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inflation-forecasting

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Inflation-forecasting pipeline that combines ARIMA diagnostics in Stata with LSTM tuning in Python. It covers CPI data preparation, exploratory analysis, model selection, residual checks, and dynamic multi-step forecasts, then benchmarks econometric and deep-learning approaches with MSE, MAE, and R^2 plus clear visuals.

  • Updated Jan 29, 2026
  • Python

To address the impact of rising house prices on the economy, we built a machine learning model resistant to market trends. We experimented with Random Forest and Linear Regression models, employing sophisticated imputation methods like median state price replacement, KNN imputation, and forward/backward filling to minimize errors.

  • Updated Feb 7, 2025
  • Jupyter Notebook

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