This repository houses the Credit Card Fraud Detection project, an advanced analytical tool designed to identify and prevent fraudulent transactions in credit card data. Utilizing state-of-the-art machine learning algorithms, this project aims to enhance security measures by accurately distinguishing between legitimate and fraudulent activities.
To clone and run this project, you'll need to have Python installed on your machine. Follow these steps to get your development environment set up:
git clone https://github.com/AvanAvi/credit-card-fraud-detection-backdrop-prototype.git
cd credit-card-fraud-detection-backdrop-prototypeEnsure you have the following libraries installed:
numpypandasmatplotlibseabornsklearnscipy
You can install these packages using pip:
pip install numpy pandas matplotlib seaborn scikit-learn scipyThe dataset used in this project (creditcard.csv) contains transactions made by credit cards, where each transaction is labeled as fraudulent or legitimate. Note: The dataset is a simplified representation for educational purposes.
- Data Preprocessing: Cleansing and preparing the data for analysis.
- Exploratory Data Analysis (EDA): Visualizing and understanding the dataset through histograms and correlation matrices.
- Feature Engineering: Selecting and transforming variables to improve model performance.
- Model Training: Utilizing Isolation Forest and Local Outlier Factor algorithms to identify outliers (fraudulent transactions).
- Evaluation: Assessing the model's performance through accuracy scores and classification reports.
To run the fraud detection analysis, execute the fraud_detection.py script:
python fraud_detection.pyThis will trigger the data preprocessing steps, followed by the EDA, model training, and evaluation phases, outputting the performance metrics of the models.
- Hat tip to anyone whose code was used
- Inspiration
- Coffee and Coffee and Coffee..
- etc.