Data Normalization
Normalization is used to scale data to a specific range (often between 0 and 1 ) to improve the performance and accuracy of machine learning models and data analysis. Here are the main reasons why we use normalization: ✅ 1. To Improve Model Performance Why? Many machine learning algorithms (e.g., linear regression, neural networks) perform better when input features are on a similar scale . Example: If one feature is in the range 0-1000 (e.g., age) and another is 0-1 (e.g., probability), the model may give more importance to larger values. ✅ 2. Faster Convergence in Training Why? Gradient-based algorithms like gradient descent converge faster on normalized data because the cost function surface becomes smoother. Example: In neural networks, if inputs are not normalized, the weights can grow too large and slow down learning. ✅ 3. Preventing Bias in Models Why? Models without normalization may favor larger scales and ignore smaller-scale features, leadi...
