Data preprocessing is a crucial step in machine learning, preparing data for analysis and modeling. It involves techniques to clean, transform, and scale data to improve algorithm performance and accuracy.
Steps Involved:
Data Cleaning:
- Remove duplicates and errors.
- Impute missing values (e.g., with mean or median).
Feature Scaling:
- Normalize data to bring it to the same scale, improving convergence and accuracy.
Feature Selection:
- Identify and remove irrelevant or redundant features to reduce model complexity and improve performance.
Data Transformation:
- Apply mathematical transformations (e.g., log, square root) to improve linearity or normality.
Example:
Consider a dataset with sales data. We want to build a model to predict sales based on store location, advertising spend, and product price.
Data Preprocessing Techniques:
- Data Cleaning: Remove duplicate entries and impute missing values for advertising spend using the median.
- Feature Scaling: Scale store location (longitude and latitude) by dividing by the maximum value. Scale advertising spend and product price by subtracting the mean and dividing by the standard deviation.
- Feature Selection: Use correlation analysis to identify highly correlated features (e.g., advertising spend and product price) and remove one of them.
- Data Transformation: Apply a log transformation to product price to improve normality.
By applying these preprocessing techniques, we prepare the data for the machine learning algorithm, ensuring optimal performance and accurate predictions.