<jupyter_start><jupyter_text>Exercise with bank marketing data  Introduction- Data from the UCI Machine Learning Repository: [data](https://github.com/justmarkham/DAT8/blob/master/data/bank-additional.csv), [data dictionary](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)- **Goal:** Predict whether a customer will purchase a bank product marketed over the phone- `bank-additional.csv` is already in our repo, so there is no need to download the data from the UCI website  Step 1: Read the data into Pandas<jupyter_code>import pandas as pd
url = 'data/bank-additional.csv'
bank = pd.read_csv(url, sep=';')
bank.head()<jupyter_output><empty_output><jupyter_text>Step 2: Prepare at least three features- Include both numeric and categorical features- Choose features that you think might be related to the response (based on intuition or exploration)- Think about how to handle missing values (encoded as "unknown")<jupyter_code># list all columns (for reference)
bank.columns<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0947680157946693, 'fraction_numerical': 0.0029615004935834156, 'mean_word_length': 5.945205479452055, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '105088', 'license': 'MIT', 'n_tokens_mistral': 298, 'n_tokens_neox': 285, 'n_words': 109, 'path': 'Workshop_Exercise.ipynb', 'repo_name': 'gsvijayraajaa/workshop', 'size': '1013'}