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 import seaborn as sns from sklearn.preprocessing import LabelEncoder,OrdinalEncoder df = pd.read_excel("Workshop_feature Engg.csv.xlsx") carbody=['convertible','wagon','hatchback','sedan','hardtop'] enc=OrdinalEncoder(categories=[carbody]) enc.fit_transform(df[['carbody']]) df['Carbody']=enc.fit_transform(df[['carbody']]) df fuelsystem=['mpfi','mfi','1bbl','2bbl','idi'] enc=OrdinalEncoder(categories=[fuelsystem]) enc.fit_transform(df[['fuelsystem']]) df['Fuelsystem']=enc.fit_transform(df[['fuelsystem']]) df enginetype=['dohc','ohc','ohcv'] enc=OrdinalEncoder(categories=[enginetype]) enc.fit_transform(df[['enginetype']]) df['Enginetype']=enc.fit_transform(df[['enginetype']]) df

import pandas as pd import seaborn as sns from sklearn.preprocessing import LabelEncoder,OrdinalEncoder from google.colab import files uploaded = files.upload() df = pd.read_excel("Workshop_feature Engg.csv.xlsx") le=LabelEncoder() df['Carname']=le.fit_transform(df['CarName']) df le=LabelEncoder() df['Carbody']=le.fit_transform(df['carbody']) df le=LabelEncoder() df['CarID']=le.fit_transform(df['car_ID']) df le=LabelEncoder() df['Carwidth']=le.fit_transform(df['carwidth']) df le=LabelEncoder() df['Enginesize']=le.fit_transform(df['enginesize']) df

import pandas as pd import seaborn as sns from sklearn.preprocessing import OneHotEncoder from google.colab import files uploaded = files.upload() df = pd.read_excel("Workshop_feature Engg.csv.xlsx") ohe=OneHotEncoder(sparse=False) enc=pd.DataFrame(ohe.fit_transform(df[['enginetype']])) df=pd.concat([df,enc],axis=1) df ohe=OneHotEncoder(sparse=False) enc=pd.DataFrame(ohe.fit_transform(df[['fuelsystem']])) df=pd.concat([df,enc],axis=1) df ohe=OneHotEncoder(sparse=False) enc=pd.DataFrame(ohe.fit_transform(df[['carbody']])) df=pd.concat([df,enc],axis=1) df

import pandas as pd import seaborn as sns !pip install category_encoders from category_encoders import BinaryEncoder from google.colab import files uploaded = files.upload() df = pd.read_excel("Workshop_feature Engg.csv.xlsx") be=BinaryEncoder() newdata=be.fit_transform(df['drivewheel']) df1=pd.concat([df,newdata],axis=1) df1 be=BinaryEncoder() newdata=be.fit_transform(df['fueltype']) df1=pd.concat([df,newdata],axis=1) df1 be=BinaryEncoder() newdata=be.fit_transform(df['aspiration']) df1=pd.concat([df,newdata],axis=1) df1 be=BinaryEncoder() newdata=be.fit_transform(df['doornumber']) df1=pd.concat([df,newdata],axis=1) df1 be=BinaryEncoder() newdata=be.fit_transform(df['drivewheel']) df1=pd.concat([df,newdata],axis=1) df1

Thus the Feature Generation process was performed and output was verified successfully.