robsannaa
/ KMeans_clustering.ipynb
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January 30, 2020 16:12
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robsannaa
/ stemming.py
Created
February 20, 2019 08:55
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| 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) |
robsannaa
/ outliers.py
Created
March 17, 2019 10:21
Two very simple functions to estimate the number of outliers according to the 3-sigma rule and quantile rule
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| 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
/ convert_dates.py
Created
November 14, 2019 19:40
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| 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
/ preprocessing.py
Created
November 14, 2019 19:45
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| 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']] |
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| 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
/ baseline.py
Created
November 14, 2019 19:58
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| from fbprophet import Prophet | |
| baseline = Prophet(yearly_seasonality=True, weekly_seasonality=False) | |
| baseline.fit(df_quantile) |
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| 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
/ custom_seas.py
Created
November 14, 2019 20:33
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| 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
/ multiple_seas.py
Created
November 14, 2019 20:38
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| 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) |
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