Fraudulent transactions

The following dataset contains the following fields that describe the online transaction:

• user_id: unique id of user
• signup_time: time that the user created account
• purchase_time: time of the transaction
• purchase_value: amount of the transaction
• device_id: device that user conducted transaction on
• source: attribution channel for the transaction
• browser: browser that user conducted transaction on
• sex: gender of the user
• age: age of the user
• ip_address: IP address of the purchase
• is_fraud: if the transaction is flagged as fraudulent

Can you describe the major differences between the non-fraud vs fraud transactions?

More specifically, can you create histograms for purchase value, time between sign up and purchase time, and age -- with fraud/non-fraud differentiated on the same chart?

Solution will be written using Python.