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

NAME: DHIVYAPRIYA. R

REG.NO: 212222230032

import pandas as pd

df=pd.read_csv('/content/Encoding Data.csv')

df.head()

df['ord_2'].unique()

from sklearn.preprocessing import LabelEncoder,OrdinalEncoder

climate = ['Cold','Warm','Hot']

en= OrdinalEncoder(categories = [climate])

df['ord_2']=en.fit_transform(df[["ord_2"]])

df

le = LabelEncoder()

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

df

!pip install --upgrade category_encoders

from category_encoders import BinaryEncoder

be = BinaryEncoder()

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

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

df

be = BinaryEncoder()

data = be.fit_transform(df['bin_2'])

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

df

df1 = pd.read_csv("/content/data.csv")

df1.head()

df1['Ord_1'].unique()

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

en= OrdinalEncoder(categories = [climate])

df1['Ord_1']=en.fit_transform(df1[["Ord_1"]])

df1

df1['Ord_2'].unique()

cl = ['High School','Diploma','Bachelors','Masters','PhD']

en= OrdinalEncoder(categories = [cl])

df1['Ord_2']=en.fit_transform(df1[["Ord_2"]])

df1

le = LabelEncoder()

df1['City'] = le.fit_transform(df1[["City"]])

df1

from category_encoders import BinaryEncoder

be = BinaryEncoder()

dat = be.fit_transform(df1['bin_1'])

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

df1

from category_encoders import BinaryEncoder

be = BinaryEncoder()

data1 = be.fit_transform(df1['bin_2'])

df1 = pd.concat([df1,data1],axis=1)

df1

df2 = pd.read_csv("/content/titanic_dataset.csv")

df2.head()

be = BinaryEncoder()

data2 = be.fit_transform(df2['Sex'])

df2 = pd.concat([df2,data2],axis=1)

df2

df2 = pd.get_dummies(df2, prefix=['Embarked'] ,columns=['Embarked'])

df2

RESULT

Thus the Feature Generation process is performed.