Delete build_features.py

build_features.py

@@ -1,79 +0,0 @@
-'''
-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 
-    
-
-
-