<jupyter_start><jupyter_text>Info on dataset: [South German Credit Dataset](https://archive.ics.uci.edu/ml/datasets/South+German+Credit+%28UPDATE%29)<jupyter_code>file = Path('../Resources/german_credit.csv')
# Count of rows with null values

# Delete rows with null values

# Separate the dataset into data and target

# Split into training and testing sets

# Create steps and pipeline

params = {'lr__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
         'lr__solver': ['sag', 'lbfgs']}
# Run GridSearchCV

# Evaluate performance

# Extract best params<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12886597938144329, 'fraction_numerical': 0.03951890034364261, 'mean_word_length': 5.010309278350515, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '59001', 'license': 'ADSL', 'n_tokens_mistral': 221, 'n_tokens_neox': 196, 'n_words': 55, 'path': '01-Lesson-Plans/20-Unsupervised-Machine-Learning/2/Activities/03-Stu_Pipeline/Unsolved/credit.ipynb', 'repo_name': 'anirudhmungre/sneaky-lessons', 'size': '582'}