ValueError: Input X contains NaN.

This error occurs when the input data contains missing values or NaN (Not a Number) values. These missing values can occur due to a variety of reasons such as data collection errors, data processing errors, or simply missing data.

To resolve this error, you can try the following steps:

1. Check your dataset for missing values or NaN values using functions such as isna(), isnull(), or any().
2. If you find missing values, try imputing them using techniques such as mean imputation, median imputation, or mode imputation.
3. You can also try removing the rows or columns that contain missing values, depending on the amount of missing data and the importance of those rows or columns in your analysis.
4. Another option is to use machine learning algorithms that can handle missing values, such as tree-based models or deep learning models.

Overall, it is important to carefully check and handle missing values in your dataset to avoid errors such as this ValueError.