Data Preprocessing Questions
The steps involved in data preprocessing are as follows:
1. Data Cleaning: This step involves handling missing values, dealing with outliers, and correcting any inconsistencies or errors in the data.
2. Data Integration: In this step, data from multiple sources or formats are combined into a single dataset.
3. Data Transformation: This step involves converting the data into a suitable format for analysis. It may include normalization, scaling, or encoding categorical variables.
4. Data Reduction: This step aims to reduce the dimensionality of the dataset by selecting relevant features or applying techniques like principal component analysis (PCA).
5. Data Discretization: If necessary, continuous variables can be converted into discrete intervals or categories.
6. Data Sampling: This step involves selecting a representative subset of the data for analysis, especially in cases where the dataset is large.
7. Data Splitting: The dataset is divided into training, validation, and testing sets to evaluate the performance of the model accurately.
These steps help to ensure that the data is clean, consistent, and suitable for analysis, improving the accuracy and efficiency of machine learning models.