data["BMI"])) ).astype(float)) < np.maximum(( data["Medical_History_5"]),(data["InsuredInfo_7"])) ).astype(float)))) +
np.minimum(((data["Medical_Keyword_38"] +(data["Ins_Age"] *(-(((data["Medical_Keyword_47"] +(( data["InsuredInfo_6"] < data["Medical_History_18"] ).astype(float)))/2.0)))))) ,(( -(((data["Medical_History_20"] <=(( data["InsuredInfo_1"] +(( data["Insurance_History_2"] + data["Medical_Keyword_47"])/2.0)) /2.0)).astype(float)))))) + 
(((data["Medical_History_21"] > data["Medical_History_7"] ).astype(float)) +(( -(((1.414214 <(data["Medical_History_5"] *(((data["Medical_History_15"] <(data["Ins_Age"] - data["Medical_History_28"])).astype(float)) - data["Ins_Age"])) ).astype(float)))) * 2.0)) + 
(((0.138462 * np.sin(((-(0.138462)) *(data["Medical_History_2"] / 2.0)))) +(( data["Medical_History_39"] <(( data["Medical_History_30"] +(((data["Medical_Keyword_3"] >= 0.138462 ).astype(float)) * np.maximum(( data["Medical_Keyword_24"]),(data["Medical_Keyword_40"])))) /2.0)).astype(float)))/2.0)+ 
(((((data["Medical_History_39"] < data["Medical_History_41"] ).astype(float)) +(np.tanh(((((data["Medical_History_1"] > np.maximum(( np.minimum(( 9.869604),(( data["Medical_History_39"] / 2.0)))) ,(9.869604)) ).astype(float)) + data["Medical_Keyword_34"])/2.0)) -(( data["Medical_History_39"] < data["InsuredInfo_6"] ).astype(float)))) /2.0)/ 2.0)+ 
(((np.minimum(( data["Medical_Keyword_39"]),(data["Medical_Keyword_47"])) + np.power(np.abs(((data["Medical_Keyword_14"] >(-(np.minimum(( data["Medical_History_10"]),(( data["Medical_Keyword_37"] + data["Medical_Keyword_24"])))))).astype(float))), np.sinh(( data["Medical_History_40"] + np.maximum(( data["Medical_History_1"]),(data["Family_Hist_2"])))))) /2.0)* 2.0)+
np.minimum(( np.cos(( data["BMI"] * 2.0))),(( np.minimum(( np.cos(( data["Medical_History_4"] - np.sinh(data["BMI"])))) ,(((data["Medical_History_4"] >(np.sinh(data["BMI"])+ np.floor(np.ceil(( data["BMI"] * 2.0)))) ).astype(float)))) * 2.0)))+ 
(0.138462 *(((((data["SumMedicalKeywords"] < np.cos(data["Product_Info_2_C"])).astype(float)) >= np.sin(((data["Product_Info_3"] + data["SumMedicalKeywords"])/2.0)) ).astype(float)) -(( data["Medical_History_24"] >=(-(( data["Medical_History_4"] * np.sin(data["Medical_Keyword_23"])))) ).astype(float)))) + 
(np.maximum(((0.318310 *(( data["BMI"] < np.minimum(((1.0/(1.0 + np.exp(- np.sin(data["Medical_History_32"]))))),(( np.sin(data["Medical_History_40"])* 2.0)))).astype(float)))) ,(((data["Medical_History_18"] > data["Medical_History_40"] ).astype(float)))) -(( 9.869604 > data["Product_Info_3"] ).astype(float))))
return p
def Individual5(data):
p =(( 5.428570 -(( data["BMI"] +(((data["BMI"] +(((data["Medical_Keyword_3"] -(data["Product_Info_4"] - data["SumMedicalKeywords"])) - np.cos(data["BMI"])) - data["Medical_History_23"])) /2.0)- np.sin(data["Medical_History_4"])))/2.0)) + 
(np.tanh(((data["Medical_History_40"] +(data["Medical_History_15"] -(((( data["InsuredInfo_5"] + data["Medical_History_5"])/2.0)+(data["Insurance_History_2"] +(data["Medical_History_30"] - data["Medical_History_4"])))/2.0)))/2.0)) -(( np.ceil(data["Product_Info_4"])< data["InsuredInfo_7"] ).astype(float)))+
np.tanh(( np.abs(data["Medical_Keyword_41"])+(data["Medical_History_27"] + np.minimum(( data["Medical_History_20"]),(((data["BMI"] +(((data["Family_Hist_4"] +(( data["Medical_History_39"] + data["Medical_History_13"])+ data["InsuredInfo_6"])) /2.0)+ data["Medical_History_11"])) /2.0)))))) + 
(((( data["Medical_Keyword_38"] < data["Medical_History_24"] ).astype(float)) +(( np.minimum(( data["Insurance_History_5"]),(((np.minimum(((-(np.maximum(( data["Medical_History_18"]),(data["Medical_Keyword_38"]))))),(np.minimum(( data["Medical_History_40"]),(data["Medical_History_7"])))) + data["SumMedicalKeywords"])/2.0)))+(( data["InsuredInfo_5"] < data["Product_Info_2_N"] ).astype(float)))/2.0)) /2.0)+
np.minimum(((1.414214 - data["Ins_Age"])) ,(np.minimum(( np.cos(( data["Medical_History_28"] * data["Employment_Info_3"]))),(np.minimum(( np.cos(np.floor(data["Product_Info_4"]))),(np.tanh(( np.tanh(data["Medical_History_31"])-(( data["Medical_History_5"] + data["Medical_History_35"])/2.0)))))))))+ 
(( data["BMI"] *(((((data["Ins_Age"] +(( -(data["Medical_History_4"])) * 2.0)) /2.0)+(((-(data["Wt"])) +(data["SumMedicalKeywords"] * 2.0)) /2.0)) /2.0)/ 2.0)) / 2.0)+ 
(( np.minimum(( np.cos(data["InsuredInfo_1"])) ,(np.tanh(( -(data["InsuredInfo_5"])))))+(( np.tanh(data["Medical_History_1"])+(((np.maximum(( data["Medical_Keyword_25"]),(( np.maximum(( data["Medical_Keyword_6"]),(data["Medical_Keyword_45"])) + data["InsuredInfo_6"])))+ data["Medical_Keyword_22"])/2.0)/ 2.0)) /2.0)) /2.0)+
np.sin(np.round(np.minimum(((np.minimum(( data["Medical_History_11"]),(np.minimum(( data["Medical_History_40"]),(np.minimum(( data["Medical_History_11"]),(data["Medical_History_17"])))))) * 2.0)) ,(np.minimum(( data["Medical_History_33"]),(( -(((data["InsuredInfo_2"] > np.tanh(( data["Medical_History_35"] * data["Medical_Keyword_9"])) ).astype(float)))))))))) +
np.sin(np.maximum(( np.minimum(( np.cos(( data["Medical_Keyword_3"] - data["Medical_Keyword_48"]))),(np.minimum(( np.cos(data["SumEmploymentInfo"])) ,(((np.tanh(np.sin(np.ceil(data["Medical_History_15"])))+(( data["Product_Info_2_C"] >= np.cos(data["Medical_Keyword_33"])).astype(float)))/2.0)))))) ,(data["Medical_Keyword_33"])))+
np.sin(np.minimum(((( data["Product_Info_2_N"] + np.abs(data["Product_Info_2_C"])) /2.0)) ,(np.minimum(( 0.367879),(np.maximum(( data["Medical_Keyword_29"]),(( data["Medical_Keyword_38"] * data["Medical_History_30"])))))))) + 
(0.058823 *(((data["Medical_Keyword_3"] *(data["SumMedicalKeywords"] + np.floor(np.minimum(((data["Medical_Keyword_15"] + data["Family_Hist_5"])) ,(data["Medical_History_30"])))))+(data["Family_Hist_2"] + data["Family_Hist_5"])) +(( data["SumMedicalKeywords"] + data["Medical_History_33"])/2.0)))+
np.minimum(((( data["Product_Info_4"] < np.floor(data["InsuredInfo_7"])).astype(float))),(((((data["Medical_History_20"] >(( data["Product_Info_4"] < np.tanh(((( np.tanh(np.tanh(data["Product_Info_4"])) + np.floor(np.floor(data["InsuredInfo_7"])))/2.0)* 2.0)) ).astype(float)) ).astype(float)) + data["BMI"])/2.0)))+
np.tanh(np.tanh(( np.minimum(( data["BMI"]),(((data["Medical_History_40"] + data["Medical_History_15"])/2.0)))* np.minimum(( np.tanh(((( 1.0/(1.0 + np.exp(- data["Medical_History_15"])))>(-(np.tanh(data["Medical_History_15"])))).astype(float)))) ,(( -(np.tanh(data["Medical_History_15"])))))))) + 
(((( data["Ins_Age"] *(( data["Medical_History_33"] <=(data["Ins_Age"] *(((( data["Medical_History_38"] >= data["Medical_History_38"] ).astype(float)) <= data["BMI"] ).astype(float)))).astype(float)))/ 2.0)+(data["Ins_Age"] * np.tanh(data["Medical_History_5"])))/2.0)+
np.minimum(( 0.318310),(np.minimum(((((( data["Employment_Info_1"] + data["Medical_Keyword_21"])/2.0)+(( data["Medical_History_6"] >= np.floor(data["Insurance_History_8"])).astype(float)))/2.0)) ,(np.sinh(np.floor(np.sinh(( data["Insurance_History_5"] *(( data["Insurance_History_4"] <= np.tanh(data["Employment_Info_2"])).astype(float)))))))))) +
np.minimum(( 0.138462),(((( -(np.floor(np.abs(( data["Product_Info_2_N"] * data["Product_Info_2_N"])))))>= np.minimum(( data["Product_Info_2_C"]),(np.round(np.abs(np.minimum(( np.sin(data["Medical_History_2"])) ,(data["SumMedicalKeywords"])))))) ).astype(float)))) + 
(-(((( math.sin(-1.0)/ 2.0)< np.minimum(( np.ceil(( data["Insurance_History_2"] -(( np.minimum(( np.sin(data["Medical_Keyword_40"])) ,(( -(data["Medical_Keyword_40"])))) < data["Medical_History_15"] ).astype(float))))),(data["Medical_History_15"])) ).astype(float)))) +
np.abs(( np.cos(((data["Medical_Keyword_19"] > data["Medical_History_15"] ).astype(float)))*(((np.tanh(data["Insurance_History_2"])-(( data["Medical_History_15"] >(( np.cos(((data["Insurance_History_2"] > data["Medical_History_4"] ).astype(float)))+ data["Medical_History_4"])/2.0)).astype(float)))>= np.round(data["Medical_History_4"])).astype(float)))) + 
(0.058823 *(( data["InsuredInfo_6"] + np.minimum(( data["Medical_Keyword_15"]),(( data["InsuredInfo_6"] * data["Medical_History_18"])))) +(( data["Medical_Keyword_28"] +(( -(data["Medical_History_19"])) +(( data["Medical_Keyword_3"] * data["InsuredInfo_6"])+ data["Medical_Keyword_19"])))/2.0)))+
np.minimum(( np.maximum(( data["Medical_History_23"]),(data["SumMedicalKeywords"]))),(np.minimum(((np.sin(np.maximum(( data["Medical_History_17"]),(np.floor(data["SumMedicalKeywords"])))) / 2.0)) ,(((data["InsuredInfo_7"] >=(( data["Medical_Keyword_38"] * data["SumMedicalKeywords"])* data["Medical_Keyword_2"])).astype(float)))))) + 
(0.138462 *(( np.cos(( np.sinh(data["BMI"])* 2.0)) + np.cos(((((np.round(data["Medical_History_4"])< data["Ins_Age"] ).astype(float)) - np.sinh(((np.abs(data["Medical_History_22"])< np.sinh(data["BMI"])).astype(float)))) * 2.0)))/2.0)) +
np.minimum(((np.sin(data["Medical_Keyword_32"])*(( data["Employment_Info_2"] >(np.floor(data["Employment_Info_2"])-(np.minimum(( data["Medical_Keyword_37"]),(3.0)) * 2.0)) ).astype(float)))) ,(np.maximum(( np.sin(data["Medical_History_18"])) ,(data["Medical_History_22"])))) +
np.sin(( data["Medical_History_4"] * np.maximum(( data["Medical_Keyword_3"]),(np.sin(( data["Medical_History_4"] * np.minimum(( 0.094340),(((np.maximum(( data["Medical_Keyword_34"]),(data["InsuredInfo_5"])) >(( -(data["Medical_Keyword_15"])) + data["Medical_History_1"])).astype(float)))))))))) +
np.sin(( np.sin(( np.maximum(( data["Medical_History_38"]),(np.round(( data["Medical_History_35"] +(-(((data["Medical_Keyword_24"] > np.minimum(((data["Medical_History_7"] * data["Ht"])) ,(np.maximum(( data["Employment_Info_2"]),(data["Medical_History_13"])))) ).astype(float)))))))) * 2.0)) * 2.0)) + 
(((((( np.minimum(( data["BMI"]),(data["Insurance_History_2"])) >(np.cos(np.cos(data["InsuredInfo_6"])) - np.minimum(( data["Ins_Age"]),(np.abs(data["InsuredInfo_6"])))) ).astype(float)) >= np.tanh(((1.584910 + data["SumInsuredInfo"])/2.0)) ).astype(float)) / 2.0)/ 2.0)+ 
(np.minimum(( np.maximum(((np.minimum(( data["Medical_History_18"]),(data["Medical_History_10"])) * 2.0)) ,(( -(np.minimum(( 5.200000),(data["BMI"]))))))),(np.minimum(( np.tanh(data["SumMedicalKeywords"])) ,(np.round(data["SumMedicalKeywords"])))))*(((data["Medical_History_18"] > data["Medical_History_6"] ).astype(float)) / 2.0)) +
np.tanh(((((((((data["InsuredInfo_1"] < data["BMI"] ).astype(float)) <(( data["InsuredInfo_1"] < data["Medical_History_12"] ).astype(float)) ).astype(float)) <(data["Medical_History_27"] *(data["Medical_History_20"] * data["Medical_History_5"])) ).astype(float)) * data["BMI"])/ 2.0)) + 
(((( data["Medical_History_9"] >(( data["Medical_History_32"] <=(20.750000 + data["InsuredInfo_5"])).astype(float)) ).astype(float)) >=(( np.minimum(( data["InsuredInfo_5"]),(data["Medical_Keyword_1"])) <=(-(np.minimum(( data["InsuredInfo_5"]),(np.minimum(( data["InsuredInfo_6"]),(np.cos(data["Medical_History_40"])))))))).astype(float)) ).astype(float)))
 return p
def Individual6(data):
p =(((5.200000 + np.sin(((data["Medical_History_23"] >(( data["Medical_Keyword_3"] + data["BMI"])/2.0)).astype(float)))) -(((( data["Medical_Keyword_3"] + np.maximum(( data["BMI"]),(data["SumMedicalKeywords"])))/2.0)+(data["BMI"] - data["Medical_History_4"])) /2.0)) + 
(((( data["Medical_History_15"] + np.minimum(((data["Medical_History_40"] - data["Insurance_History_2"])) ,(np.minimum(((data["Product_Info_4"] - data["Medical_History_30"])) ,(np.ceil(( data["Medical_History_13"] - data["InsuredInfo_5"])))))))/2.0)+ np.tanh(( np.ceil(data["Product_Info_4"])- data["InsuredInfo_7"])))/2.0)+
np.minimum(( np.tanh(((data["Medical_History_27"] + np.minimum(( data["Medical_History_11"]),(( np.minimum(( data["Medical_History_31"]),(( np.round(((data["Medical_History_7"] + data["Product_Info_4"])/2.0)) + data["Medical_History_20"])))* 2.0)))) /2.0))),(np.cos(np.maximum(( data["Ins_Age"]),(np.sinh(data["Medical_History_5"])))))) + 
(((((data["InsuredInfo_6"] +(( data["Medical_History_15"] + data["BMI"])- data["Medical_History_18"])) /2.0)+(((( data["Medical_Keyword_25"] - data["Employment_Info_3"])+ data["Insurance_History_3"])+(data["Product_Info_2_N"] +(data["Family_Hist_4"] + data["Medical_Keyword_41"])))/2.0)) /2.0)/ 2.0)+
np.sin(((np.maximum(( data["InsuredInfo_2"]),(np.maximum(( np.floor(data["Product_Info_2_C"])) ,(((data["Medical_History_24"] > data["Medical_History_38"] ).astype(float)))))) + np.sin(((((data["Medical_History_1"] +(( data["Medical_Keyword_33"] + data["Medical_History_4"])/2.0)) /2.0)+(data["Medical_History_5"] * data["Medical_Keyword_3"])) /2.0)))/2.0)) +
np.sin(np.minimum(((data["Medical_History_15"] *(((((data["Medical_History_17"] <=(( 0.720430 + data["Medical_History_15"])/2.0)).astype(float)) * 2.0)-(( data["Product_Info_3"] <= data["Insurance_History_2"] ).astype(float)))* 2.0))),(( data["Medical_History_11"] * 2.0)))) + 
(np.cos(8.0)* np.round(((( data["Medical_Keyword_38"] + data["Medical_History_35"])+(data["InsuredInfo_1"] -(data["Family_Hist_2"] + np.maximum(( np.maximum(( data["Medical_Keyword_34"]),(data["InsuredInfo_5"]))),(data["Medical_Keyword_22"])))))/2.0)))+
np.minimum(( np.tanh(((1.0/(1.0 + np.exp(- data["InsuredInfo_6"])))*(( data["InsuredInfo_6"] + np.cos(data["Medical_History_15"])) /2.0)))) ,(((np.cos(( data["BMI"] -(( data["Medical_History_4"] <(1.0/(1.0 + np.exp(- data["Ins_Age"])))).astype(float)))) + data["Medical_History_15"])/2.0)))+ 
(( np.sin(np.maximum(( data["Medical_History_28"]),(( data["Employment_Info_3"] * data["Medical_History_40"])))) +(( np.maximum(( np.minimum(( data["Medical_History_40"]),(np.cos(((((data["InsuredInfo_5"] + data["Medical_History_40"])/2.0)+ data["Insurance_History_2"])/2.0))))),(data["Medical_History_4"])) < data["Medical_Keyword_19"] ).astype(float)))/2.0)+ 
(0.094340 *(( np.minimum(((data["Medical_History_6"] - data["Medical_Keyword_9"])) ,(np.minimum(( data["Medical_History_39"]),(data["Medical_History_33"])))) +(((data["SumMedicalKeywords"] * data["Ins_Age"])- data["Family_Hist_4"])-(data["Ins_Age"] + data["Product_Info_4"])))/2.0)) +
np.sin(np.sin(np.sin(( 0.138462 *(((data["Medical_Keyword_45"] + data["Medical_Keyword_15"])/2.0)+ np.cos(( np.ceil(data["Product_Info_2_N"])- np.floor(((data["Product_Info_2_N"] < np.power(np.abs(data["Medical_History_21"]), data["Medical_History_21"])).astype(float)))))))))) +
np.minimum(((( np.tanh(data["Medical_History_16"])> data["Medical_History_15"] ).astype(float))),(np.sin(np.round(np.maximum(((data["InsuredInfo_2"] * data["Medical_History_5"])) ,(( np.minimum(( 1.584910),(( -(data["Medical_History_4"])))) * 2.0)))))))+ 
(np.minimum(( np.minimum(((np.minimum(((1.0/(1.0 + np.exp(- data["Product_Info_2_C"])))) ,(data["Medical_Keyword_32"])) * data["Product_Info_2_C"])) ,(np.cos(np.maximum(( np.tanh(data["Product_Info_2_C"])) ,(data["Ins_Age"])))))) ,(((data["Employment_Info_1"] +(( data["Medical_History_33"] > data["Insurance_History_8"] ).astype(float)))/2.0)))/ 2.0)+ 
(((( data["Medical_History_40"] - data["Medical_History_30"])<(( np.sin(data["Medical_Keyword_2"])< data["Medical_Keyword_40"] ).astype(float)) ).astype(float)) *(( data["Medical_Keyword_3"] >=(((( data["Medical_Keyword_32"] * data["Medical_History_30"])* data["Medical_Keyword_3"])+ data["Medical_History_11"])/2.0)).astype(float)))+
np.minimum(((data["SumMedicalKeywords"] *(( data["Medical_History_18"] >= data["Medical_History_39"] ).astype(float)))) ,(np.minimum(( 0.058823),(np.maximum(( data["Wt"]),(np.minimum(( np.sin(np.ceil(data["Product_Info_4"]))),(( data["InsuredInfo_6"] *(( data["Medical_History_19"] > data["Medical_History_39"] ).astype(float)))))))))))+
np.minimum(((0.138462 *(((( data["Medical_History_23"] <=(data["Medical_Keyword_25"] - data["Medical_History_13"])).astype(float)) <=(data["Medical_Keyword_12"] - data["Product_Info_4"])).astype(float)))) ,(((0.094340 - np.minimum(((( data["InsuredInfo_5"] < data["Product_Info_4"] ).astype(float))),(data["BMI"])))/ 2.0)))+ 
(( np.tanh(((data["Medical_History_23"] >=(( data["BMI"] < np.sinh(np.power(np.abs(0.094340), data["Medical_History_32"])) ).astype(float)) ).astype(float)))+ np.sin(( data["Medical_History_13"] * np.floor(((data["Insurance_History_2"] >= data["Medical_History_40"] ).astype(float)))))) /2.0)+ 
(((data["Medical_History_22"] <(((np.cos(np.minimum(( data["Medical_History_22"]),(np.floor(((266)* data["Medical_History_15"])))))* np.cos(data["Medical_Keyword_3"])) < data["Medical_History_19"] ).astype(float)) ).astype(float)) * np.minimum(( data["Insurance_History_2"]),(( data["Medical_History_41"] / 2.0)))) +
np.sinh(( np.floor(np.cos(((data["Insurance_History_5"] - 0.367879)+(data["Insurance_History_7"] + np.minimum(( data["Insurance_History_5"]),(( data["Insurance_History_5"] +(data["Insurance_History_4"] - 31.006277)))))))) * 2.0)) + 
(np.sin(((((data["Medical_Keyword_28"] > np.cos(data["Medical_Keyword_6"])).astype(float)) >= np.cos(np.maximum(( np.minimum(( np.round(data["Family_Hist_5"])) ,(data["BMI"]))),(((data["Medical_Keyword_3"] *(( data["InsuredInfo_6"] > data["Medical_History_36"] ).astype(float)))+ data["Medical_Keyword_2"])))) ).astype(float)))/ 2.0)+
np.minimum(((( np.sin(data["Medical_Keyword_41"])>= data["Medical_History_15"] ).astype(float))),(((data["SumMedicalKeywords"] *(( data["Medical_History_5"] >= np.minimum(((( data["SumMedicalKeywords"] *(((( data["InsuredInfo_5"] >= data["Medical_History_20"] ).astype(float)) >= data["Medical_History_31"] ).astype(float)))/ 2.0)) ,(data["Medical_History_17"])) ).astype(float)))/ 2.0)))+
np.tanh(( data["Medical_History_4"] * np.sinh(( np.minimum(( np.minimum(((((( -(data["BMI"])) * data["Product_Info_5"])>= data["Medical_History_17"] ).astype(float))),(np.maximum(( 0.138462),(data["Medical_History_15"]))))),(np.maximum(((-(data["BMI"]))),(data["Medical_History_15"])))) / 2.0)))) + 
(np.tanh(np.sin(np.tanh(np.sin(data["Medical_Keyword_12"])))) * np.minimum(( np.cos(np.floor(( -(( data["Medical_History_2"] *(997))))))),(np.cos(( -(((data["Medical_History_2"] * 8.0)*(997)))))))) + 
(0.058823 * np.minimum(( np.power(np.abs(data["Product_Info_4"]),(( 112)*(( data["Product_Info_4"] <= data["Medical_Keyword_15"] ).astype(float))))),(((data["Medical_Keyword_15"] * 2.0)+(np.cos(((1.630430 * data["Employment_Info_2"])* 2.0)) + data["Medical_History_29"])))))+ 
(( data["Ins_Age"] *(((( data["Medical_History_5"] >= np.cos(np.power(np.abs(data["BMI"]),(np.abs(((data["Ins_Age"] + data["BMI"])/2.0)) / 2.0)))).astype(float)) >= np.cos(((np.power(np.abs(data["Ins_Age"]), data["Medical_Keyword_23"])/ 2.0)/ 2.0)) ).astype(float)))/ 2.0)+
np.sin(np.maximum(( data["Medical_Keyword_29"]),(np.maximum(( data["Medical_Keyword_25"]),(np.tanh(((( data["Medical_Keyword_8"] * data["Medical_History_18"])>= np.abs(((( np.maximum(((data["Medical_History_15"] / 2.0)) ,(data["Medical_Keyword_29"])) + data["Medical_Keyword_29"])/2.0)-(data["Medical_History_15"] / 2.0)))).astype(float)))))))) +
np.tanh(((data["InsuredInfo_5"] * np.maximum(((( np.floor(data["Medical_History_40"])*(1.584910 - data["InsuredInfo_1"])) / 2.0)) ,(np.minimum(( data["Employment_Info_3"]),(( 5.428570 *(( data["Medical_History_28"] > data["Medical_Keyword_38"] ).astype(float)))))))) * 2.0)) +
np.minimum(((( data["Medical_History_27"] >=(( data["Medical_History_10"] <(2.44962)).astype(float)) ).astype(float))),(np.sin(np.sin(np.ceil(np.maximum(( data["SumMedicalHistory"]),(np.maximum(( data["SumEmploymentInfo"]),(np.maximum(( data["SumMedicalHistory"]),(( data["Medical_History_11"] * data["Ht"])))))))))))))
 return p
def MungeData(train, test):
le = LabelEncoder()
train.Product_Info_2.fillna('Z0', inplace=True)
 test.Product_Info_2.fillna('Z0', inplace=True)
 train.insert(0, 'Product_Info_2_N', train.Product_Info_2.str[1:])
 train.insert(0, 'Product_Info_2_C', train.Product_Info_2.str[0])
 train.drop('Product_Info_2', inplace=True, axis=1)
 test.insert(0, 'Product_Info_2_N', test.Product_Info_2.str[1:])
 test.insert(0, 'Product_Info_2_C', test.Product_Info_2.str[0])
 test.drop('Product_Info_2', inplace=True, axis=1)
 le.fit(list(train.Product_Info_2_C)+list(test.Product_Info_2_C))
train.Product_Info_2_C = le.transform(train.Product_Info_2_C)
 test.Product_Info_2_C = le.transform(test.Product_Info_2_C)
 trainids = train.Id
testids = test.Id
train.drop('Id', axis=1, inplace=True)
 test.drop('Id', axis=1, inplace=True)
 responses = train.Response.values
train.drop('Response', inplace=True, axis=1)
 train = train.astype(float)
 test = test.astype(float)
 train.insert(0,
'SumNAs',
len(train.columns)-train.count(axis=1))
test.insert(0,
'SumNAs',
len(test.columns)-test.count(axis=1))
train.insert(0,
'SumMedicalKeywords',
train[train.columns[train.columns.str.contains('Medical_Keyword')]] 
.sum(axis=1, skipna=True))
test.insert(0,
'SumMedicalKeywords',
test[test.columns[test.columns.str.contains('Medical_Keyword')]] 
.sum(axis=1, skipna=True))
train.insert(0,
'SumFamilyHistory',
train[train.columns[train.columns.str.contains('Family_Hist')]] 
.sum(axis=1, skipna=True))
test.insert(0,
'SumFamilyHistory',
test[test.columns[test.columns.str.contains('Family_Hist')]] 
.sum(axis=1, skipna=True))
train.insert(0,
'SumInsuranceHistory',
train[train.columns[train.columns.str 
.contains('Insurance_History')]] 
.sum(axis=1, skipna=True))
test.insert(0,
'SumInsuranceHistory',
test[test.columns[test.columns.str.contains('Insurance_History')]] 
.sum(axis=1, skipna=True))
train.insert(0,
'SumInsuredInfo',
train[train.columns[train.columns.str.contains('InsuredInfo')]] 
.sum(axis=1, skipna=True))
test.insert(0,
'SumInsuredInfo',
test[test.columns[test.columns.str.contains('InsuredInfo')]] 
.sum(axis=1, skipna=True))
train.insert(0,
'SumEmploymentInfo',
train[train.columns[train.columns.str.contains('Employment_Info')]] 
.sum(axis=1, skipna=True))
test.insert(0,
'SumEmploymentInfo',
test[test.columns[test.columns.str.contains('Employment_Info')]] 
.sum(axis=1, skipna=True))
train.insert(0,
'SumMedicalHistory',
train[train.columns[train.columns.str.contains('Medical_History')]] 
.sum(axis=1, skipna=True))
test.insert(0,
'SumMedicalHistory',
test[test.columns[test.columns.str.contains('Medical_History')]] 
.sum(axis=1, skipna=True))
train.fillna(-1, inplace=True)
 test.fillna(-1, inplace=True)
 train['Response'] = responses
train.insert(0, 'Id', trainids)
 test.insert(0, 'Id', testids)
 return train, test
if __name__ == "__main__":
ss = StandardScaler()
train = pd.read_csv(".. /input/train.csv")
 test = pd.read_csv(".. /input/test.csv")
 train, test = MungeData(train, test)
 features = train.columns[1:-1]
scaledtrain = train.copy()
scaledtest = test.copy()
scaledtrain[features] = ss.fit_transform(scaledtrain[features])
 scaledtrain[features] = np.round(scaledtrain[features],6)
 scaledtest[features] = ss.transform(scaledtest[features])
 scaledtest[features] = np.round(scaledtest[features],6)
 train[features] = np.round(train[features],6)
 test[features] = np.round(test[features],6)
 trainindividual1 = np.clip(Individual1(scaledtrain),1,8)
 trainindividual2 = np.clip(Individual2(scaledtrain),1,8)
 trainindividual3 = np.clip(Individual3(scaledtrain),1,8)
 trainindividual4 = np.clip(Individual4(train),1,8)
 trainindividual5 = np.clip(Individual5(scaledtrain),1,8)
 trainindividual6 = np.clip(Individual6(scaledtrain),1,8)
 testindividual1 = np.clip(Individual1(scaledtest),1,8)
 testindividual2 = np.clip(Individual2(scaledtest),1,8)
 testindividual3 = np.clip(Individual3(scaledtest),1,8)
 testindividual4 = np.clip(Individual4(test),1,8)
 testindividual5 = np.clip(Individual5(scaledtest),1,8)
 testindividual6 = np.clip(Individual6(scaledtest),1,8)
 trainPredictions =(trainindividual1+trainindividual2+trainindividual3+trainindividual4+trainindividual5+trainindividual6)/6.
 testPredictions =(testindividual1+testindividual2+testindividual3+testindividual4+testindividual5+testindividual6)/6.
 cutoffs = [2.951759, 3.653780, 4.402781, 4.911808, 5.543988, 6.135754, 6.716891]
trainPredictions = np.digitize(trainPredictions,cutoffs)+1
testPredictions = np.digitize(testPredictions,cutoffs)+1
pdtrain = pd.DataFrame({"Id": train.Id.astype(int), "Prediction":trainPredictions.astype(int), "Response": train.Response.astype(int)})
 pdtrain = pdtrain.set_index('Id')
 pdtrain.to_csv('gptrain.csv')
 pdtest = pd.DataFrame({"Id": test.Id.astype(int), "Response": testPredictions.astype(int)})
 pdtest = pdtest.set_index('Id')
 pdtest.to_csv('gptest.csv')