The error you're encountering is due to your dataset containing string or categorical values, and Logistic Regression (or other ML models) generally require numerical input. The error suggests that there's a value 'unknown' in your data, which couldn't be converted to a numeric format. The common ways to handle this situation are: 1. Drop the column: If the column with the string values is not significant, you can drop it. But be cautious, this might lead to a loss of information. 2. Label Encoding: This is used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. 3. One-hot Encoding: It creates new (binary) columns, indicating the presence of each possible value from the original data. Let's assume that all columns with non-numeric data are important and that we want to use one-hot encoding. We can use `pandas.get_dummies()` function to do this: ```python # Convert categorical variables to dummy variables X = pd.get_dummies(X) ``` Then, use the processed `X` for training and testing. However, before applying this, please make sure you understand the impact of one-hot encoding on your model, especially if you have categorical variables with many levels. If you have columns with missing values, they might also be treated as 'unknown'. It's a good practice to handle missing values before training your model. You can fill missing values using methods like forward fill, backward fill, or by taking mean, median or mode. In the pandas library, the `fillna()` function is used for this purpose. But before proceeding, it's a good idea to first identify which columns are causing this issue. You can do this by checking the datatypes of all columns using: ```python print(X.dtypes) ``` This will give you a better idea of how to handle each column.