Marketing Data Analysis using Python Pandas and Plotly

import numpy as np
from numpy import isnan
import pandas as pd
from sklearn.impute import KNNImputer
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import plotly.graph_objs as go
import plotly.express as px
from scipy.stats import shapiro
from scipy.stats import chi2_contingency
from scipy.stats import chi2
import scipy.stats as stats
from numpy import median
from numpy import std
from IPython.display import Image
import warnings
warnings.filterwarnings('ignore')
dataset = pd.read_csv('marketing_data.csv')
dataset.head()

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	…	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Response	Complain	Country
0	1826	1970	Graduation	Divorced	84,835.00	0	0	6/16/14	0	189	…	6	1	0	0	0	0	0	1	0	SP
1	1	1961	Graduation	Single	57,091.00	0	0	6/15/14	0	464	…	7	5	0	0	0	0	1	1	0	CA
2	10476	1958	Graduation	Married	67,267.00	0	1	5/13/14	0	134	…	5	2	0	0	0	0	0	0	0	US
3	1386	1967	Graduation	Together	32,474.00	1	1	05-11-2014	0	10	…	2	7	0	0	0	0	0	0	0	AUS
4	5371	1989	Graduation	Single	21,474.00	1	0	04-08-2014	0	6	…	2	7	1	0	0	0	0	1	0	SP

dataset.rename(columns = {' Income ' :'Income'}, inplace = True) # Rename the column
dataset['Income'] = dataset['Income'].str.replace(',', '')
dataset['Income'] = dataset['Income'].astype(float)