Data Preprocessing Questions
The common techniques used for data encoding in data preprocessing are:
1. One-Hot Encoding: This technique is used to convert categorical variables into a binary vector representation. Each category is represented by a binary value (0 or 1) in a separate column, indicating its presence or absence.
2. Label Encoding: Label encoding is used to convert categorical variables into numerical values. Each category is assigned a unique numerical label, allowing algorithms to process the data more effectively.
3. Ordinal Encoding: This technique is similar to label encoding but is specifically used for ordinal variables. It assigns numerical labels to categories based on their order or rank.
4. Binary Encoding: Binary encoding converts categorical variables into binary code. Each category is assigned a unique binary code, which is then split into separate binary columns.
5. Hashing: Hashing is a technique used to convert categorical variables into a fixed-length numerical representation. It uses a hash function to map each category to a unique numerical value.
6. Feature Scaling: Feature scaling is used to normalize numerical variables to a specific range, such as between 0 and 1 or -1 and 1. This ensures that all variables have a similar scale and prevents certain features from dominating the analysis.
These techniques are commonly used in data preprocessing to transform and encode data in a format suitable for machine learning algorithms.