<jupyter_start><jupyter_text>Data Understanding<jupyter_code>media.info()
media['Date'] = pd.to_datetime(media.Date)
media['day_elapsed'] = (media['Date'] - media['Date'][0]).astype('timedelta64[D]')
media.head()
media.info()
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
plt.plot(media['day_elapsed'], media['Views_show'], color='blue')
plt.xlabel('days elapsed')
plt.ylabel('Views Of Show')
plt.xticks(np.arange(4, max(media['day_elapsed'])+1, 7.0))
plt.plot(media['day_elapsed'], media['Ad_impression'], color='red')
plt.xticks(np.arange(4, max(media['day_elapsed'])+1, 7.0))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.209375, 'fraction_numerical': 0.0171875, 'mean_word_length': 10.051724137931034, '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': '53634', 'license': 'MIT', 'n_tokens_mistral': 262, 'n_tokens_neox': 251, 'n_words': 35, 'path': 'Media_Regression.ipynb', 'repo_name': 'aryancodify/LinearRegression', 'size': '640'}