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

Data.csv

import pandas as pd
df=pd.read_csv("data.csv")
df

from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
from sklearn.preprocessing import OneHotEncoder

oe=OrdinalEncoder()
df1=df.copy()

df1["City"] = oe.fit_transform(df1[["City"]])
df1["bin_1"] = oe.fit_transform(df1[["bin_1"]])
df1["Ord_1"] = oe.fit_transform(df1[["Ord_1"]])
df1["Ord_2"] = oe.fit_transform(df1[["Ord_2"]])
df1["bin_2"] = oe.fit_transform(df1[["bin_2"]])

df2=df.copy()

#feature scaling
from sklearn.preprocessing import MinMaxScaler
sc=MinMaxScaler()
df2=pd.DataFrame(sc.fit_transform(df1),columns=['id', 'bin_1', 'bin_2', 'City', 'Ord_1','Ord_2','Target'])
df2

from sklearn.preprocessing import StandardScaler
sc1=StandardScaler()
df3=pd.DataFrame(sc1.fit_transform(df1),columns=['id', 'bin_1', 'bin_2', 'City', 'Ord_1','Ord_2','Target'])
df3

from sklearn.preprocessing import MaxAbsScaler
sc2=MaxAbsScaler()
df4=pd.DataFrame(sc2.fit_transform(df1),columns=['id', 'bin_1', 'bin_2', 'City', 'Ord_1','Ord_2','Target'])
df4

from sklearn.preprocessing import RobustScaler
sc3=RobustScaler()
df5=pd.DataFrame(sc3.fit_transform(df1),columns=['id', 'bin_1', 'bin_2', 'City', 'Ord_1','Ord_2','Target'])
df5

Encoding.csv

import pandas as pd
qf=pd.read_csv("encoding.csv")
qf

from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
from sklearn.preprocessing import OneHotEncoder

oe=OrdinalEncoder()

qf1=qf.copy()


qf1["bin_1"] = oe.fit_transform(qf1[["bin_1"]])
qf1["nom_0"] = oe.fit_transform(qf1[["nom_0"]])
qf1["ord_2"] = oe.fit_transform(qf1[["ord_2"]])
qf1["bin_2"] = oe.fit_transform(qf1[["bin_2"]])

#feature scaling
from sklearn.preprocessing import MinMaxScaler
sc=MinMaxScaler()
qf0=pd.DataFrame(sc.fit_transform(qf1),columns=['id', 'bin_1', 'bin_2', 'nom_0','ord_2'])
qf0   

from sklearn.preprocessing import StandardScaler
sc1=StandardScaler()
qf2=pd.DataFrame(sc1.fit_transform(qf1),columns=['id', 'bin_1', 'bin_2', 'nom_0','ord_2'])
qf2

from sklearn.preprocessing import MaxAbsScaler
sc2=MaxAbsScaler()
qf3=pd.DataFrame(sc2.fit_transform(qf1),columns=['id', 'bin_1', 'bin_2', 'nom_0','ord_2'])
qf3

from sklearn.preprocessing import RobustScaler
sc3=RobustScaler()
qf4=pd.DataFrame(sc3.fit_transform(qf1),columns=['id', 'bin_1', 'bin_2', 'nom_0','ord_2'])
qf4

Titanic_dataset.csv

import pandas as pd
rf=pd.read_csv("titanic.csv")
rf

#removing unwanted data
rf.drop("Name",axis=1,inplace=True)
rf.drop("Ticket",axis=1,inplace=True)
rf.drop("Cabin",axis=1,inplace=True)  

rf["Age"]=rf["Age"].fillna(rf["Age"].median())
rf["Embarked"]=rf["Embarked"].fillna(rf["Embarked"].mode()[0])

rf.isnull().sum()

rf1=rf.copy()

from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
embark=['S','C','Q']
oe=OrdinalEncoder()

e1=OrdinalEncoder(categories=[embark])
rf1['Embarked'] = e1.fit_transform(rf[['Embarked']])
rf1['Sex'] = oe.fit_transform(rf[['Sex']])
rf1

#feature scaling
from sklearn.preprocessing import MinMaxScaler
sc=MinMaxScaler()
rf0=pd.DataFrame(sc.fit_transform(rf1),columns=['PassengerId', 'Survived', 'Pclass', 'Sex','Age','SibSp','Parch','Fare','Embarked'])
rf0

from sklearn.preprocessing import StandardScaler
sc1=StandardScaler()
rf3=pd.DataFrame(sc1.fit_transform(rf1),columns=['Passenger','Survived','Pclass','Sex','Age','SibSp','Parch','Fare','Embarked'])
rf3

from sklearn.preprocessing import MaxAbsScaler
sc2=MaxAbsScaler()
rf4=pd.DataFrame(sc2.fit_transform(rf1),columns=['Passenger','Survived','Pclass','Sex','Age','SibSp','Parch','Fare','Embarked'])
rf4

from sklearn.preprocessing import RobustScaler
sc3=RobustScaler()
rf5=pd.DataFrame(sc3.fit_transform(rf1),columns=['Passenger','Survived','Pclass','Sex','Age','SibSp','Parch','Fare','Embarked'])
rf5