CodeBender's starred repositories
LibrePilot
This is the GitHub mirror for the LibrePilot source code. The main development is taking place at https://bitbucket.org/librepilot
DataSciencePractice
DataScience Practice Programs by AkshatSoni64
Credit-Card-Fraud-Detection-using-Machine-Learning-Model
“Fraud detection is a set of activities that are taken to prevent money or property from being obtained through false pretenses.” Fraud can be committed in different ways and in many industries. The majority of detection methods combine a variety of fraud detection datasets to form a connected overview of both valid and non-valid payment data to make a decision. This decision must consider IP address, geolocation, device identification, “BIN” data, global latitude/longitude, historic transaction patterns, and the actual transaction information. In practice, this means that merchants and issuers deploy analytically based responses that use internal and external data to apply a set of business rules or analytical algorithms to detect fraud. Credit Card Fraud Detection with Machine Learning is a process of data investigation by a Data Science team and the development of a model that will provide the best results in revealing and preventing fraudulent transactions. This is achieved through bringing together all meaningful features of card users’ transactions, such as Date, User Zone, Product Category, Amount, Provider, Client’s Behavioral Patterns, etc. The information is then run through a subtly trained model that finds patterns and rules so that it can classify whether a transaction is fraudulent or is legitimate. All big banks like Chase use fraud monitoring and detection systems.
Data-science-practice
My first steps aftere a datascience masterclass, everything was done by reading the pandas first steps documentation,
Data-Science-Practice-With-Python
Step by step practice of data science concepts
bfx-core-volume
Proof that the BankersFX Core Volume indicator is not feeded by institutional data