text_plain_output_1.png image_output_1.png
from sklearn.impute import SimpleImputer

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')

Data.shape

Data.columns

Data.isnull().sum().sum()

Data.corr()

feature_name = list(Data.columns[:-1])

Data.drop('NOX', axis=1, inplace=True)

Data_copy = SimpleImputer().fit_transform(Data)

Data = pd.DataFrame(Data_copy, columns=Data.columns)

Data.isnull().sum().sum()