<jupyter_start><jupyter_text>Showcase Capability Team Lead: Data Science & EngineeringAuthor: <NAME><jupyter_code># load libraries

from cryptocast import cryptocast
from matplotlib import pyplot as plt
from pylab import rcParams
import statsmodels.api as sm
# init the class
btc_forecast = cryptocast()

# getting the data
btc_forecast.get_data()

# prep the data
btc_forecast.prep_data()
# taking a look at the data
btc_forecast.data.head(n = 10)
# show all data
btc_forecast.data[['high']].plot(subplots=True, figsize=(20,10))
# check the first day
print(btc_forecast.data[['date']].iloc[0])
print(btc_forecast.data[['high']].iloc[0])
# check the last day
print(btc_forecast.data[['date']].iloc[-2])
print(btc_forecast.data[['high']].iloc[-2])
# forecast the next day
btc_forecast.forecast()
# print outcome
btc_forecast.message<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12775842044134728, 'fraction_numerical': 0.011614401858304297, 'mean_word_length': 6.981481481481482, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, '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': '322748', 'license': 'MIT', 'n_tokens_mistral': 321, 'n_tokens_neox': 314, 'n_words': 74, 'path': 'showcase/showcase.ipynb', 'repo_name': 'LJStroemsdoerfer/applyiverse', 'size': '879'}