Ex:05 Feature Generation

AIM

To read the given data and perform Feature Generation process and save the data to a file.

Explanation

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.

ALGORITHM

STEP 1

Read the given Data

STEP 2

Clean the Data Set using Data Cleaning Process

STEP 3

Apply Feature Generation techniques to all the feature of the data set

STEP 4

Save the data to the file

PROGRAM

Developed by: Dinesh S
Register Number: 212222230033

For Encoding.csv file

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

Data.csv

import pandas as pd
df1 = pd.read_csv("/content/data.csv")
df1.head()
df1['Ord_1'].unique()
from sklearn.preprocessing import LabelEncoder,OrdinalEncoder
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

BMI.csv file

import pandas as pd
df2 = pd.read_csv("/content/bmi.csv")
df2.head()
be = BinaryEncoder()
data2 = be.fit_transform(df2['Gender'])
df2  = pd.concat([df2,data2],axis=1)
df2
df2 = pd.get_dummies(df2, prefix=['Index'] ,columns=['Index'])
df2