Fixing ValueError: This solver needs samples of at least 2 classes in Logistic Regression

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
62/100
Issue type
Documentation
Clarity
Mostly clear
Activity status
Active
Tech stack
python
Domain
documentation

Research direction

The issue provides the question, explanation, and a Python example, but does not name a file or test. Start by checking how this repository stores FAQ entries and whether this answer belongs there; done means the explanation and example are available in the repository’s FAQ format.

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Description

faq-proposal
Course

machine-learning-zoomcamp

Question

When training LogisticRegression on the course_lead_scoring_2026.csv dataset in Module 4, the model.fit(X_train, y_train) step fails with:
ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: np.int64(0)
How can I fix this?

Answer

This error occurs because y_train contains only zeros and is missing the positive class (1). This typically happens due to one of two common data preparation mistakes:

  1. Target overwritten during imputation: The target column (converted) was accidentally included in the numerical features list and overwritten when executing .fillna(0.0) for missing values.
  2. Target deleted too early: The target column was dropped from the DataFrame (e.g., del df_train['converted']) before its values were extracted into y_train.

To resolve this, ensure you explicitly exclude the target column when defining your numerical features:

numerical = [c for c in df.select_dtypes(include=['number']).columns if c != 'converted']

### Checklist

- [x] I have searched existing FAQs and this question is not already answered
- [x] The answer provides accurate, helpful information
- [x] I have included any relevant code examples or links
Dominant language
Python
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8
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20
Avg merge
4d 9h
Merged PRs (30d)
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