<jupyter_start><jupyter_text>*Q2. A college bookstore must order books two months before each semester starts. They believe that the number of books that will ultimately be sold for any particular course is related to the number of students registered for the course when the books are ordered. From past records, the bookstore obtains the number of students registered, X, and the number of books actually sold for a course, Y, for 12 different semesters. These data are below.*<jupyter_code>#making the dataset

semester = np.arange(1,13)
X= np.array([36,28,35,39,30,30,31,38,36,38,29,26])
y = np.array([31,29,34,35,29,30,30,38,34,33,29,26])
linear_regression(X,y)
data=pd.read_csv("output_filename.csv")
data
print(1)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09866666666666667, 'fraction_numerical': 0.07333333333333333, 'mean_word_length': 6.435643564356436, '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': '392485', 'license': 'Apache-2.0', 'n_tokens_mistral': 270, 'n_tokens_neox': 237, 'n_words': 88, 'path': '.ipynb_checkpoints/Linear_regression-checkpoint.ipynb', 'repo_name': 'inaayush/Data-Analytics', 'size': '750'}