Importance of data preprocessing.

Data preprocessing is a critical step in machine learning that involves cleaning and transforming raw data into a suitable format for analysis. It ensures that the data fed into a machine learning model is accurate, consistent, and free from errors, which directly impacts the model’s performance.

Why Data Preprocessing Matters

• Improves Accuracy: Preprocessing eliminates noise, handles missing values, and normalizes data, leading to more accurate and reliable models.
• Enhances Model Efficiency: Clean and well-structured data reduces the complexity of models, making them faster and more efficient.
• Prevents Garbage In, Garbage Out: Without proper preprocessing, models may learn from incorrect or irrelevant data, leading to poor predictions.