Welcome!
Are you ready to dive into the fascinating world of machine learning? Today, we will explore the crucial steps of data preprocessing.
Why is Preprocessing Important?
Data preprocessing is a critical step in the machine learning workflow. It ensures that your datasets are clean, consistent, and ready for model training. Proper preprocessing can significantly enhance model performance.
Key Preprocessing Techniques:
- Data Cleaning: Detect and correct errors and outliers.
- Data Integration: Combine data from different sources.
- Data Transformation: Normalize and encode data to make it uniform.
- Data Reduction: Reduce dimensionality to streamline datasets.
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