text_plain_output_1.png
from sklearn.preprocessing import LabelEncoder

import pandas as pd

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

import seaborn as sns

housing = pd.read_csv('../input/california-housing-prices/housing.csv', sep=',', encoding='utf-8')
housing

housing.shape

housing.isnull().sum()

housing.columns

housing.isnull().sum()

from sklearn.preprocessing import LabelEncoder
ocean_le = LabelEncoder()

housing['ocean_proximity'] = ocean_le.fit_transform(housing['ocean_proximity'])

ocean_le.classes_