Upload build_features.py

build_features.py

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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 
+    
+
+
+