This notebook explores fraud detection using machine learning techniques, primarily for educational purposes. The main objective is to build models that can accurately detect fraudulent credit card transactions. We will examine different classification algorithms like Logistic Regression and Random Forest Classifier, as well as ensemble methods like bagging, boosting, and stacking, to evaluate their performance for this task.
Contents:
Data Exploration: An initial exploration of the dataset is conducted to understand its structure, features, and distribution.
Data Preprocessing: Preprocessing steps, such as handling missing values, feature scaling, and encoding categorical variables, are performed to prepare the data for modeling.
Model Development: Various machine learning algorithms are trained and evaluated on the dataset to identify the most suitable model for fraud detection.
Model Evaluation: The performance of each model is assessed using relevant evaluation metrics, such as accuracy, precision, recall, and F1-score.
Model Optimization: Techniques for model optimization, including hyperparameter tuning and feature selection, are explored to enhance model performance further.
Results Interpretation: Insights and interpretations derived from the model outputs are provided to aid in understanding the effectiveness and limitations of the developed models.