Exoplanet Detection ML is a machine learning project dedicated to the detection of exoplanets using transit survey-based light curves. By leveraging advanced machine learning algorithms and feature engineering techniques, this project aims to enhance the accuracy and efficiency of exoplanet discovery. Features Automated Exoplanet Detection: Utilizes transit survey-based light curves to identify potential exoplanets. Advanced Algorithms: Implements state-of-the-art machine learning models for high accuracy. Feature Engineering: Employs robust feature extraction and selection techniques to enhance model performance. Dimensionality Reduction: Reduces feature space complexity while preserving essential information. Machine Learning Algorithms Exoplanet ML employs a variety of machine learning algorithms to ensure comprehensive analysis and accurate predictions: Random Forest Classifier LightGBM AdaBoost Histogram Gradient Boosting XGBoost XGBoost Calibrated Below are some examples of model performance: Model Performance Machine Learning Models Accuracy Precision Sensitivity F1-Score ROC-AUC Score Random Forest 84% 85% 84% 83% 85% Adaptive Boosting 82% 82% 82% 80% 86% Histogram Gradient Boosting 87% 87% 87% 87% 96% Extreme Gradient Boosting 86% 87% 86% 85% 95% Extreme Gradient Boosting (Calibrated) 89% 89% 89% 89% 93% Resources Dimensionality Reduction Introduction to PCA, t-SNE, and UMAP Plotly t-SNE and UMAP Projections Kernel PCA in scikit-learn Understanding UMAP UMAP Documentation TsFresh Feature Selection TsFresh API Documentation TsFresh for Industrial Applications Scikit-Learn Supervised Learning List and Description Scikit-Learn Supervised Learning Gaussian Process Scikit-Learn Gaussian Process Gaussian Process Classifier Gaussian Process Kernels Scikit-Learn Unsupervised Learning List and Description Scikit-Learn Unsupervised Learning Hyperopt Hyperparameter Tuning Hyperopt Getting Started Hyperopt Tutorial on Kaggle Incremental Principal Component Analysis Principal Component Analysis with Python Incremental PCA in scikit-learn Scikit-Learn Plotting Scikit-Learn Display Object Visualization Confusion Matrix Probability Calibration Probability Calibration in scikit-learn Calibrated Classifier Technical Problem Solution and Miscellaneous Links Issue #1280 - YOLOv7 Scikit Optimize Issues Acknowledgements Feature Engineering with TSFresh Exoplanet Archive Acknowledgements Exoplanet Archive DOI Exoplanet Archive Table View Exoplanet Archive Table Redirect License This project is licensed under the CC-BY-4.0. Full Changelog: https://github.com/mirsakhawathossain/Exoplanet-Machine-Learning/commits/1.0.0