Data preprocessing is a critical step in the machine learning pipeline. It involves transforming raw data into a clean, structured format that can be effectively used for analysis and modeling. Below are key techniques and best practices:
1. Data Cleaning
- Remove duplicates 🧹
- Handle missing values 📌 (e.g., imputation or deletion)
- Correct inconsistencies ✏️
- Filter irrelevant data 🔍
2. Data Transformation
- Normalization 📈 (scale data to [0,1])
- Standardization 🧪 (center data around mean)
- Encoding categorical variables 🧮 (e.g., one-hot encoding or label encoding)
- Feature scaling ⚖️
3. Data Reduction
- Dimensionality reduction 📊 (e.g., PCA, t-SNE)
- Feature selection 🔍 (remove redundant features)
- Aggregation 🧩 (combine data points)
4. Data Splitting
- Train-test split 📖 (e.g., 80-20 ratio)
- Cross-validation 🔄 (e.g., k-fold)
- Validation set 🧪
For deeper insights into related topics like data visualization, check out our Data Visualization Techniques guide. 📈