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I have started learning ML and I am stuck at finding a problem solution. I need the steps to follow up: Below are the formats of data sets: cust_Demographics

Customer_ID         0
Nationality         0
Income_Range        0
Job_Type            0
Marital_Status      0
Gender              0
State               0
Language            0
Loyalty_Status      0
Age                 0
Points              0
recent_tran_date    0
Recency             0
custSince           0
Frequency           0
Monetary            0
90999

Customer_Transaction

Year                0
Week                0
Store_Code          0
City_Name           0
Transaction_Type    0
Customer_ID         0
Invoices            0
Item_Count          0
Revenue             0
Discount            0
Units_Sold          0
500000

Customer_Store

Region              0
Region_Code         0
Store_Code          0
Store_Name          0
Mall_Name           0
Sales_Per_Day       0
Store_Size_Sq_Ft    0
Customer_Count      0
Total_Revenue       0
38

Test
Customer_ID    0
Store_Code     0
400000

Problem: Need to predict whether a customer goes to a store(new stores) or not.(0/1).

So , I merge Demographics,Transaction and store table.

Q1 : I have total rows= 500000 and added one target column as 1(represents they went for shopping) so all the values are 1's for this column so how should I train to predict the target value.

Q2 : If I merge Test,demographics,Store it is ok but how can I use the features of transaction table. customer->store (A Customer shops for that store)

Or can it be related to the recommendation based problem?

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