Introduction to Data Preprocessing
Data preprocessing is a crucial step in the data science workflow. It involves several steps including data cleaning, transformation, and feature engineering. 💡 These steps help transform raw data into a format that is more suitable and effective for analysis.
Steps in Data Preprocessing
- Data Cleaning: This step involves handling missing values, removing duplicates, and correcting errors in the data.
- Data Transformation: Scaling and normalizing data helps improve the performance of machine learning algorithms.
- Feature Engineering: Creating new features or modifying existing ones can enhance the predictive power of the model.
Tools and Libraries
Popular tools for data preprocessing include:
- Pandas - for data manipulation and analysis.
- NumPy - for large, multi-dimensional arrays and matrices.
- Scikit-learn - for implementing machine learning algorithms.
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