Based on the original data, I removed some data, which had invalid names and addresses. And I selected some fields that are important and meaningful from 280 fields. There are 234,090,073 us data.
Compromised data:
HH_ID,First_Name_01,Last_Name_01,Ind_Gender_Code,Ind_Date_Of_Birth_Year,Ind_Date_Of_Birth_Month,Ind_Occupation_Code,Ind_Ethnic_Code,Address,Address_ID,City,State,ZIP,"Geo_Lat_Long",Marital_Status_Code,Number_Children_Code,Children_Present_Flag,Veteran_Present_HH_Flag,Email,Phone,CellPhone,"Home_Value_Description",Home_Owner_Renter_Code,Home_Square_Footage,Home_Dwelling_Type_Code,Home_Purchase_Date,Home_Built_Year,"Income_Description","Credit_Capacity_Description",Email_02,Email_03,Email_04,Email_05
Sample:
14001331485957,John,Loflin,M,1980,11,1802,nor,35392 Gore Dr SE,1495135291,Albany,OR,97322,"44.560516,-122.9806242",M,0,,,,,,"$300,000 to $349,999",,,S,201908,,"$100,000 to $149,999","$10,000 to $24,999",,,,
13977999998085,Amber,Osterholme,F,1978,1,,dut,20900 Yosemite St NE,622383241,Aurora,OR,97002,"45.2227803,-122.7541828",M,,,,,,,"$350,000 to $399,999",O,3100,S,200511,2005,"$150,000 to $174,999","$25,000 to $49,999",,,,
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