# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

# importing packages
import numpy as np # to perform calculations 
import pandas as pd # to read data
import matplotlib.pyplot as plt # to visualise

# In read_csv() function, we have passed the location to where the file is located at dphi official github page
boston_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/Boston_Housing/Training_set_boston.csv" )

boston_data.head()

X = boston_data.drop('MEDV', axis = 1)    # Input Variables/features
y = boston_data.MEDV      # output variables/features