This project is an investigative work on detecting fraudulent transactions and fraudulent accounts in a transactions dataset
The file named transport_data.csv consists of data from a fictional transportation company. It details the number of orders that customers of this company make on any given day, with some additional variables.
• Provided exploratory analysis of the dataset.
• Summarised and explain the key trends in the data, providing visualisations and tabular representations as necessary.
• Constructed a model or models to predict the number of jobs that this transportation company will complete on any given day.
The file name customers_devices_cards.csv is a list of fraudulent users of a smartphone app that takes payments. Each line represents a user, a device and a credit card. To detect networks in this file we:
• Provided a visualisation of any networks found.
• Detected and hypothesised two different types of fraudulent behaviour from the data provided.
The file name fraudulent_orders_locations.csv is data from an e-commerce company. It contains a month's worth of fraudulent orders.
• Provided exploratory analysis of the dataset.
• Where should the fraud detection team concentrate their efforts?
• Where is the worst hotspot in terms of number of orders? And in terms of combined order values?
• Identify any limitations you think apply to this dataset.
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