athy125/AUC_Plotter

This project focuses on ROC (Receiver Operating Characteristic) curve analysis and AUC (Area Under the Curve) visualization for evaluating machine learning classifiers. It provides tools to assess classifier performance, offers flexibility for binary and multiclass scenarios, and supports customization of classifier hyperparameters.

AUC Plotter

This project focuses on visualizing the performance of machine learning classifiers by plotting ROC (Receiver Operating Characteristic) curves and calculating the AUC (Area Under the Curve) scores.

Getting Started 🛠️

Prerequisites

Make sure you have the required Python packages installed. You can install them using the following command:

pip install -r requirements.txt

Usage 🚀

To run this project, follow these steps:

1. Clone the repository:

git clone https://github.com/athy125/AUC_Plotter.git
cd auc-plotting-project

2. Install the dependencies:

pip install -r requirements.txt

3. Execute the project:

python main.py --dataset path_to_your_dataset.csv

Replace path_to_your_dataset.csv with the path to your dataset file. You can also specify additional parameters like n_estimators, max_depth, and min_samples_split to fine-tune the Random Forest classifier.

Project Structure 📂

The project is organized as follows:

• main.py: The main entry point of the project that orchestrates the execution.
• data_preparation.py: Contains functions for loading and preprocessing the dataset.
• classifier.py: Includes the train_classifier function for training a Random Forest classifier.
• evaluation.py: Defines the calculate_roc_auc function for calculating ROC curves and AUC.
• plotting.py: Contains the plot_roc_curve function to visualize ROC curves and AUC.
• requirements.txt: Lists the required Python packages and their versions.

Features ⚙️

This project supports features like:

• Handling binary and multiclass classification scenarios.
• Customizing Random Forest hyperparameters.
• Visualizing multiple ROC curves with AUC values.

Contributing 🤝

We welcome contributions to this project! Feel free to open issues, suggest improvements, or submit pull requests.

Acknowledgments 🙏

• Thanks to the open-source community for valuable tools and libraries used in this project.