~ SYNOPSIS :
	- THE FOLLOWING PROGRAM IS TO CREATE A WEATHER PREDICTING PYTHON PROGRAM WHICH USES MACHINE LEARNING ALGORITHMS TO DO
	  A PREDICTION BASED ON A TIME SERIES OF OBSERVATIONS ON MEANS OF MONTHS

~ OBJECTIVE : 
	- TO IMPLEMENT AN ALGORITHM THAT CAN PREDICT THE FUTURE VALUES OF THE TEMPERATURE ,
	  WHICH WE WILL USE TO CHECK IF INDIA WILL BE SAFE (HABITABLE) FOR FUTURE GENERATION AND MANKIND TO LIVE.

~ PYTHON VERSION : PYTHON 3.5

~ PYTHON PACKAGES :

	#### USED TO HANDLE THE JSON TYPE DATA
	import json

	#### USED FOR THE NUMPY ARRAY OPERATIONS
	import numpy as np

	#### USED FOR PLOTTING THE OBSERVATIONS INTO A GRAPH
	import matplotlib.pyplot as plt

	#### USED FOR DATAFRAMES CREATION AND HANDLING
	import pandas as pd

	#### USED FOR CREATING A TIME SERIES ON THE DATES
	from datetime import datetime
	
	##### CONVERT THE TIME SERIES INTO LABELED VARIABLES
    	from sklearn.preprocessing import LabelEncoder
    	
	#### SINCE WE ARE USING A SUPERVISED LEARNING MODEL, WE WILL BE 
	#### USING TRAINING DATA AND TESTING DATA SPLITS
	from sklearn.cross_validation import train_test_split
        
	#### USE LINEAR MODEL HERE (FOR NOW TO TEST)
	from sklearn.linear_model import LinearRegression

	# >>>>>>>> CALCULATE THE PERFORMANCES OF YOUR MODEL
	#### MEAN ABSOLUTE ERROR CALCULATION
	from sklearn.metrics import mean_absolute_error

	#### CALCULATE THE MEAN SQUARRED ERROR
	from sklearn.metrics import mean_squared_error

	##### CALCULATE THE R^2 SCORE
	from sklearn.metrics import r2_score


~ MODEL CHECKS : 
	Mean Absolute Error 	=  0.431656091686
	Mean Squarred Error 	=  0.2860703634
	R^2 Score 		= -0.198410493103