sudhar2303 / odd2023-datascience-ex-05

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

CODE AND OUTPUT FOR FEATURE ENCODING AND FEATURE SCALING:

import pandas as pd
import numpy as np

from google.colab import files
upload = files.upload()

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

from category_encoders import BinaryEncoder
e1 = BinaryEncoder()
bn = e1.fit_transform(df['Gender'])
df = pd.concat([df,bn],axis = 1)
df

from sklearn.preprocessing import RobustScaler
rs = RobustScaler()
df[['Height','Weight']] = rs.fit_transform(df[['Height','Weight']])
df

from google.colab import files
upload = files.upload()

df = pd.read_csv("data1.csv")
df

from sklearn.preprocessing import OrdinalEncoder,LabelEncoder,OneHotEncoder
data = ['Very Hot','Hot','Warm','Cold']
e1 = OrdinalEncoder(categories = [data])
df['Ord_1'] = e1.fit_transform(df[['Ord_1']])
df

data1 = ['High School','Diploma','Bachelors','Masters','PhD']
e1 = LabelEncoder()
df['Ord_2'] = e1.fit_transform(df['Ord_2'])
df

e2 = OneHotEncoder(sparse = False)
enc = pd.DataFrame(e2.fit_transform(df[['City']]))
df = pd.get_dummies(df,columns = ['City'])
df

pip install --upgrade category_encoders
from category_encoders import BinaryEncoder
e3 = BinaryEncoder()
bn = e3.fit_transform(df[['bin_1','bin_2']])
df = pd.concat([df,bn],axis = 1)
df

from sklearn.preprocessing import MinMaxScaler
mm = MinMaxScaler()
df[['Ord_1','Ord_2']] = mm.fit_transform(df[['Ord_1','Ord_2']])
df

from google.colab import files
upload = files.upload()

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

from sklearn.preprocessing import LabelEncoder,OneHotEncoder
data1 = ['Hot','Warm','Cold']
e1 = LabelEncoder()
df['ord_2'] = e1.fit_transform(df['ord_2'])
df

e2 = OneHotEncoder(sparse = False)
enc = pd.DataFrame(e2.fit_transform(df[['nom_0']]))
df = pd.get_dummies(df,columns = ['nom_0'])
df```

from category_encoders import BinaryEncoder
e3 = BinaryEncoder()
bn = e3.fit_transform(df[['bin_1','bin_2']])
df = pd.concat([df,bn],axis = 1)
df

from sklearn.preprocessing import  StandardScaler
ss = StandardScaler()
df[['ord_2']] = ss.fit_transform(df[['ord_2']])
df

RESULT:

Feature Encoding process and Feature Scaling process is applied to the given data frame sucessfully.