This project is focused on using logistic regression, applying learnings from a machine learning course. It aims to develop a predictive model using a Fraudulent Transaction dataset, determining the probability of a transaction being fraudulent or not. The process involves training the model with a specific dataset and evaluating its performance on a separate test set.
- Model Training: Utilizes training and testing datasets to develop the model and assess the performance.
- Performance metric: Accuracy is utilized as the primary metric to evaluate the model effectiveness.
- Model comparison: Conducts a comparison between two generated models, focusing on their predictive accuracy and effectiveness.
This project, a component of a machine learning course, leverages logistic regression to identify fraudulent transactions. It aims to develop a predictive model using the Fraudulent Transactions dataset, determining the likelihood of a transaction being fraudulent. The process involves training the model with a designated dataset and evaluating its performance on a separate test set.
You can see the Jupyter Notebook here or can install it locally using the next steps:
Prerequisite: This project requires Jupyter Notebook to be installed. If you do not have Jupyter Notebook installed, you can find installation instructions at the documentation.
To set up and show the notebook locally, follow these steps:
- Run the following command in your terminal to clone the project repository:
git clone https://github.com/Joel-Milla/Fraud_Detection.git- Navigate to the cloned project directory and launch Jupyter Notebook to access the .ipynb files.
- Run cells sequentially using
Shift + Enter.