blackheart
New Member
The dataset contains the Credit and Debit transactions recorded by 100 fictional CB users. The CEO asks you to analyze the data and extract some insights from the dataset.
Requirment 1
Organize the data and give an understanding of how sticky these users are. Stickiness is to be measured by # of weekly transactions and weeks actively transacting. Identify the top 10 users of the group in terms of:
● Average transactions per active week
● # Weeks actively transacting Present the results in the way you think it’s most appropriate and easiest to digest.
Requirment 2
Analyze the credit cycle of the top 10 users. Understand if they are more on the lending or borrowing side. How financially exposed they are. From your discoveries make assumptions on what type of business they are running. Suppliers, wholesalers, retailers etc. and justify your answers.
Dataset information
● user_id : unique identifier of the user. business owner who registers the transaction
● customer_id: unique identifier for the customer. Customer with whom the business owner performs the specific transactions
● transaction_amount: transaction amount (PKR)
● transaction_type: defines a transaction either as a credit (the customer owes money to the shop owner) or debit (the business owner is buying something without immediately paying)
● transaction_date: date of the transaction
Dataset Donwload
Requirment 1
Organize the data and give an understanding of how sticky these users are. Stickiness is to be measured by # of weekly transactions and weeks actively transacting. Identify the top 10 users of the group in terms of:
● Average transactions per active week
● # Weeks actively transacting Present the results in the way you think it’s most appropriate and easiest to digest.
Requirment 2
Analyze the credit cycle of the top 10 users. Understand if they are more on the lending or borrowing side. How financially exposed they are. From your discoveries make assumptions on what type of business they are running. Suppliers, wholesalers, retailers etc. and justify your answers.
Dataset information
● user_id : unique identifier of the user. business owner who registers the transaction
● customer_id: unique identifier for the customer. Customer with whom the business owner performs the specific transactions
● transaction_amount: transaction amount (PKR)
● transaction_type: defines a transaction either as a credit (the customer owes money to the shop owner) or debit (the business owner is buying something without immediately paying)
● transaction_date: date of the transaction
Dataset Donwload
dataset.csv
drive.google.com