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 numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("data.csv")

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

classes = ['Cold','Warm','Hot','Very Hot']

enc = OrdinalEncoder(categories = [classes])

enc.fit_transform(df[["Ord_1"]])

df['ord_1']=enc.fit_transform(df[["Ord_1"]])

df

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("data.csv")

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

classes = [0,1]

enc = OrdinalEncoder(categories = [classes])

enc.fit_transform(df[["Target"]])

df['target']=enc.fit_transform(df[["Target"]])

df

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("data.csv")

!pip install category_encoders

from category_encoders import BinaryEncoder

be=BinaryEncoder()

newdata=be.fit_transform(df['bin_1'])

df1=pd.concat([df,newdata],axis=1)

df1

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("data.csv")

from sklearn.preprocessing import OneHotEncoder

ohe = OneHotEncoder(sparse=False)

df1 = df.copy()

enc = pd.DataFrame(ohe.fit_transform(df1[['City']]))

df1 = pd.concat([df1,enc],axis=1)

df1

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("Encoding Data.csv")

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

classes = ['Red','Blue','Green']

enc = OrdinalEncoder(categories = [classes])

enc.fit_transform(df[["nom_0"]])

df['Nom_0']=enc.fit_transform(df[["nom_0"]])

df

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("Encoding Data.csv")

!pip install category_encoders

from category_encoders import BinaryEncoder

be=BinaryEncoder()

newdata=be.fit_transform(df['bin_1'])

df1=pd.concat([df,newdata],axis=1)

df1

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("titanic_dataset.csv")

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

classes = [1,2,3]

enc = OrdinalEncoder(categories = [classes])

enc.fit_transform(df[["Pclass"]])

df['ord_2']=enc.fit_transform(df[["Pclass"]])

df

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("titanic_dataset.csv")

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

classes = ['C','Q','S',np.nan]

enc = OrdinalEncoder(categories = [classes])

enc.fit_transform(df[["Embarked"]])

df['ord']=enc.fit_transform(df[["Embarked"]])

df

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("titanic_dataset.csv")

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

le = LabelEncoder()

df['Name1']=le.fit_transform(df['Name'])

df

import pandas as pd

import numpy as np

import seaborn as sns

from google.colab import files

uploaded = files.upload()

df = pd.read_csv("titanic_dataset.csv")

!pip install category_encoders

from category_encoders import BinaryEncoder

be=BinaryEncoder()

newdata=be.fit_transform(df['Sex'])

df1=pd.concat([df,newdata],axis=1)

df1

Thus the Feature Generation for the given data set is executed and output was verified successfully