How do you clean and preprocess data for machine learning?

3 Handle missing and duplicate data

Missing and duplicate data can cause bias and inefficiency in your machine learning models, so you must handle them properly. There are different ways to deal with missing and duplicate data, depending on the nature and extent of the problem. For missing data, either remove the columns that contain missing values, or impute them using mean, median, mode, or other methods. For duplicate data, either remove the duplicate columns, or aggregate them using sum or average.

4 Transform and normalize your data

Transforming and normalizing your data means changing the scale, shape, or distribution of your data to make it more suitable for machine learning. For example, you can apply log, square root, or other transformations to reduce skewness or variance in your data. You can also apply normalization or standardization to rescale your data to a common range and standard deviation. These techniques can help improve the performance of your machine learning models, especially for linear and distance-based algorithms.

5 Encode and discretize your data

Encoding and discretizing your data refers to converting the data from one form to another to make it more compatible with your machine learning algorithms. For example, you can encode categorical data into numerical data using methods like one-hot encoding or label encoding. And you can discretize numerical data into categorical data using binning, clustering, or other methods. These techniques assist with reducing the dimensionality and complexity of your data, plus capture the relationships among your features.

6 Split and shuffle your data

Split and shuffle your data into different subsets for training and testing your machine learning models. This will help you avoid overfitting, underfitting, and leakage issues, and evaluate the generalization of your models. Different ratios and methods can help you split your data, depending on the size and characteristics of your data. Also shuffle your data to ensure randomness and diversity in your samples.