This project forecasts exchange-rate volatility in high-remittance economies by combining macroeconomic indicators with news-based text analysis. Using ML models (logistic, random forest, GBM) and text sentiment, we identify early FX stress signals and show macro factors dominate remittances.
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This project forecasts exchange-rate volatility in high-remittance economies by combining macroeconomic indicators with news-based text analysis. Using ML models (logistic, random forest, GBM) and text sentiment, we identify early FX stress signals and show macro factors dominate remittances.
RahilChadha/ExchangeRate_Volatility
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This project forecasts exchange-rate volatility in high-remittance economies by combining macroeconomic indicators with news-based text analysis. Using ML models (logistic, random forest, GBM) and text sentiment, we identify early FX stress signals and show macro factors dominate remittances.
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