Data Pre-Processing

Data preprocessing in machine learning, basic steps in data science, different techniques you should know for data preprocessing

Data Pre-Processing

In machine learning and data science, data preprocessing is a critical step to prepare raw data into a form that models can understand and learn from. While the exact steps depend on the dataset and algorithm, the following are commonly considered mandatory or essential preprocessing techniques:

 

1. Data Cleaning

2. Data Transformation / Feature Scaling

3. Encoding Categorical Variables

4. Feature Selection

5. Dimensionality Reduction

6. Splitting the Dataset

7. Data Type Conversion

8. Handling Imbalanced Data (for classification problems)

9. Time Series Preprocessing