ml.py

#!/usr/bin/env python
# coding: utf-8

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import matplotlib.pyplot as plt
import pylab as pl
import numpy as np #for matrix ...
import pandas as pd #for reading ...
#get_ipython().run_line_magic('matplotlib', 'inline #this is for nootBook and datas')


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allData = pd.read_csv("C:/Users/ASUS.PIESC/Downloads/FuelConsumption.csv",encoding = "ISO-8859-1",low_memory=False)
allData.head()#take a simple look on file


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allData.describe()


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simpleRow = allData[{'EngineSize','Cylinders','Transmission','Fuel','Smog','CO2'}]


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simpleRow.head(10) #take a simple view of list


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visualTable = allData[['EngineSize','Cylinders','Transmission','Fuel']]
visualTable.hist()
plt.show()


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plt.scatter(simpleRow.EngineSize, simpleRow.Cylinders , color='blue')
plt.xlabel("EngineSize")
plt.ylabel("Cylinders")
plt.show()


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TestPart = np.random.rand(len(allData)) < 0,8 


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train = simpleRow[TestPart]
test = simpleRow[~TestPart]


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