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'''
Author : Rupesh Garsondiya
github : @Rupeshgarsondiya
Organization : L.J university
'''
# Feature Engineering
# import library
import pandas as pd
import numpy as np
import streamlit as st
from sklearn.preprocessing import OneHotEncoder,StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline,make_pipeline
from sklearn.compose import ColumnTransformer
'''create class FeatureEngineering is created to perform feature engineering on the dataset'''
class FeatureEngineering:
def __init__(self): # define constructor
pass
def cleandata(self):
data = pd.read_csv('/home/rupeshgarsondiya/workstation/lab/Project-1/Data/user_behavior_dataset.csv') # load Dataset
data.drop('User ID',axis=1,inplace=True) # Drop user id column it not required
'''Rename column name'''
data.rename(columns={'Device Model':'P_Model','Operating System':'OS','App Usage Time (min/day)':'App_Time(hours/day)',
'Screen On Time (hours/day)':'(hours/Screen_timeday)','Battery Drain (mAh/day)':'Battery_Drain(mAh/day)',
'Number of Apps Installed':'Installed_app','Data Usage (MB/day)':'Data_Usage(GB/day)'},inplace=True)
# App time convert minit into the hours
data['App_Time(hours/day)']=data['App_Time(hours/day)']/60
# convert data use MB into GB
data['Data_Usage(GB/day)']=data['Data_Usage(GB/day)']/1024
return data
def get_clean_data(self):
df = FeatureEngineering().cleandata()
print(df.head())
X = df.drop('User Behavior Class', axis=1)
y = df['User Behavior Class']
x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
categorical_col = ['P_Model','OS','Gender']
categorical_transform = OneHotEncoder()
numerical_col = ['Battery_Drain(mAh/day)']
numerical_transform = StandardScaler()
# use to column transformer to perform onehotencoing and standard scaling
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transform, categorical_col)
],remainder='passthrough')
# create sklearn pipeline
pipeline = Pipeline(steps=[('preprocessor', preprocessor)])
pipeline.fit(x_train)
pipeline.fit(x_test)
x_train_t = pipeline.transform(x_train)
x_test_t = pipeline.transform(x_test)
return x_train_t,x_test_t,y_train,y_test,pipeline
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