I am
working on a Master’s thesis in finance, analyzing the predictive power
of machine learning algorithms for corporate credit risk. The core involves building
ML models and interpreting their outputs.
Looking
for:
• Advice on model selection, feature engineering, and performance metrics
• Guidance on translating algorithm outputs into interpretable insights
• Support in structuring methodology, results, and discussion sections
• Feedback on tables, charts, and referencing best practices
Prior
experience with Python, R, or Matlab is preferred. I will share preliminary
datasets and research notes.
Budget
0 Bids
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