Basic workflow:
Use lightkurve api to get the lightcurves corresponding to the TESS ID's we want, use astropy to process and fold the lightcurves.
Use astropy and the MAST api to get further information about the stars.
Run a neural network on TESS lightcurve false positive/confirmed planet data.
Useful links:
https://exofop.ipac.caltech.edu/tess/view_toi.php
https://mast.stsci.edu/api/v0/_services.html
https://mast.stsci.edu/api/v0/_t_i_cfields.html
https://docs.lightkurve.org/api/index.html
https://github.com/spacetelescope/notebooks/tree/master/notebooks/MAST/TESS
https://archive.stsci.edu/tess/
https://www.kaggle.com/keplersmachines/kepler-labelled-time-series-data
https://astropy-timeseries.readthedocs.io/en/latest/timeseries/
https://github.com/gabrielgarza/exoplanet-deep-learning
https://github.com/elopezaguilera/exoplanets
Special thanks to Joe Bender for his help and lending code.