To read the given data and perform Feature Generation process and save the data to a file.
Feature Generation (also known as feature construction, feature extraction or feature engineering) is the process of transforming features into new features that better relate to the target.
Read the given Data
Clean the Data Set using Data Cleaning Process
Apply Feature Generation techniques to all the feature of the data set
Save the data to the file
import pandas as pd
from scipy import stats
import numpy as np
df=pd.read_csv("/content/bmi.csv")
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
df[['Height','Weight']]=sc.fit_transform(df[['Height','Weight']])
df.head(10)
import pandas as pd
df=pd.read_csv("/content/Encoding Data (1).csv")
from sklearn.preprocessing import LabelEncoder,OrdinalEncoder
temp=["Cold","Warm","Hot"]
enc=OrdinalEncoder(categories=[temp])
df["ord_2"]=enc.fit_transform(df[["ord_2"]])
df
from sklearn.preprocessing import OneHotEncoder
ohe=OneHotEncoder(sparse=False)
ohe.fit_transform(df[["nom_0"]])
df=pd.read_csv("/content/bmi(1).csv")
from sklearn.preprocessing import MinMaxScaler
mms=MinMaxScaler()
df=pd.DataFrame(mms.fit_transform(df),columns=['Height','Weight','Index'])
df
from sklearn.preprocessing import MaxAbsScaler
mas=MaxAbsScaler()
df=pd.DataFrame(mas.fit_transform(df),columns=['Height','Weight','Index'])
df
from sklearn.preprocessing import RobustScaler
rs=RobustScaler()
df=pd.DataFrame(rs.fit_transform(df),columns=['Height','Weight','Index'])
df
Feature Generation process and Feature Scaling process is applied to the given data frames sucessfully.
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