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import pandas as pd

import ppscore as pps

import seaborn as sns

df = pd.read_csv('../input/melbourne-housing-market/Melbourne_housing_FULL.csv')

df.isnull().sum() / len(df) * 100

df_cat = df[['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'CouncilArea', 'Regionname', 'Postcode']]
df_num = df[['Rooms', 'Distance', 'Bedroom2', 'Bathroom', 'Car', 'Landsize', 'BuildingArea', 'YearBuilt', 'Lattitude', 'Longtitude', 'Propertycount']]
df_date = pd.to_datetime(df['Date'])
y = df['Price']

for column in df_num.columns:
    if df_num[column].isna().any() == True:
        df_num.loc[:, column] = df_num[column].fillna(df_num[column].mean())
df_num.loc[:, column] = (df_num[column] - df_num[column].min()) / (df_num[column].max() - df_num[column].min())

df_num.describe().T