The goal of this project is to detect fraud related to financial payments using machine learning. To achieve this, we need to create a machine learning model that can classify payments as either fraudulent or non-fraudulent. To build this model, we require a database containing information about financial fraud, which will help us understand the types of transactions that lead to fraud.
- Data Collection: The dataset used for this project is sourced from Kaggle,. This dataset will serve as the foundation for training and evaluating the machine learning model.
- Data Preprocessing: The imported dataset is preprocessed to handle missing values, outliers, and any necessary feature engineering. This step ensures that the data is in a suitable format for training the machine learning models.
- Model Selection: Three classification algorithms, namely logistic regression, K-nearest neighbors (KNN), and decision tree, are implemented to determine which algorithm is most suitable for this specific fraud detection task. Each algorithm is trained and evaluated using appropriate metrics to assess their performance.
- Model Training and Evaluation: The selected algorithms are trained on the preprocessed dataset using appropriate training techniques. The models are then evaluated using validation techniques such as cross-validation and performance metrics such as accuracy, precision, recall, and F1-score to assess their effectiveness in classifying fraudulent and non-fraudulent payments.
- Model Comparison: The performance of the three algorithms is compared based on their evaluation metrics to identify the most suitable algorithm for this particular fraud detection task. The chosen algorithm will be used for further analysis and predictions.