ExoProfiler is an unsupervised machine learning pipeline that mines the NASA Exoplanet Archive to uncover hidden structure among distant worlds. Starting from 910 TESS-confirmed planets and 12 physical and orbital features, the pipeline produces two stable, interpretable planetary families — without any predefined labels or categories.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/LauraSilRu/exoplanet-profiler/llms.txt
Use this file to discover all available pages before exploring further.
Quickstart
Set up the environment and run the full pipeline end-to-end in minutes.
Pipeline Overview
Understand the six-stage ML workflow from raw data to cluster profiles.
Preprocessing Notebook
Learn how missing values, outliers, and scaling are handled in notebook 03.
Planetary Families
Explore the two discovered families: Standard Population and Exotic Objects.
How It Works
ExoProfiler follows a reproducible six-stage pipeline that takes raw astronomical measurements and transforms them into scientifically interpretable planetary clusters.Acquire & Understand Data
Download the TESS subset from the NASA Exoplanet Archive and run notebooks
01_data_understanding.ipynb and 02_eda.ipynb to explore the 910-planet dataset with 355 columns.Preprocess
Run
03_preprocessing.ipynb to select 12 candidate features, filter planets with fewer than 8 observed values, apply log1p transforms, RobustScaler, and KNNImputer. Output: 731 clean planets.Reduce Dimensions with PCA
Run
04_pca_and_cluster_profiling.ipynb to compress 12 features into 4 principal components that retain 90.35% of total variance.Key Results
731 Exoplanets
Retained after completeness filtering from 910 TESS-observed planets.
90.35% Variance
Captured in 4 PCA components from 12 original features.
2 Planetary Families
Standard Population (91.24%) and Exotic Objects (8.76%), stable across seeds.
Explore the Documentation
Dataset & Source
NASA Exoplanet Archive TESS subset — feature descriptions and data provenance.
Feature Variables
The 12 planetary and stellar variables used as model inputs.
PCA Component Loadings
How each variable contributes to the four principal components.
Evaluation Metrics
Elbow method, Silhouette scores, and multi-seed stability analysis.
Artifacts & Outputs
All exported CSV, joblib, and JSON files produced by the pipeline.
Limitations
Known data biases, imputation risks, and interpretation caveats.