Exoplanet Explorer
Completed: 2025-10-06
The Exoplanet Data Dashboard is a data-driven web application built with Flask that enables users to explore and analyze exoplanet discoveries using authentic NASA datasets. It integrates several machine learning algorithms to classify and evaluate exoplanet data for deeper scientific insight. The dashboard provides an intuitive interface for comparing model predictions, analyzing planetary characteristics, and visualizing feature importance across different algorithms. Key Insights: 30 Total Records 8 Data Columns 8 Features Tracked Tracked Features: kepid, kepler_name, gradient boosting, randomforest, xgboost, lightgbm, actual, koi_score Tech Stack: Backend: Flask (Python) Data Source: NASA Exoplanet Archive (CSV datasets) Machine Learning Models: Gradient Boosting, Random Forest, XGBoost, LightGBM Custom Algorithm: TCG (Tuned Classification Gradient) — designed by Leon for optimized performance on small exoplanet datasets Purpose: The Exoplanet Data Dashboard demonstrates how applied machine learning can be used to enhance the understanding of planetary systems beyond our solar system. By integrating multiple models and a custom algorithm, it provides an analytical framework for accurate exoplanet prediction and research visualization.
Saved: 2025-10-19T19:40:34.759372