Exoplanet_classifier

This repository is to showcase the ML approach to classify exoplanets and generate synthetic data that could be used for several applications such as simulations.

Exoplanet Classification and Synthetic Data Generation

Overview

This project focuses on classifying exoplanets using data from the NASA Exoplanet Archive and synthesizing new realistic data. Two machine learning models, Support Vector Classifier (SVC) and Random Forest Classifier (RFC), were trained and combined using a model fusion technique. Additionally, GaussianCopulaSynthesizer was used to generate synthetic tabular data, and the most realistic data was selected according to the fused model.

Dataset

The dataset used for training the models is the Kepler Objects of Interest (KOI) dataset, which contains data related to potential exoplanet candidates observed by the Kepler space telescope. The dataset includes features like orbital period, planetary radius, and stellar temperature, among others, which are useful for distinguishing between confirmed exoplanets and false positives.

Models

1. Support Vector Classifier (SVC)

   • A classification algorithm effective for high-dimensional spaces and used for both binary and multi-class classification.
   • Achieved an F1 score of approximately 98% on the validation set.

2. Random Forest Classifier (RFC)

   • An ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes for classification.
   • Also achieved an F1 score of around 98% on the validation set.

Model Fusion

To improve classification performance and ensure an even distribution of classifications across exoplanets and non-exoplanets, majority voting was employed to fuse the two models. The fusion approach combines the predictions of both SVC and RFC, and the final output is the class predicted by the majority of the models.

• Achieved an F1 score of %99.5 on a different data set with the ensembling model

Synthetic Data Generation

Using the GaussianCopulaSynthesizer, new synthetic data was generated based on the patterns observed in the original dataset. The synthetic data aimed to replicate the distributions of key features while avoiding overfitting to the specific instances in the training data.

After generating the synthetic data, the fused model was used to evaluate and select the most realistic synthetic data based on the classification confidence and accuracy.