Online Payment Fraud Detection using Machine Learning in Python
ABSTARCT :
As we are approaching modernity, the trend of paying online is increasing tremendously. It is very beneficial for the buyer to pay online as it saves time, and solves the problem of free money.
Also, we do not need to carry cash with us. But we all know that Good thing are accompanied by bad things.
The online payment method leads to fraud that can happen using any payment app. That is why Online Payment Fraud Detection is very important.
EXISTING SYSTEM :
Online fraud exposes users to the possibility of their data being compromised, as well as the inconvenience of having to report the fraud, block their payment method, and other things. When businesses are involved, it causes some issues; occasionally, they must issue refunds in order to keep customers.
Therefore, it is crucial that both consumers and businesses are aware of these internet scams. A model to determine if an online payment is fraudulent or not is put forth in this study. To determine if a certain Online payment is fraudulent or not, some features like the type of payment, the recipient’s identity, etc. would be taken into account.
DISADVANTAGE :
The decision tree classifier in your project has certain drawbacks. It is prone to overfitting, especially when the tree is deep, which captures noise and leads to poor generalization. Decision trees are sensitive to small variations in the training data, resulting in different tree structures with slight changes.
They can be biased toward features with more levels, lack global optimality, and struggle with imbalanced datasets. While decision trees offer simplicity and interpretability for shallow trees, deep trees are complex and challenging to interpret.
In addition However they may not capture complex relationships as effectively as more advanced models such as neural networks. Consideration of these limitations is essential, and alternative algorithms such as random forests, gradient boosting, or neural networks might be explored based on the specific characteristics of the data and the project's objectives.
PROPOSED SYSTEM :
The provided code implements an interactive fraud detection application using a pre-trained random forest classifier. The model is loaded from the .sav file, and an IPython widget-based user interface is created, which allows users to input transaction details.
These details include account age, number of items, local time, payment method, and payment method age. Upon clicking the "Check Fraud" button, the code processes the user-entered data through the Random Forest model and displays the prediction in the output area, indicating whether the transaction is classified as "Fraud" or "Not Fraud.“
This application provides a convenient and intuitive means for users to leverage machine learning for fraud detection in real-time scenarios.
ADVANTAGE :
Numerous research are being conducted using the data in a way that protects privacy. One of the experiments was carried out using blockchain technology and machine learning techniques, according to Kalbande et al..
The usage of block chain technology, however, can be helpful in protecting the privacy of the data, but we cannot ignore the fact that it is a decentralized solution and has some drawbacks along with it, such as scalability issues and high energy consumption.
A supervised machine learning strategy utilizing block chain technology was developed by Thennakoon et al.. Ethereum was employed by the author to implement block chain technology. 300,000 accounts were used in the study, and the outcomes were compared with a number of machine learning techniques.
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