Abstract
In recent years credit card became one of the essential parts of the people. Sudden increase in E-commerce, customer started using credit card for online purchasing therefore risk of fraud also increases. Instead of carrying a huge amount in hand it is easier to keep credit cards. But nowadays that too becomes unsafe. Now a days we are facing a big problem on credit card fraud which is increasing in a good percentage. The main purpose is the survey on the various methods applied to detect credit card frauds. From the abnormalities, in the transaction, the fraudulent one is identified. We address this issue in order to implement some machine learning algorithm like logistic regression in order to detect this kind of fraud. In this paper we increase the efficiency in finding the fraud. Currently, the issues of credit card fraud detection have become a big problem for new researchers. We implement an intelligent algorithm which will detect all kind of fraud in a credit card transaction. We handled the problem by finding a pattern of each customer in between fraud and legal transaction. Logistic Regression Algorithm and Local Outlier Factor are used to predict the pattern of transaction for each customer and a decision is made according to them. In order to prevent data from mismatching, all attribute are marked equally.
Most of the ML models out there are trying to predict Credit card Frauds using historical Fraud data and other technical indicators — i.e., NUMERIC INPUTS
With Machine Learning (ML) technology a Fraud prediction problem is formulated as a Classification analysis which is a statistical technique used to estimate the relationship between a dependent/target variable and single or multiple independent (interdependent) variables.
Our project focusses Predicting Frauds with use of credit card fraud dataset.
These datasets are used for training the Machine Learning system through Logistic Regression algorithm and used for the prediction of credit card frauds. This is very useful to Identify the fraud transactions.
Existing System
- Credit Card Fraud prediction is the act of trying to Identify the future frauds of credit card usage .
- The successful prediction of Credit Card Fraud could yield significant decrease in risks.
- The future, like any complex problem, has far too many variables to be predicted. Quantitative models, historical models, even psychic models have all been tried and have all failed.
- The existing system is done with logistic regression technique which randomized the data and predicting the future.
- The problem with the existing system is its, prediction data which is not accurate or almost not matching the nearest values.
Proposed System
- Predicting how the fraud will perform is one of the most difficult things to do. There are so many factors involved in the prediction.
- We need a clear, very strong prediction system which will have more accuracy in pinpointing the data for accuracy.
- Our system will take the normal data for consideration and in case of more accuracy raw data too can be taken for prediction.
- We built the model with the training data sets and the prediction involves verifying the testing data.
- The prediction is much better and accurate when compared to other machine learning algorithms.
Hardware Requirements
- Processor : Intel Core i3 (or above)
- System Type : 64-bit Operating System
- Storage : 500 GB HDD / SSD
- RAM : 4 GB (minimum)
Software Requirements
- Operating System : Windows 10 (or above)
- Software : Anaconda Navigator, Python IDE
- Libraries: Pandas, NumPy, Matplotlib, Keras
- Programming Language : Python
Architecture Diagram
Project Modules
- Get the fraud quote
- Visualize the Fraud history
- Scale the data
- Create the scaled training dataset
- Visualize the data
- Show the valid and prediction
Components of Project Report
- Abstract
- Table of Contents
- List of Tables
- List of Figures
- Chapters
- Introduction
- Literature review
- Problem definition and requirement analysis
- Design and Implementation
- Testing and deployment
- Future enhancements
- Summary
- References
Project Report Pages : 80
Can be used in : Data Science
Delivery Time : Within 2 hours.
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