What are the most common data preprocessing techniques used in machine learning?

1 Data Cleaning

Data cleaning is the process of identifying and correcting or removing inaccurate, incomplete, inconsistent, or irrelevant data from a dataset. Data cleaning can involve various tasks, such as fixing typos, formatting errors, spelling mistakes, duplicates, or incorrect values. Data cleaning can also involve handling missing values, which are common in real-world data and can affect the performance of machine learning algorithms. Missing values can be handled by deleting the rows or columns that contain them, imputing them with mean, median, mode, or a constant value, or using more advanced methods such as k-nearest neighbors or regression.

2 Data Transformation

Data transformation is the process of modifying the data to make it more suitable for analysis and modeling. Data transformation can involve various tasks, such as scaling, normalization, standardization, encoding, or discretization. Scaling is the process of adjusting the range of the data values to a common scale, such as 0 to 1 or -1 to 1. Normalization is the process of dividing each value by the maximum value in the column, while standardization is the process of subtracting the mean and dividing by the standard deviation of the column. Encoding is the process of converting categorical or textual data into numerical values, such as one-hot encoding or label encoding. Discretization is the process of converting continuous data into discrete bins or intervals, such as equal-width or equal-frequency bins.

3 Data Integration

Data integration is the process of combining data from multiple sources or formats into a single dataset. Data integration can involve various tasks, such as merging, joining, concatenating, or appending data from different tables, files, databases, or APIs. Data integration can also involve resolving data conflicts, such as different attribute names, values, units, or scales. Data integration can help you enrich your dataset with more features, observations, or dimensions, and enable you to perform more comprehensive and diverse analysis and modeling.

4 Data Reduction

Data reduction is the process of reducing the size or complexity of the data without compromising its quality or usefulness. Data reduction can involve various tasks, such as feature selection, feature extraction, dimensionality reduction, or sampling. Feature selection is the process of selecting the most relevant or informative features for the machine learning task, while feature extraction is the process of creating new features from existing ones, such as principal component analysis or linear discriminant analysis. Dimensionality reduction is the process of reducing the number of dimensions or variables in the data, while sampling is the process of selecting a subset of the data that represents the whole population.

5 Data Balancing

Data balancing is the process of adjusting the distribution of the data classes or labels to make them more balanced or proportional. Data balancing can involve various tasks, such as oversampling, undersampling, or synthetic data generation. Oversampling is the process of increasing the number of samples in the minority class, while undersampling is the process of decreasing the number of samples in the majority class. Synthetic data generation is the process of creating new samples in the minority class using techniques such as SMOTE or ADASYN. Data balancing can help you overcome the problem of imbalanced data, which can cause machine learning algorithms to be biased or inaccurate.

6 Data Exploration

Data exploration is the process of examining and understanding the data using descriptive statistics, visualizations, or other methods. Data exploration can involve various tasks, such as calculating summary statistics, such as mean, median, mode, standard deviation, or quartiles, plotting histograms, boxplots, scatterplots, or heatmaps, or testing hypotheses, such as correlation, causation, or significance. Data exploration can help you gain insights, discover patterns, identify outliers, or verify assumptions about the data and the machine learning task.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?