#AIImpactOnForex Data Preprocessing for AI Models in Forex Prediction Data preprocessing is a critical first step in building AI models for forex (foreign exchange) prediction. It involves preparing raw forex data — such as exchange rates, volumes, and economic indicators — into a clean and structured format suitable for model training. Key steps include: Data Cleaning: Handling missing values, correcting errors, and removing outliers to ensure data quality. Feature Engineering: Creating informative features like moving averages, RSI, MACD, or other technical indicators that capture market trends. Normalization/Scaling: Rescaling data (e.g., using Min-Max or Z-score normalization) to ensure that different features contribute proportionally to model training. Encoding Categorical Variables: Converting non-numerical data, like economic event categories, into a format the model can process. Data Splitting: Dividing the dataset into training, validation, and testing sets to evaluate model performance reliably. Time-Series Specific Handling: Ensuring that the temporal structure of the data is preserved, such as using sequential train/test splits instead of random ones. Good preprocessing improves model accuracy, reduces overfitting, and enhances the ability to generalize to unseen market conditions.