libraries for wine rating predictor part one

wine_rating_predictor_imports.py

# data manipulation libraries
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', 60)

# data visualization libraries
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.core.pylabtools import figsize

%matplotlib inline
# to display visuals in the notebook

%config InlineBackend.figure_format='retina'
#to enable high resolution plots

# feature extraction and preprocessing
import re
import datetime

# feature transformation and preprocessing
from category_encoders.ordinal import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer