<jupyter_start><jupyter_text>Variables<jupyter_code>y = data["Admitted"]
x1 = data["SAT"]<jupyter_output><empty_output><jupyter_text>Scatter Plot<jupyter_code>plt.scatter(x1, y)
plt.xlabel("SAT Scores", fontsize=20)
plt.ylabel("Admitted", fontsize=20)

plt.show()<jupyter_output><empty_output><jupyter_text>Regression<jupyter_code>x = sm.add_constant(x1)
reg_log = sm.Logit(y, x)
results_log = reg_log.fit()<jupyter_output>Optimization terminated successfully.
         Current function value: 0.137766
         Iterations 10<jupyter_text>Logistic regression summary<jupyter_code>results_log.summary()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12995245641838352, 'fraction_numerical': 0.025356576862123614, 'mean_word_length': 9.193548387096774, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, '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': '569169', 'license': 'MIT', 'n_tokens_mistral': 265, 'n_tokens_neox': 248, 'n_words': 28, 'path': 'Logistic Regression.ipynb', 'repo_name': 'dr02651/Classification-Logistic-Regression-', 'size': '631'}