robsannaa

location_to_state = {'A': 0,
                     'B': 1,
                     'C': 2,
                     'D': 3,
                     'E': 4,
                     'F': 5,
                     'G': 6,
                     'H': 7,
                     'I': 8,
                     'J': 9,

robsannaa

m_lr = Prophet(yearly_seasonality=True, weekly_seasonality=True, seasonality_prior_scale=5)

m_lr.add_seasonality(name='bim', period=90, fourier_order=2)
m_lr.add_seasonality(name='quarter', period=60, fourier_order=2)
m_lr.add_seasonality(name='m', period=30.5, fourier_order=2)

m_lr.add_country_holidays(country_name='IT')
m_lr.fit(df_quantile)

robsannaa

m_seas = Prophet(yearly_seasonality=True, weekly_seasonality=True)
m_seas.add_seasonality(name='bim', period=60, fourier_order=2)
m_seas.add_country_holidays(country_name='IT')

m_seas.fit(df_quantile)

robsannaa

summer_sales = pd.DataFrame({
  'holiday': 'summer_sales',
  'ds': pd.date_range(start='1/07/2018', end='15/08/2018'),
  'lower_window': -5,
  'upper_window': 0,
})

black_friday = pd.DataFrame({
  'holiday': 'black_friday',
  'ds': pd.date_range(start='23/11/2018', end='23/11/2018'),

robsannaa

from fbprophet import Prophet

baseline = Prophet(yearly_seasonality=True, weekly_seasonality=False)
baseline.fit(df_quantile)

robsannaa

Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1

df_quantile = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]

robsannaa

df_clean = convert_dates(df_clean, 'Created at', replace_index=True)

df_clean = df_clean.resample('d').sum()
df_clean['ds'] = df_clean.index.values
df_clean.index = range(0, len(df_clean))
df_clean['ds'] = df_clean.ds.dt.date

df_clean = df_clean.rename(columns={'Total':'y'})

df_clean = df_clean[['ds', 'y']]

robsannaa

def convert_dates(df, date_column, replace_index, time_zone='CET'):
    df[date_column] = pd.to_datetime(df[date_column], utc=True).dt.tz_convert(time_zone)
    if replace_index == False:
        return df
    else:
        df.index = df[date_column]
        return df

robsannaa

import numpy as np
import pandas as pd


def get_quantile_outliers(series):
    outliers_dic = {}

    outliers_list = []

    iqr = series.quantile(0.75) - series.quantile(0.25)

robsannaa

from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0, len(yelp)):
    review = re.sub('[^a-zA-Z]', ' ', yelp['text'].values[i])
    review = review.lower()
    review = review.split()
    ps = PorterStemmer()
    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
    review = ' '.join(review)