bytebard101/exoplanet_classifier repository overview

⁠The Exoplanet Classifier 🪐

A robust, data-driven Machine Learning tool that classifies whether a given set of transit data corresponds to a confirmed exoplanet, false positive, or candidate.
This version blends the strengths of ensemble learning with extensive preprocessing, imputation, and class balancing, resulting in a more stable and generalizable model.

⁠Table of Contents

• The Exoplanet Classifier 🪐⁠
  • Table of Contents⁠
  • Project Overview⁠
  • Screenshots⁠
  • Tech Stack⁠
  • Getting Started⁠
  • How to Use⁠
  • Project Structure⁠
  • Model Details⁠
    • Architecture⁠
    • Preprocessing Pipeline⁠
    • Features⁠
    • Labels⁠
    • Data Sources⁠
    • Training Workflow⁠
    • Evaluation⁠
  • Acknowledgements⁠
  • Appreciation⁠

⁠Project Overview

NASA’s exoplanet survey missions (Kepler, K2, and others) have generated thousands of data points using the transit method — tracking dips in starlight caused by orbiting planets.
These datasets contain both confirmed exoplanets and false positives, and the aim of this project is to build an AI classifier capable of making preliminary predictions on new candidates.

The classifier runs inside a Flask-powered web interface, allowing anyone — from students to researchers — to enter transit parameters and instantly receive a prediction.

The goal is to provide a scientifically meaningful, intuitive, and educational experience for users interested in exoplanet research.

⁠Screenshots

Landing Page Input Fields Input Fields when filled Output About Page

⁠Tech Stack

• Python 3.11 or above – Core programming language
• Pandas, NumPy – Data processing and numerical computation
• Scikit-learn – Pipeline, scaling, imputation, model stacking, metrics
• XGBoost – Gradient boosting-based sub-model for ensemble
• Imbalanced-learn (SMOTE) – Class balancing for improved fairness
• Flask – Backend web framework
• HTML/CSS/JavaScript – Frontend for the interactive web UI
• Jupyter Notebook – Used as a sandbox (research.ipynb) to experiment with different model architectures, hyperparameters, and feature engineering before finalizing fit.py.

⁠Getting Started

1. Clone the repository

git  clone  https://github.com/ByteBard58/Exoplanet_Classifier
cd  "Exoplanet_Classifier"

1. Install dependencies

pip  install  -r  requirements.txt

1. Run the Flask app

python  app.py

1. Open your browser and go to http://127.0.0.1:5000 to access the web interface.

2. If you want to close the server, press Ctrl + C in the terminal where you have run app.py from.

⁠How to Use

• Press Get Started on the webpage.

• Enter the candidate features in the input fields (values like Orbital Period, Transit Epoch, Transit Depth, etc.).

• Click Predict to run the prediction.

• For more detailed information about each input and other subjects, press LEARN MORE (located at the top).

⁠Project Structure

EXOPLANET_CLASSIFIER/
├── .github/              # Folder for GitHub actions
│
├── data/             
│   ├── k2_data.csv
│   ├── kepler_data.csv
│   └── source.txt
│
├── models/              
│   ├── column_names.pkl    # Not included in the repo
│   ├── info.txt
│   └── pipe.pkl            # Not included in the repo
│
├── screenshots/           
│
├── static/
│   ├── materials/
│   └── script.js
│
├── templates/
│   ├── about.html
│   └── index.html
│
├── .gitignore
├── app.py
├── fit.py
├── LICENSE
├── README.md
├── requirements.txt
└── research.ipynb

⁠Model Details

⁠Architecture

The upgraded classifier uses a stacking ensemble combining multiple base models with a meta-classifier:

• Base Models:
  • RandomForestClassifier
    • n_estimators=1000
    • max_depth=None
    • class_weight="balanced"
  • XGBClassifier
    • n_estimators=1000
    • max_depth=None
    • learning_rate=0.5
• Meta-classifier:
  • LogisticRegression
    • solver="saga"
    • penalty="l2"
    • C=0.1
    • class_weight="balanced"
    • max_iter=5000

The stacking classifier uses 5-fold cross-validation internally and passes original features to the meta-classifier for better learning.

⁠Preprocessing Pipeline

Before feeding data into the model, the following preprocessing steps are applied via a Pipeline:

1. Imputation: SimpleImputer(strategy="mean") to handle missing values.
2. Scaling: StandardScaler to normalize features.
3. Class Balancing: SMOTE (Synthetic Minority Oversampling Technique) to address class imbalance.
4. Model Training: Stacking ensemble as described above.

⁠Features

The model uses 13 transit and orbital-related features, including:

• Orbital period, transit epoch, transit depth
• Planetary radius, semi-major axis, inclination
• Equilibrium temperature, insolation, impact parameter
• Radius ratios, density ratios, duration ratios
• Number of observed transits

⁠Labels

Targets are mapped as follows:

• 0 → FALSE POSITIVE or REFUTED
• 1 → CANDIDATE
• 2 → CONFIRMED

⁠Data Sources

• Kepler Objects of Interest (KOI)⁠
• K2 Planets and Candidates⁠

⁠Training Workflow

• Train/test split: 2/3 training, 1/3 testing with stratification on class labels.
• Pipeline is trained end-to-end in fit.py.
• Hyperparameters and model choices were extensively tested in research.ipynb⁠, which served as a sandbox for experimentation and optimization.
• Final trained pipeline is saved as models/pipe.pkl and column order as models/column_names.pkl.

⁠Evaluation

The model was evaluated on a held-out test set (1/3 of the data, stratified by class).
Here is the classification report:

Overall Metrics:

• Accuracy: 0.75
• Macro Avg: Precision = 0.72, Recall = 0.72, F1-score = 0.72
• Weighted Avg: Precision = 0.74, Recall = 0.75, F1-score = 0.75

This demonstrates that the upgraded stacking classifier maintains strong performance on confirmed and false positive classes, with room for improvement on candidate predictions.
The model balances accuracy, generalization, and class fairness, making it reliable for preliminary exoplanet classification tasks.

⁠Acknowledgements

• NASA Kepler and K2 Mission for providing the training datasets

• Scikit-learn, XGBoost, and Imbalanced-learn teams for exceptional libraries

• Inspiration from data science projects exploring real-world astrophysics datasets

• The scientists who are engaged with exoplanet research. Their problem inspired us to create this project from the ground up

• Ontohin 4b team for the original NASA SAC 2025 version of this project

⁠Appreciation

Thank you for checking out this upgraded version of The Exoplanet Classifier. This repository is a personal continuation of a NASA Space Apps Challenge project — rebuilt with the intent to learn, improve, and explore the depths of real-world astrophysics through Machine Learning.

Have a great day !