thirisaa / ex-05-feature-generation

EX-05 Feature generation

AIM

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

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

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

OUPUT

RESULT

Thus feature generation process was performed successfully on the given dataset