Preprocessing data for a neural network involves preparing raw data into a format that the model can effectively learn from. The process typically includes cleaning, normalizing or scaling, and splitting the data. First, you address missing or inconsistent values—like filling gaps with averages or removing outliers. Next, you convert categorical data (e.g., text labels) into numerical values using techniques like one-hot encoding. Finally, you split the data into training, validation, and test sets to evaluate model performance. These steps ensure the data is structured, consistent, and free of biases that could hinder training.
A critical step is normalizing or scaling numerical features. Neural networks perform best when input values are on a similar scale, as large disparities can slow learning or cause instability. For example, if one feature ranges from 0 to 1 and another from 0 to 10,000, the larger feature might dominate training. Scaling methods like Min-Max (scaling values to a 0-1 range) or Z-score normalization (centering data around zero with a standard deviation of 1) address this. For image data, pixel values (0-255) are often divided by 255 to scale them to 0-1. Text data might require tokenization (converting words to integers) and padding sequences to ensure uniform input lengths. These steps standardize inputs, making gradient descent optimization more efficient.
The final phase involves splitting data and engineering features. A common split is 70% training, 15% validation, and 15% testing. The validation set helps tune hyperparameters, while the test set evaluates generalization. Feature engineering—like creating interaction terms (e.g., multiplying age by income) or extracting date components (day, month) from timestamps—can improve model performance. For time-series data, lag features (e.g., previous day’s sales) might be added. Data augmentation, such as rotating images or adding noise to audio, artificially expands training data. Always ensure transformations are applied to the training set first, then replicated on validation/test sets to avoid data leakage. This structured approach ensures the model learns patterns effectively without overfitting.