Today I was working on Outline on my project and figured out the Outlinbe of my whole project
Label Encoding: In label encoding, each category is assigned a unique numerical value. However, because it assumes an ordinal relationship between the categories, this may not be appropriate for all algorithms. For this reason, Scikit-learn provides the LabelEncoder.
One-Hot Encoding: For each category, this approach generates binary columns. Each category is converted into a binary vector, with 1s in the respective category column and 0s everywhere else. This is more appropriate when there is no intrinsic order in the categories. One-hot encoding is made simple by libraries like pandas.
Dummy Variables: When using one-hot encoding, you may encounter multicollinearity problems, in which one column may be predicted from the others. You can use n-1 columns in this situation and drop one category as a reference.
Frequency Counts: Features can be created based on the frequency of each category. This is beneficial if the number of categories is important information for your analysis.
Target Encoding (Mean Encoding): In some circumstances, the mean of the target variable for that category might be used to substitute categories. This can help with regression difficulties. However, concerns such as target leakage and overfitting must be addressed.
Missing Data: Determine how to handle missing categories. You can construct a separate “missing” category or use methods like mode, median, or a specific value to impute missing values.
Visualization: To show the distribution of categorical data, use plots such as bar charts, histograms, and pie charts. This can help you better grasp the facts.
Statistical Tests: To test for independence between two categorical variables, use statistical tests such as Chi-Square or Fisher’s exact test.
Feature Selection: In machine learning, feature selection strategies may be required to determine the most essential categorical variables for your model.
Decision trees, random forests, and gradient-boosting models can handle categorical data without requiring one-hot encoding. They automatically partition nodes based on categorical features.
Cross-Validation: Use suitable cross-validation approaches when working with categorical data in machine-learning models to avoid data leaking and overfitting.
Handling High cardinality refers to categorical variables having a large number of distinct categories. Techniques such as target encoding, grouping comparable categories, and employing embeddings may be useful in such instances.