#CurrencyPairPrediction Data Normalization Techniques for Currency Features: Data normalization for currency features involves scaling monetary values to ensure consistency and improve model performance. Common techniques include: 1. Min-Max Scaling: Transforms currency values to a fixed range, typically [0, 1], helping models treat different currencies uniformly. 2. Z-score Standardization: Centers currency data by subtracting the mean and dividing by standard deviation, making it suitable for algorithms sensitive to feature variance. 3. Log Transformation: Reduces skewness in highly variable currency data, making distributions more normal-like. 4. Currency Conversion: Converts all values to a common base currency using exchange rates, ensuring comparability across international data. 5. Robust Scaling: Uses median and interquartile range to scale data, effective for currency features with outliers. These techniques help in improving model accuracy, training stability, and comparability across diverse datasets.