<jupyter_start><jupyter_text>straight up no idea what these things are<jupyter_code>df_drop = df.drop(columns = 'id')
df_drop.hist(figsize = (20, 20))
corred = df_drop.corr().abs()
s = corred.unstack()
s = s.reset_index()

s = s[s['level_0']=='saleprice'].sort_values(by = 0, ascending = False)#
s.head(10)
X = df_drop[['overallqual', 'grlivarea', 'garagecars', 'garagearea', 'totalbsmtsf']]
y = df['saleprice']
X_train, X_test, y_train, y_test = train_test_split(X, y)
model = LogisticRegression(solver = 'lbfgs',penalty = 'none', max_iter = 42069)
model.fit(X_train, y_train)
X = sm.add_constant(X)

results = sm.OLS(y, X).fit()
results.summary()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17994100294985252, 'fraction_numerical': 0.019174041297935103, 'mean_word_length': 6.895348837209302, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '349595', 'license': 'MIT', 'n_tokens_mistral': 298, 'n_tokens_neox': 283, 'n_words': 53, 'path': 'Solving Regression Problems.ipynb', 'repo_name': 'AsterLaoWhy/Thinkful', 'size': '678'}