https://github.com/mugendesu/credit-card-fraud-detection
Credit card fraud detection using Python Machine Learning
https://github.com/mugendesu/credit-card-fraud-detection
credit-card-fraud-detection documentation google-colab jupyter-notebook logistic-regression machine-learning python python3
Last synced: 8 months ago
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Credit card fraud detection using Python Machine Learning
- Host: GitHub
- URL: https://github.com/mugendesu/credit-card-fraud-detection
- Owner: Mugendesu
- Created: 2025-02-18T21:58:01.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-18T23:22:03.000Z (8 months ago)
- Last Synced: 2025-02-18T23:26:05.544Z (8 months ago)
- Topics: credit-card-fraud-detection, documentation, google-colab, jupyter-notebook, logistic-regression, machine-learning, python, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Credit Card Fraud Detection
## Table of Contents
1. [Project Overview](#1-project-overview)
2. [Dataset](#2-dataset)
3. [Data Preprocessing](#3-data-preprocessing)
4. [Model training](#4-model-training)
5. [Model Evaluation](#5-model-evaluation)
6. [Results and Insights](#6-results)
7. [How to run the project](#7-how-to-run-the-project)
8. [Conclusion](#8-conclusion)
## 1. Project Overview
The Project aims to detect whether a Credit card transaction is Legit or Fraudulent using Machine Learning Techniques in Python.
## 2. Dataset
Download the Dataset here : [Credit card](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/code)
This Dataset contains:
- A large number of transactions with various features.
- As the credit card informations are highly sensitive the features are named as V1,V2...
- A highly unbalanced distribution of Legit vs. fraudulent transactions.
## 3. Data Preprocessing
- Loading the dataset using Pandas.
- Checking for missing values and handling them accordingly.
- As the dataset was highly unbalanced. To balance the ratio, the dataset was split into legit_transactions and fraud_transactions.
- Perform scaling and selection.
- Splitting the dataset into training and testing sets.
## 4. Model Training
- As the data can be classified into legit and fraud we will be using Logistic Regression as a baseline model.
- Other possible models that can be used: Decision Trees, Random Forest, and Neural Networks.
## 5. Model Evaluation
- The performance of the model is assessed using accuracy, precision, recall, and F1-score.
## 6. Results
- The model works perfectly as the training dataset accuracy score is not significantly larger or miniscule than the testing data set.
- The model does not have issue with underfitting or overfitting.
## 7. How to run the Project
1. Install the required dependencies using `pip install -r requirements.txt`.
2. Run the Jupyter Notebook step by step.
3. Evaluate the model’s performance and make necessary modifications.
## 8. Conclusion
This project demonstrates the effectiveness of machine learning techniques in detecting fraudulent transactions.