There are several approaches you can use to process categorical data for use in regression problems:

For numerical data, some common preprocessing steps include:

if you are using python for analysis there are packages which you can use as example ; https://scikit-learn.org/stable/modules/preprocessing.html etc

Uditha's answer is excellent. Do note however that even though ordinal encoding is probably easiest to implement, it also has a downside. The machine may understand that certain categories are "higher" than others because their encoded number is higher than the others. Which likely is not your intention. The target encoding may produce a fairer result in such a case. Also, if you have merely a few categories, one-hot encoding is a quality option too.

If you have a mix of categorical and numerical data in your dataset and you wish to use the categorical data as a feature in a regression problem, you will need to preprocess the data to convert the categorical data into a numerical form that can be used by the machine learning model. There are several techniques you can use to do this, including one-hot encoding, label encoding, target encoding, and dummy coding.

One-hot encoding is a technique that creates a separate binary column for each category in the data. For example, if a categorical variable has three categories (A, B, and C), three new columns will be created, with each column representing one of the categories. A value of 1 in a column indicates that the sample belongs to that category, while a value of 0 indicates that it does not. One-hot encoding can be useful when the categories are not ordinal (i.e., there is no inherent order to the categories).

Label encoding is a technique that assigns a unique integer value to each category. For example, if a categorical variable has three categories (A, B, and C), they could be encoded as 0, 1, and 2, respectively. Label encoding can be useful when the categories are ordinal (i.e., there is an inherent order to the categories).

Target encoding is a technique that converts categorical variables into continuous variables by replacing each category with the mean target value for that category. For example, if a categorical variable has three categories (A, B, and C), and the target variable is a continuous variable, the mean target value for category A could be calculated as the average of all the target values for samples belonging to category A. Target encoding can be useful when there is a strong relationship between the categorical variable and the target variable.

Dummy coding is similar to one-hot encoding, but instead of creating a separate binary column for each category, it creates a column for each category except for one. The category that is left out is used as a reference category and the other columns are used to represent the difference between the categories and the reference category. Dummy coding is useful when there are a large number of categories in the data.

I hope this helps! Let me know if you have any questions.

You may use Naive Bayes Classifier

• Use the following libraries for one-hot encoding:
• Pandas' get_dummies
• Scikit-learn preprocessing
• Python’s category_encoding library

You can use CatBoost ML model also without encoding.

CatBoost stand for Categorical Boosting is an alternative to XGBoost. It has special features summarize as follows:

• Can handle categorical features directly without encoding
• Run faster than XGBoost
• Has simpler hyperparameter tuning process