blobcity dataset.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder
import warnings
warnings.filterwarnings("ignore")


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df= pd.read_csv(r"C:\Users\Dhruv Bajaj\Downloads\blobCity\train_u6lujuX_CVtuZ9i.csv")
df.head(20)


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df.isnull().sum()


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df['LoanAmount'].values


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df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)


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df.isnull().sum()


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df['Gender']


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df['Gender']=LabelEncoder().fit_transform(df['Gender'])
df['Gender']


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df['Gender'] = OrdinalEncoder().fit_transform(df['Gender'].values.reshape(-1, 1))
df['Gender']


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df['Gender']=np.where(df['Gender']=='Male',1,0)
df['Gender']


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df[['ApplicantIncome','CoapplicantIncome']]


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df['sum_column'] = df["ApplicantIncome"] + df["CoapplicantIncome"]
df['sum_column']


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from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
print(scaler.fit(df[['ApplicantIncome','CoapplicantIncome']]))


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print(scaler.transform(df[['ApplicantIncome','CoapplicantIncome']]))


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from sklearn.preprocessing import RobustScaler
transformer = RobustScaler().fit(df[['ApplicantIncome','CoapplicantIncome']])
transformer.transform(df[['ApplicantIncome','CoapplicantIncome']])


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from sklearn.preprocessing import MinMaxScaler

scaler =  MinMaxScaler()

print(scaler.fit(df[['ApplicantIncome','CoapplicantIncome']]))
print(scaler.transform(df[['ApplicantIncome','CoapplicantIncome']]))


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df.groupby('Education', as_index=False).sum_column.mean()


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