This project aims to perform fraud analysis on a credit card dataset using machine learning techniques. The goal is to develop a model that can accurately classify transactions as either fraudulent or legitimate, based on the provided features for the client Abhishek Ganguly.

The main objective of this project is to develop a machine learning model that can accurately classify credit card transactions as either fraudulent or legitimate. By analyzing a credit card dataset, we aim to identify patterns and indicators of fraudulent activity and build a model capable of detecting and flagging potential fraud in real-time.

The dataset used for this analysis is a credit card transaction dataset. It contains a large number of transactions, each labeled as fraudulent or legitimate. The dataset includes various features such as transaction amount, time, and anonymized numerical input variables. This information will be used to train and evaluate the machine learning model.

The dataset can be obtained from source link. Please download the dataset and place it in the project directory.

Drive URL :

URL: https://colab.research.google.com/drive/15ZrMqPc1lTKIb_Nq0wYnc41y0YyszV3F?usp=sharing

To run this project, you need the following dependencies:

• Python 3.x
• Jupyter Notebook or Google Colab

To install the required Python libraries, use the following command:

pip install numpy pandas scikit-learn matplotlib seaborn

• Clone this repository or download the project files to your local machine.
• Open the Jupyter Notebook or Google Colab.
• Upload the notebook file (fraud_analysis.ipynb) to the notebook environment.
• Upload the credit card dataset file (credit_card_dataset.csv) to the notebook environment.
• Run the notebook cells sequentially to execute the code and analyze the dataset.

The project structure is as follows:

• fraud_analysis.ipynb: The main Jupyter Notebook file containing the analysis code.
• credit_card_dataset.csv: The credit card dataset file (to be provided separately).

Open the Jupyter Notebook or Google Colab and execute the cells sequentially. The notebook provides step-by-step instructions on loading the dataset, preprocessing the data, training the machine learning model, and evaluating its performance. It also includes visualizations to help understand the data and the results of the analysis.

Upon running the analysis, you will obtain the following results:

• Evaluation metrics of the machine learning model (accuracy, precision, recall, F1-score, etc.).
• Visualizations such as confusion matrix, ROC curve, and precision-recall curve.
• Insights into the performance of the model and its ability to detect fraudulent transactions.

Fraud analysis using machine learning is a valuable technique for detecting fraudulent activities in credit card transactions. By leveraging the provided dataset and the power of machine learning algorithms, this project aims to build an effective fraud detection model. Feel free to modify the code, experiment with different models, and explore additional techniques to enhance the performance further.

Himanshu Hada [email protected]

MIT