The document contains various loan details including amounts, interest rates, terms, employment titles, and their states. It also references multiple resources and projects in data science related to Kaggle competitions and astronomical classification. Furthermore, it includes links for further reading on topics such as deep learning and feature engineering.

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loan_amnt annual_inc int_rate term emp_title emp_length home_ownership addr_state last_pymnt_d loan_condition
30000 100,000.00 22.35 36 months Supervisor 5 years MORTGAGE CA Jan-19 Good Loan
40000 45,000.00 16.14 60 months
Assistant to
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8000 55,000.00 6.46 36 months Meat Cutter 10+ years MORTGAGE WA NaN Bad Loan
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7500 50,000.00 12.73 36 months NaN NaN MORTGAGE IN Oct-18
30000 109,000.00 20.89 36 months President 8 years MORTGAGE TX Feb-19
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19. Performance Tiers
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https://www.kaggle.com/progression
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https://www.kaggle.com/c/PLAsTiCC-2018

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https://www.kaggle.com/michaelapers/the-plasticc-astronomy-starter-kit
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https://www.kaggle.com/michaelapers/the-plasticc-astronomy-starter-kit
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Gaussian Process
https://www.kaggle.com/kenmatsu4/feature-engineering-with-gaussian-process

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31. PLAsTiCC Astronomical Classification
https://www.kaggle.com/c/PLAsTiCC-2018/leaderboard

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