Prediction of Credit Card fraud

By: Engineer's Planet

A credit card is one of the most used financial products to make online purchases and payments. Though the Credit cards can be a convenient way to manage your finances, they can also be risky. Credit card fraud is the unauthorized use of someone else’s credit card or credit card information to make purchases or withdraw cash. Your focus in this project should be on the following:

Analyze and understand the data to identify patterns, relationships, and trends in the data by using Descriptive Statistics and Visualizations

1. Exploratory Data Analysis:

This might include standardization, handling the missing values and outliers in the data.

2. Data Cleaning:

3. Dealing with Imbalanced data:

This data set is highly imbalanced. The data should be balanced using the appropriate methods before moving onto model building.

4. Feature Engineering:

Create new features or transform the existing features for better performance of the ML Models.

5. Model Selection & Model Training

Choose the most appropriate model that can be used for this project.  Split the data into train & test sets and use the train set to estimate the best model parameters.

6. Model Validation

Evaluate the performance of the model on data that was not used during the training process. The goal is to estimate the model’s ability to generalize to new, unseen data and to identify any issues with the model.

7. Model Deployment:

Model deployment is the process of making a trained machine learning model available for use in a production environment.

In Conclusion, this project delves into the realm of credit card fraud prediction, highlighting the pivotal role of advanced algorithms and data analytics. By leveraging predictive models, this narrative underscores the significance of proactive measures in combating financial fraud, safeguarding both consumers and financial institutions in the digital age.

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