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Anomaly Detection

CSV files include deposits,loans taken and all successful purchases of token between January and March 2019. The features include:

  • address: the mobile number of account
  • amount/transamount: the total paid or borrowed for loan purchase
  • unit: the unit of the token
  • transactionid : unique identifier for each transaction
  • time/systranstime: the timestamp of the transaction
  • billrefnumber:unique identifier for account

successfulVENDS2019.csv: holds data for successful purchases

deposit2019.csv: holds data for deposits

loan2019.csv: holds data for borrowed amounts to purchase tokens

Involves exploring system behaviour and customer behaviour

Exploration

System Behaviour

  • Volume.ipynb: Analyzes the amounts over time
  • Transaction.ipynb: Analyzes number of transactions over time

Customer Behaviour

  • Customer Behaviour.ipynb: Analyzes customer behaviour

Model Building

  • Customer Behaviour Regression Models.ipynb: Created regression models to predict a customer's amount using past amounts and timestamp

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