Credit Card Fraud Detection and Analysis

Inspiration

I was inspired by machine learning models to combat fraud, particularly in the financial sector. Wanting to understand how these models worked in this field, I decided on a project using Kaggle data to delve into credit card fraud detection. Despite the availability of more suitable unsupervised algorithms, I decided to try to implement a Random Forest model to extract insights from class classifications.

What it does

The project constructs a Random Forest model to predict if a particular credit card transaction is fraudulent or not.

How I built it

I used Jupyter Notebook (iPython Notebook) in order to write and implement the model.

Challenges I ran into

Even with the diversity of resources, understanding the model's nuances and the dataset posed challenges, such as: navigating hyperparameters and dataset preprocessing demanded meticulous attention. However, the experience deepened my understanding of the data science process and machine learning libraries.

Accomplishments that I'm proud of

I am proud to have learned of the complexities of machine learning implementation and model evaluation. Through intense research in attempting to gain knowledge from these diverse resources, I've gained a better understanding of data preprocessing, model training, and performance evaluation.

What I learned

Through this project, I gained a lot of insight into the data science workflow; from dataset acquisition to model evaluation. Gaining some experience with machine learning libraries has developed a high-level understanding of the Random Forest model. My experience with this project underscores the importance of meticulous research and hands-on experimentation in data science.

Built With

• ipython
• jupyter-notebook
• python