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rsvp / fecon235

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Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

python finance statistics time-series jupyter-notebook pandas economics housing econometrics gdp fx gold equities inflation federal-reserve bonds asset-pricing interest-rates employment fecon236
  • Updated Jan 20, 2023
  • Jupyter Notebook

lnsongxf / fecon235

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Computational data tools for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

python finance statistics jupyter-notebook pandas economics housing gdp fx gold equities inflation federal-reserve income bonds asset-pricing interest-rates employment time-series-econometrics
  • Updated May 1, 2017
  • Jupyter Notebook

tanvirnwu / Feature-Selection--Python

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"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

python feature-selection housing feature-engineering constant feature chi-square information-gain chi-square-test pearson-correlation feature-importance pearson-correlation-coefficient housing-dataset features-removal univariance-selection univariance mobile-dataset
  • Updated May 28, 2023
  • Jupyter Notebook

anibalsanchez / answering-house-prices-questions-based-on-advanced-regression-techniques

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Ames Housing Market Analysis: ML & CRISP-DM. Predict Iowa home prices using Kaggle dataset. Apply data science techniques: cleaning, feature engineering, regression modeling. Ideal for aspiring analysts and ML enthusiasts. Includes Jupyter Notebook, blog, visualizations. #DataScience #MachineLearning #RealEstate

ml datascience kaggle-competition housing housing-prices machinelearning crisp-dm
  • Updated Oct 11, 2024
  • HTML

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