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This is the last project in the Advanced data analysis nanodegree offered by Udacity.

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dina-adel/Prosper-Loan-Data-Exploration

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Prosper Loan Data Exploration

by Dina El-kholy

Prosper Loan Data

This dataset contains information about 113,937 loans represented by 81 features such as the loan amount, the monthly income, the employment status, and the loan status.

Summary of Findings

  • The amount of loan is affected by the listed category of the loan. Most loans with high loan amount are rejected. However, in some categories, higher loans are completed. I believe this is related to how important is that category. Moreover, higher loans tend to be completed in cases of adoption or medical/dental categories which are important human needs.
  • I investigated whether the employment status also is a factor. I found that high loans are defaulted more often in all employment categories. However, I found that self-employed borrowers tend to request the highest loan amount.

Key Insights for Presentation

  • Relationship between the loan status, the monthly income, and the loan amount.
  • The relation between all the numeric features of interest.

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This is the last project in the Advanced data analysis nanodegree offered by Udacity.

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