Data Preprocessing in Machine Learning

Data preprocessing is a critical step in building effective machine learning models. It involves transforming raw data into a clean, structured format to improve model performance. Below are key steps and techniques:

1. Data Cleaning

Remove duplicates, correct inconsistencies, and handle noise.

- Use tools like [Pandas](https://pandas.pydata.org/) for handling missing values. - Example: `df.dropna()` to remove rows with missing data.

2. Handling Missing Values

Replace or remove missing data using methods like mean, median, or interpolation.

- For categorical data, consider using `SimpleImputer` from Scikit-learn.

3. Feature Scaling

Normalize or standardize features to a common scale.

- Popular techniques: Min-Max Scaling, Z-Score Normalization. - Example: `StandardScaler()` in Scikit-learn.

4. Feature Encoding

Convert categorical variables into numerical format.

- Methods: One-Hot Encoding, Label Encoding. - Explore more in our [Feature Encoding Tutorial](/en/tutorials/feature-encoding).

5. Data Splitting

Divide data into training, validation, and test sets.

- Use `train_test_split` from Scikit-learn for stratified sampling.

For advanced techniques, check our Data Preprocessing Best Practices guide.