To read the given data and perform Feature Generation process and save the data to a file.
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
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
Thus feature generation process was performed successfully on the given dataset