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Credit Card Fraud Detection Using Machine Learning Source Code ( Final Year)

Download clean, well-commented Credit Card Fraud Detection Using Machine Learning source code for final year projects — easy to run, demo-ready, and mentor-friendly.

  • MACHINE-LEARNING Project
  • MySQL / MongoDB
  • Setup guide & demo steps
  • Beginner-friendly
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Admin Features

  • Dataset Description:

  • The dataset consists of credit card transactions made by European cardholders in September 2013.
  • It spans transactions over two days, comprising 284,807 transactions.
  • Within this dataset, there are 492 instances of fraudulent transactions, making up 0.172% of the total transactions.
  • The dataset is highly unbalanced, with frauds being the minority class.

  • Objective:

  • Develop a machine learning model to identify fraudulent credit card transactions.
  • Ensure that customers are not wrongly charged for purchases they did not make.

  • Data Features:

  • The dataset primarily contains numerical input variables.
  • Principal Component Analysis (PCA) has been applied to most features, resulting in features labeled as V1 to V28.
  • The 'Time' feature represents the seconds elapsed between each transaction and the first transaction in the dataset.
  • The 'Amount' feature indicates the transaction amount.
  • The response variable 'Class' indicates whether a transaction is fraudulent (1) or not (0).

  • Model Evaluation:

  • Due to the class imbalance, traditional metrics like accuracy are not suitable for evaluation.
  • The recommended metric for evaluation is the Area Under the Precision-Recall Curve (AUPRC).
  • AUPRC provides a comprehensive assessment of the model's performance, particularly in the context of imbalanced datasets.

  • Challenges:

  • Dealing with the highly imbalanced nature of the dataset poses a significant challenge.
  • Ensuring that the model does not overfit to the majority class and can accurately identify fraudulent transactions despite their rarity.

  • Inspiration:

  • The primary motivation is to protect customers from fraudulent charges and maintain the integrity of credit card transactions.
  • Detecting fraudulent transactions efficiently is crucial for financial institutions to maintain trust and security in their services.
Description

The final-year Credit Card Fraud Detection Using Machine Learning source code is structured for fast setup and easy customization. You get readable code, clear folder architecture, and a guided README so you can run locally and present confidently.

Quick setupRun in minutes with README
Clean architectureModular & scalable
Responsive UIMobile & desktop ready
What you’ll build & learn
  • Implement core modules and end-to-end workflow
  • Connect to database and handle CRUD operations
  • Follow best practices for project structure & comments
Included in the download
FrontendPages, components, assets
BackendAPIs, controllers, services
DatabaseSchema & sample/seed data
READMESetup steps, scripts, notes
Source Code Overview
Technical snapshot & environment
Project Name
Credit Card Fraud Detection Using Machine Learning
Language / Stack
machine-learning
Database
MySQL or MongoDB
UI Technologies
HTML, AJAX, jQuery, JavaScript, Bootstrap
Browsers
Chrome, Firefox, Edge, Opera
Run Scripts
Documented in README (install, seed, start)
License
Academic use for college submission
Academic use only: this code is provided to help you learn and submit your college project. For institute-specific formatting or extra diagrams, contact us on WhatsApp.
User Features

  • Dataset Description:

  • The dataset consists of credit card transactions made by European cardholders in September 2013.
  • It spans transactions over two days, comprising 284,807 transactions.
  • Within this dataset, there are 492 instances of fraudulent transactions, making up 0.172% of the total transactions.
  • The dataset is highly unbalanced, with frauds being the minority class.

  • Objective:

  • Develop a machine learning model to identify fraudulent credit card transactions.
  • Ensure that customers are not wrongly charged for purchases they did not make.

  • Data Features:

  • The dataset primarily contains numerical input variables.
  • Principal Component Analysis (PCA) has been applied to most features, resulting in features labeled as V1 to V28.
  • The 'Time' feature represents the seconds elapsed between each transaction and the first transaction in the dataset.
  • The 'Amount' feature indicates the transaction amount.
  • The response variable 'Class' indicates whether a transaction is fraudulent (1) or not (0).

  • Model Evaluation:

  • Due to the class imbalance, traditional metrics like accuracy are not suitable for evaluation.
  • The recommended metric for evaluation is the Area Under the Precision-Recall Curve (AUPRC).
  • AUPRC provides a comprehensive assessment of the model's performance, particularly in the context of imbalanced datasets.

  • Challenges:

  • Dealing with the highly imbalanced nature of the dataset poses a significant challenge.
  • Ensuring that the model does not overfit to the majority class and can accurately identify fraudulent transactions despite their rarity.

  • Inspiration:

  • The primary motivation is to protect customers from fraudulent charges and maintain the integrity of credit card transactions.
  • Detecting fraudulent transactions efficiently is crucial for financial institutions to maintain trust and security in their services.
Other Features

  • Dataset Description:

  • The dataset consists of credit card transactions made by European cardholders in September 2013.
  • It spans transactions over two days, comprising 284,807 transactions.
  • Within this dataset, there are 492 instances of fraudulent transactions, making up 0.172% of the total transactions.
  • The dataset is highly unbalanced, with frauds being the minority class.

  • Objective:

  • Develop a machine learning model to identify fraudulent credit card transactions.
  • Ensure that customers are not wrongly charged for purchases they did not make.

  • Data Features:

  • The dataset primarily contains numerical input variables.
  • Principal Component Analysis (PCA) has been applied to most features, resulting in features labeled as V1 to V28.
  • The 'Time' feature represents the seconds elapsed between each transaction and the first transaction in the dataset.
  • The 'Amount' feature indicates the transaction amount.
  • The response variable 'Class' indicates whether a transaction is fraudulent (1) or not (0).

  • Model Evaluation:

  • Due to the class imbalance, traditional metrics like accuracy are not suitable for evaluation.
  • The recommended metric for evaluation is the Area Under the Precision-Recall Curve (AUPRC).
  • AUPRC provides a comprehensive assessment of the model's performance, particularly in the context of imbalanced datasets.

  • Challenges:

  • Dealing with the highly imbalanced nature of the dataset poses a significant challenge.
  • Ensuring that the model does not overfit to the majority class and can accurately identify fraudulent transactions despite their rarity.

  • Inspiration:

  • The primary motivation is to protect customers from fraudulent charges and maintain the integrity of credit card transactions.
  • Detecting fraudulent transactions efficiently is crucial for financial institutions to maintain trust and security in their services.
How to run Credit Card Fraud Detection Using Machine Learning

  1. Download the Dataset:

  1. Organize the Files:

  • Place the creditcard.csv file inside the main folder.

  1. Install Required Python Packages:

  • If you haven't installed the necessary packages, you can install them using pip. Run the following command in your terminal or command prompt:
  • pip install pandas numpy scikit-learn matplotlib seaborn
  • pip install notebook

  • Run the Code in Jupyter Notebook:

  • Open Jupyter Notebook.
  • Navigate to the Credit-Card-Fraud-Detection folder.
  • Create a new Jupyter Notebook file or use an existing one.
  • Import the necessary libraries and load the dataset (creditcard.csv).
  • Follow the code provided in the notebook to perform data preprocessing, model training, and evaluation.
  • Run the code cells one by one to execute the code.
  • run this command : python -m notebook

  • Enjoy:

  • Once you have run the code, you should see the results of the model training and evaluation.
  • Feel free to explore the dataset and experiment with different machine learning models and techniques for fraud detection
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credit card fraud detection
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final year students
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