Subscribe YouTube For Latest Update Click Here

Latest Machine Learning Project with Source Code

Buy Now ₹1501

Buy Now Project Report ₹1001

Frauds in mastercard transactions are common today as most folks are using the mastercard payment methods more frequently. this is often thanks to the advancement of Technology and increase in online transaction leading to frauds causing huge loss . Therefore, there's need for effective methods to scale back the loss. additionally , fraudsters find ways to steal the mastercard information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack then on. This paper aims in using the multiple algorithms of Machine learning like support vector machine (SVM), k-nearest neighbor (Knn) and artificial neural network (ANN) in predicting the occurrence of the fraud. Further, we conduct a differentiation of the accomplished supervised machine learning and deep learning techniques to differentiate between fraud and non-fraud transactions.

link of dataset=https://www.kaggle.com/mlg-ulb/creditcardfraud

The datasets contains credit card transactions over a two day collection period in September 2013 by European cardholders. There are a total of 284,807 transactions, of which 492 (0.172%) are fraudulent.

The dataset contains numerical variables that are the result of a principal components analysis (PCA) transformation. This transformation was applied by the original authors to maintain confidentiality of sensitive information. Additionally the dataset contains Time and Amount, which were not transformed by PCA. The Time variable contains the seconds elapsed between each transaction and the first transaction in the dataset. The Amount variable is the transaction amount, this feature can be used for example-dependant cost-senstive learning. The Class variable is the response variable and indicates whether the transaction was fraudulant.

The dataset was collected and analysed during a research collaboration of Worldline and the Machine Learning Group of Université Libre de Bruxelles (ULB) on big data mining and fraud detection.

Models

• Applied various classification techniques like :-
• Logistic Regression Light
• GBM K Nearest Neighbors (KNN ) Classification
• Trees Random Forest
• SVM XGBoost Classifier

Technology Used in the project :-

1. We have developed this project using the below technology
2. HTML : Page layout has been designed in HTML
3. CSS : CSS has been used for all the desigining part
4. JavaScript : All the validation task and animations has been developed by JavaScript
5. Python : All the business logic has been implemented in Python
6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

1. We can configure this project on following operating system.
2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
3. Python 3.6.10, PIP, Django.
4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

1. python 3.6.8
2. command 1 - python -m pip install --user -r requirements.txt
3. command 2 - python app.py

Read Before Purchase  :

1. One Time Free Installation Support.
2. Terms and Conditions on this page: https://projectworlds/terms
3. We offer Paid Customization installation Support
4.  If you have any questions please contact  Support Section
5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
6. You can download the product after the purchase by a direct link on this page.