This project focuses on detecting fraudulent transactions on credit cards using machine learning techniques. The goal is to develop a model that can predict fraud based on specific characteristics present in transaction data.
The dataset used for this project is provided by Kaggle and can be downloaded here after creating an account. It contains transactions made by European cardholders over a period of 2 days in September 2013. The dataset consists of a total of 284,807 transactions, with 492 instances of fraud recorded (0.172% of the total). Dataset Details
- The dataset comprises 31 columns, with 28 columns resulting from a PCA transformation to maintain data privacy and confidentiality.
- It is worth noting that the dataset is free of missing data, which ensures that the analysis can be performed without the need for extensive data preprocessing.
The main objective of this project is to develop a machine learning model that can effectively identify fraudulent credit card transactions. The following steps will be undertaken:
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Exploratory Data Analysis (EDA): We will perform a comprehensive analysis of the dataset to gain insights into its structure, distribution, and relationships between variables. This step will help us understand the characteristics of fraudulent transactions and identify potential patterns.
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Data Preprocessing: As the dataset has already undergone PCA transformation, we will focus on any necessary preprocessing steps such as data normalization, handling imbalanced classes, and feature selection, if required.
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Model Training and Evaluation: We will train and evaluate different machine learning algorithms to identify the most suitable model for fraud detection. Evaluation metrics such as accuracy, precision, recall, and F1-score will be used to assess model performance.
To get started with this project, follow the steps below:
- Sign up for an account on Kaggle (if you haven't already).
- Download the dataset from this link after logging in.
- Clone this GitHub repository to your local machine.
- Set up the required dependencies and environment (including Python, Jupyter Notebook, and necessary libraries).
- Open the Jupyter Notebook file provided in this repository to access the code, data analysis, and model implementation.
Contributions to this project are welcome! If you have any suggestions, enhancements, or bug fixes, please submit a pull request. Let's collaborate to improve fraud detection and make a positive impact in the financial industry. License
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code and resources for educational or commercial purposes.
Please refer to the LICENSE file for more details. Contact
If you have any questions or inquiries regarding this project, please feel free to reach out to [Project Owner Name] at [email address].
Happy coding!