# importing required libraries
import pandas as pd
import numpy as np

# obtaining the user data, user can fit its own data in user_data
user_data = pd.read_csv("user_data.csv")

# checking the user_data
print(user_data.head())

# importing pipeline from sklearn.pipeline library
from sklearn.pipeline import Pipeline

# importing standard scaler for feature scaling the features in user_data
from sklearn.preprocessing import StandardScaler

# importing simple imputer for filling the empty places in user_data
from sklearn.impute import SimpleImputer

# fitting the pipeline with standard scaler and simple imputer
my_pipeline = Pipeline({
    ('imputer', SimpleImputer(strategy= "median")),('standard-scaler', StandardScaler()),
})

# fitting the user_data in the timeline
new_data = my_pipeline.fit_transform(user_data)

# checking the new_data
print(new_data.head())