libraries used in WineRatingPredictor-2 notebook

wine_rating_predictor_imports2.py

# manipulation libraries
import pandas as pd
import numpy as np

# visualization libraries
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline
# to display visuals in the notebook

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

# normalization and random-search and error metric
from sklearn.model_selection import RandomizedSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error

# potential machine Learning Models
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
import lightgbm as lgb

# to save machine learning Models
import pickle