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Fraud_Detection

mohammed Saket

mohammed Saket

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Each row in fraud_data.csv corresponds to a credit card transaction. Features include confidential variables V1 through V28 as well as Amount which is the amount of the transaction. The target is stored in the class column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud. ...learn more

Project status: Under Development

Artificial Intelligence

Groups
Student Developers for AI

Code Samples [1]

Overview / Usage

Each row in fraud_data.csv corresponds to a credit card transaction. Features include confidential variables V1 through V28 as well as Amount which is the amount of the transaction.
The target is stored in the class column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud.

Methodology / Approach

In this project, we used 3 different machine learning approach-

  1. DummyClassifier
  2. SVC
    3.LogisticRegression
    and calulate accuracy
    1-accuracy= 97.08702064896755 %
    2-accuracy= 99.63126843657817 %
    3- accuracy= 99.64970501474927 %
    So, We use use Logistic Regression to detect fraud.

And plot Heat map between variables V1 through V28.

Technologies Used

  1. Python
    2.Anaconda
    3.Machine learning
  2. Sklearn and python lib.

Repository

https://github.com/mohammedsaket/Fraud_Detection

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