image_output_1.png
import pandas_profiling
import numpy as np
import random as rand
import datetime as dt
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import norm, skew
from scipy.special import boxcox1p
from scipy.stats import boxcox_normmax
from scipy import stats
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import scale
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import RobustScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
import warnings
warnings.filterwarnings(action='ignore')
pd.options.display.max_seq_items = 5000
pd.options.display.max_rows = 5000
flatui = ['#9b59b6', '#3498db', '#95a5a6', '#e74c3c', '#34495e', '#2ecc71']
sns.set_palette(flatui)
sns.palplot(sns.color_palette(flatui))