ex-05-feature-generation's Introduction

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

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

from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
data = ["Red","Green","Blue"]
set = OrdinalEncoder(categories = [data])
set.fit_transform(df[["nom_0"]])
df["nom_1"] = set.fit_transform(df[["nom_0"]])
df

data = ["Cold","Warm","Hot"]
st = OrdinalEncoder(categories = [data])
st.fit_transform(df[["ord_2"]])
df["ord_1"] = st.fit_transform(df[["ord_2"]])
df

le = LabelEncoder()
df["sty"]=le.fit_transform(df[["ord_2"]])
df

pd.get_dummies(df,columns=["nom_0"])

df = pd.read_csv("data.csv")
from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
data = ["Cold","Warm","Hot","Very Hot"]
set = OrdinalEncoder(categories = [data])
set.fit_transform(df[["Ord_1"]])
df["nom_1"] = set.fit_transform(df[["Ord_1"]])
df