What are some common data transformation techniques and when to use them?

1 Scaling and normalization

Scaling and normalization are techniques that change the range or magnitude of your data, usually to make it comparable or compatible with other data sets or algorithms. Scaling involves multiplying or dividing your data by a constant factor, such as converting units or adjusting for inflation. Normalization involves shifting and rescaling your data to fit a specific interval, such as 0 to 1 or -1 to 1. You can use scaling and normalization when you want to compare data with different units, scales, or ranges, or when you want to apply algorithms that are sensitive to the scale of the data, such as k-means clustering or linear regression.

2 Log and power transformations

Log and power transformations are techniques that change the shape or distribution of your data, usually to make it more symmetrical or linear. Log transformations involve taking the logarithm of your data, which reduces the effect of outliers and compresses the range of large values. Power transformations involve raising your data to a power, which can either increase or decrease the skewness of your data. You can use log and power transformations when you want to stabilize the variance of your data, make it more normal, or improve the fit of linear models.

3 Binning and discretization

Binning and discretization are techniques that change the type or level of your data, usually from continuous to categorical. Binning involves dividing your data into intervals or groups, such as age ranges or income brackets. Discretization involves assigning your data to discrete values or labels, such as yes or no, low or high, or colors. You can use binning and discretization when you want to simplify your data, reduce noise or outliers, or create new features for classification or clustering.

4 Encoding and dummy variables

Encoding and dummy variables are techniques that change the format or representation of your data, usually from categorical to numerical. Encoding involves assigning numerical values to categorical variables, such as 1 for male and 0 for female, or 1 for red, 2 for green, and 3 for blue. Dummy variables involve creating binary variables for each category of a categorical variable, such as 1 for red and 0 for not red, 1 for green and 0 for not green, and 1 for blue and 0 for not blue. You can use encoding and dummy variables when you want to convert your data to a form that can be used by numerical algorithms, such as regression or neural networks.

5 Imputation and deletion

Imputation and deletion are techniques that deal with missing or incomplete data, which can affect the quality and validity of your analysis. Imputation involves filling in the missing values with some reasonable estimates, such as the mean, median, or mode of the data, or using a model to predict the missing values based on other variables. Deletion involves removing the missing values or the entire rows or columns that contain them, which can reduce the size and complexity of your data. You can use imputation and deletion when you want to handle missing data in a way that minimizes the bias or error in your analysis.

6 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?