The motivation behind choosing this particular project was twofold. Firstly, credit card fraud is a pervasive issue affecting individuals and organizations worldwide. Developing an effective fraud detection system can help safeguard financial transactions and protect users from unauthorized activities. Secondly, this project served as an excellent opportunity for me to gain hands-on experience and practical knowledge in the fields of machine learning and data analysis. This project was first for many things for me. Almost every libraries and their functions. This project really helped me to understand machine learning and python libraries.
I delved into the fundamentals of machine learning, learning about various algorithms, techniques, and concepts. Understanding the theory behind machine learning was essential for building an accurate fraud detection model.
I explored and utilized a wide range of Python libraries, including but not limited to:
Pandas: For data manipulation and analysis.
NumPy: For numerical operations and computations.
Scikit-Learn: For machine learning algorithms and model evaluation.
Matplotlib and Seaborn: For data visualization.
Jupyter Notebooks: For interactive development and documentation.
Data is the cornerstone of any machine learning project. I spent significant time cleaning and preprocessing the dataset, ensuring that it was ready for model training. This involved handling missing values, feature engineering, and data scaling.
I experimented with various machine learning models, fine-tuning their parameters to achieve the best performance. The evaluation process involved metrics such as accuracy, precision, recall, F1-score, and ROC AUC to assess the model's effectiveness in fraud detection.
I would like to express my gratitude to BTKAkademi for their unwavering support and interest throughout this project. Their resources and guidance have been instrumental in my learning journey.