Data preprocessing for machine learning involves preparing raw data to make it suitable for models. The process typically includes handling missing values, encoding categorical data, and scaling features. Without proper preprocessing, models may perform poorly or produce biased results due to inconsistencies in the data.
First, address missing data. Common approaches include removing rows or columns with missing values or imputing them using statistical methods. For example, if a dataset has missing age values, you might fill them with the median age of the existing records. For numerical data, mean or median imputation works well, while categorical data might use the mode (most frequent value). Libraries like pandas in Python simplify this: df.dropna() removes missing values, and df.fillna() replaces them. However, removing data can reduce sample size, so imputation is often preferred unless missing values are excessive. Advanced techniques like K-Nearest Neighbors (KNN) imputation or using models to predict missing values are also options, but they add complexity.
Next, handle categorical data and scale features. Most algorithms require numerical input, so categorical variables (e.g., “red,” “blue”) must be encoded. Label encoding assigns integers (e.g., "red"=0, "blue"=1), but this can imply unintended order. One-hot encoding creates binary columns for each category (e.g., “is_red,” “is_blue”), avoiding ordinal bias. Tools like scikit-learn’s OneHotEncoder or pandas’ get_dummies() automate this. Feature scaling ensures variables with large ranges (e.g., income) don’t dominate those with smaller ranges (e.g., age). Standardization (mean=0, variance=1) using StandardScaler or normalization (scaling to 0-1) with MinMaxScaler are common choices. For example, in k-nearest neighbors algorithms, unscaled data can skew distance calculations.
Finally, split the data into training and testing sets and apply domain-specific transformations. Use train_test_split from scikit-learn to reserve a portion (e.g., 20%) of the data for evaluating model performance. For time-series data, ensure the split respects temporal order to avoid data leakage. Feature engineering, like creating interaction terms (e.g., multiplying age and income) or polynomial features, can improve model performance. Text data might require tokenization or TF-IDF vectorization. Always validate preprocessing steps by testing the model’s performance—if results are inconsistent, revisit the pipeline. Preprocessing is iterative; refine steps based on model feedback and domain knowledge to ensure robustness.