<jupyter_start><jupyter_text>Choose your target. Which column in your tabular dataset will you predict?<jupyter_code>target = 'met_adp_rank'<jupyter_output><empty_output><jupyter_text>Is your problem regression or classification?<jupyter_code># classification<jupyter_output><empty_output><jupyter_text>Choose your evaluation metric(s)<jupyter_code># I'll certainly use accuracy, but I may use recall and ROC AUV as well<jupyter_output><empty_output><jupyter_text>How is your target distributed?<jupyter_code># Looks like about 63% actually met/exceed their ADP rank, but the penalty for 
# drafting someone in the minority class (potential bust) is high. 
# May need to consider this when choosing reporting metrics

# Another thing I notice here is that there are many "1s" in the lower portion
# of the draft, which makes sense but is not valuable, and may even 
# weaken the model - I may need to whittle this down a bit more so we're
# working with the most fantasy relevant players

# I may also remove Games and Average as the main relationship I want
# the model to explore is between points rank and adp rank, with the latter
# being informed by Points<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.05877413937867338, 'fraction_numerical': 0.0025188916876574307, 'mean_word_length': 5.890173410404624, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, 'https://': 0, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '115495', 'license': 'MIT', 'n_tokens_mistral': 349, 'n_tokens_neox': 335, 'n_words': 153, 'path': 'module1-define-ml-problems/FF_Unit_2_Build.ipynb', 'repo_name': 'ekselan/DS-Unit-2-Applied-Modeling', 'size': '1191'}