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This study introduces data-driven investment strategies for peer-to-peer lending, utilizing machine learning and data analytics. It details the practical application and evaluation of these strategies using real-world data for enhanced financial decision-making.

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Area of Science:

  • Quantitative Finance
  • Data Science
  • Machine Learning

Background:

  • Peer-to-peer (P2P) lending platforms generate vast amounts of data.
  • Traditional investment strategies may not fully leverage this data for optimal returns.
  • There is a need for robust, data-driven approaches to navigate the complexities of P2P lending investments.

Purpose of the Study:

  • To develop and evaluate data-driven investment strategies for P2P lending.
  • To demonstrate the application of machine learning and data analytics in guiding P2P loan investments.
  • To provide a comprehensive, modular framework for instructors teaching data science.

Main Methods:

  • Data acquisition from a P2P lending platform.
  • Development of investment strategies using various data science approaches.
  • Evaluation of predictive performance and real-world strategy effectiveness.
  • Utilizing Python for data cleaning, modeling, and portfolio optimization.

Main Results:

  • Demonstrated the efficacy of machine learning and data analytics in P2P lending.
  • Provided a practical methodology for applying and evaluating data science in finance.
  • Assessed strategy performance beyond mere predictive accuracy using real data.

Conclusions:

  • Data-driven strategies significantly enhance investment decisions in P2P lending.
  • The developed framework offers a flexible and comprehensive resource for data science education.
  • Practical evaluation of investment strategies is crucial for real-world success.