Data Preprocessing Questions
The common techniques used for data transformation in data preprocessing include:
1. Scaling: This technique is used to normalize the data by transforming it to a specific range, such as between 0 and 1 or -1 and 1. It helps in avoiding bias towards certain features with larger values.
2. Encoding: Encoding is used to convert categorical variables into numerical representations that can be easily understood by machine learning algorithms. Common encoding techniques include one-hot encoding, label encoding, and ordinal encoding.
3. Imputation: Imputation is used to handle missing values in the dataset. It involves filling in the missing values with estimated or predicted values based on the available data. Techniques like mean imputation, median imputation, and regression imputation are commonly used.
4. Feature extraction: Feature extraction involves reducing the dimensionality of the dataset by extracting relevant features. Techniques like principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract important features that capture most of the variance in the data.
5. Discretization: Discretization is used to convert continuous variables into categorical variables by dividing them into intervals or bins. It helps in simplifying the data and making it more understandable for certain algorithms.
6. Normalization: Normalization is used to rescale the data to have zero mean and unit variance. It helps in bringing all the features to a similar scale, preventing any particular feature from dominating the analysis.
These techniques are commonly used in data preprocessing to ensure that the data is in a suitable format for analysis and modeling.