What are the best practices for data preprocessing?

What are the best practices for data preprocessing?

Data preprocessing is crucial for effective machine learning. Key practices include handling missing values, normalizing data, and encoding categorical variables.

How do I handle missing values in my dataset?

You can handle missing values by removing them, imputing with mean/median/mode, or using algorithms that support missing values.

What normalization techniques should I use?

Common normalization techniques include Min-Max scaling and Z-score standardization. The choice depends on your data distribution.

Should I scale my features before training a model?

Yes, scaling features is important, especially for algorithms sensitive to the scale of data, like SVM or K-means.

Can you explain one-hot encoding?

One-hot encoding converts categorical variables into a binary matrix, allowing algorithms to interpret them effectively.