<jupyter_start><jupyter_text><jupyter_code>from google.colab import files
files.upload()
import pandas as pd
df = pd.read_csv('giannis.csv')
print(df.shape)
df.head()
df.tail()
df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index('Date')
df.plot(figsize=(16,9))
df = df.reindex(pd.date_range(start='2015-07-01', end='2021-05-23'))
df.plot(figsize=(16,9));
df['<NAME>'].mean()
df['mean base'] = df['<NAME>'].mean()
df.plot(figsize=(16,9))
df['naive base'] = df['<NAME>'].shift(1).ffill()
df.plot(figsize=(16,9))
df['2019-04': '2019-05'].plot(figsize=(16,9))
import numpy as np
df['Drift Baseline'] = np.nan
df.loc['2015-07-01', 'Drift Baseline'] = df.loc['2015-07-01', '<NAME>']
df.loc['2019-05-22', 'Drift Baseline'] = df.loc['2019-05-22', '<NAME>']
df['Drift Baseline'] = df['Drift Baseline'].interpolate(limit_area='inside')
df.plot(figsize=(16,9));
df = df.reset_index().rename(columns = {'index': 'ds', '<NAME>': 'y'})
from fbprophet import Prophet
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=365)
future.tail()
forecast = m.predict(future)
fig.title = 
fig1 = m.plot(forecast)
fig2 = m.plot_components(forecast)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2363481228668942, 'fraction_numerical': 0.07167235494880546, 'mean_word_length': 6.925675675675675, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, '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': '442663', 'license': 'MIT', 'n_tokens_mistral': 533, 'n_tokens_neox': 476, 'n_words': 79, 'path': 'Mastin_2_1_1_Making_Forecasts_Assignment.ipynb', 'repo_name': 'mmastin/DS-Unit-2-Regression-1', 'size': '1262'}