How to use target statistics for categorical features

Target statistics are a powerful tool for data analysis, especially when dealing with categorical features. By calculating statistics for each category in a feature based on the target variable, you can gain insights into how different categories affect the target variable.

Steps to use target statistics for categorical features:

1. Calculate target statistics: Calculate statistics such as mean, median, mode, etc. for the target variable for each category in the categorical feature.
2. Analyze the results: Look for patterns in the statistics to understand how each category influences the target variable.
3. Use the insights: Use the insights gained from the target statistics to make informed decisions in your analysis or modeling process.

Example:

Suppose you have a dataset with a categorical feature “city” and a target variable “sales”. By calculating the average sales for each city, you can see which cities have higher or lower sales on average. This information can be useful for targeting marketing efforts or identifying areas for improvement.

Here’s a simple example using Python:


import pandas as pd
df = pd.DataFrame({'city': ['A', 'B', 'A', 'B'], 'sales': [100, 200, 150, 180]})
df.groupby('city')['sales'].mean()


In this example, we calculate the average sales for each city using the groupby() function in Pandas.

Using target statistics for categorical features can provide valuable insights into your data and help you make better decisions. Make sure to experiment with different statistics and visualization techniques to fully understand the relationship between categorical features and the target variable.

Remember, data analysis is an iterative process, so don’t be afraid to try different approaches and refine your analysis based on the results.