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import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.feature_selection import mutual_info_regression
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
from sklearn.pipeline import Pipeline
from sklearn.neighbors import KNeighborsRegressor
df = pd.read_csv('../input/fe-course-data/customer.csv')

df.dtypes

y = df['ClaimAmount']
X = df.drop(['Customer', 'ClaimAmount', 'Unnamed: 0'], axis=1, inplace=False).copy()

sns.relplot(x='Income', y='ClaimAmount', data=df)