This is a quick project that I made to apply what I learned about data engineering and logistic regression to real-world data. I'm using scikit-learn's LogisticRegression model to help classify discovieries by Kepler as CANDIDATE for an exoplanet or a FALSE POSITIVE discovery.
My dataset was pulled directly from the NASA Exoplanet Archive
Below are the descriptions for each column:
kepid: KepID
kepoi_name: KOI Name
kepler_name: Kepler Name
koi_disposition: Exoplanet Archive Disposition
koi_pdisposition: Disposition Using Kepler Data
koi_score: Disposition Score
koi_fpflag_nt: Not Transit-Like False Positive Flag
koi_fpflag_ss: Stellar Eclipse False Positive Flag
koi_fpflag_co: Centroid Offset False Positive Flag
koi_fpflag_ec: Ephemeris Match Indicates Contamination False Positive Flag
koi_period: Orbital Period [days]
koi_time0bk: Transit Epoch [BKJD]
koi_impact: Impact Parameter
koi_duration: Transit Duration [hrs]
koi_depth: Transit Depth [ppm]
koi_prad: Planetary Radius [Earth radii]
koi_teq: Equilibrium Temperature [K]
koi_insol: Insolation Flux [Earth flux]
koi_model_snr: Transit Signal-to-Noise
koi_tce_plnt_num: TCE Planet Number
koi_tce_delivname: TCE Delivery
koi_steff: Stellar Effective Temperature [K]
koi_slogg: Stellar Surface Gravity [log10(cm/s**2)]
koi_srad: Stellar Radius [Solar radii]
ra: RA [decimal degrees]
dec: Dec [decimal degrees]
koi_kepmag: Kepler-band [mag]
The model had around 95% accuracy on the test data, which is very good. I see this as an absolute win!