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Merge pull request zaidfarekh#2 from abulyomon/master
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Initial files - ML Session by Yaman
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@mjalajel
mjalajel committed Dec 19, 2015
2 parents 9d5d37c + 069def6 commit 953400d
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Binary file added MachineLearning/Session1/AlWaseet Listing.ods
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73 changes: 73 additions & 0 deletions MachineLearning/Session1/AlWaseet.csv
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PRICE,ORIGIN,MAKE,MODEL,YEAR,MILEAGE,COLOR,HYBRID,DIESEL,4WD,CONVERTIBLE,LEATHER,AUTOTRANSMISSION,AUTOWINDOWS,AC,HEATEDSEATS,CHASSIS_FR,CHASSIS_FL,CHASSIS_RR,CHASSIS_RL
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79 changes: 79 additions & 0 deletions MachineLearning/Session1/regression.py
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#This is meant for an interactive Python shell
#Tested on jupyter
#Created by [email protected] for JOSA's Data Science Bootcamp

#Load the data
import pandas
cars = pandas.read_csv("AlWaseet.csv")
#Take a look
cars.describe()

#Visualize
import matplotlib.pyplot as plt
plt.xlabel("Milage in KM")
plt.ylabel("Price in JOD")
plt.xlim([0,200000])

plt.scatter(cars['MILEAGE'], cars['PRICE'])
plt.show()
#Do they relate?
cars['PRICE'].corr(cars['MILEAGE'])
#FYI
cars['MILEAGE'].corr(cars['PRICE'])
#Let's regress
import statsmodels.formula.api as stats

formula1 = 'PRICE ~ MILEAGE'
model1 = stats.ols(formula1, data = cars).fit()
model1.summary()

#Remove outlier
cars = cars[cars.PRICE < max(cars.PRICE)]
model1v2 = stats.ols(formula1, data = cars).fit()
model1v2.summary()

#Plot again
plt.xlabel("Mileage in KM")
plt.ylabel("Price in JOD")
plt.xlim([0,200000])
plt.scatter(cars['MILEAGE'], cars['PRICE'])
plt.plot(cars['MILEAGE'], model1v2.predict(cars))
plt.show()

#### More variables
import datetime
cars['AGE'] = datetime.date.today().year - cars['YEAR'] #Guess what, ignore the warning!
cars.describe()

plt.xlabel("Age in Years")
plt.ylabel("Price in JOD")
plt.xlim([0,20])
plt.scatter(cars['AGE'], cars['PRICE'])

cars['AGE'].corr(cars['PRICE'])

formula2 = 'PRICE ~ MILEAGE + AGE'
model2 = stats.ols(formula2, data = cars).fit()
model2.summary()

formula3 = 'PRICE ~ MILEAGE + AGE + CHASSIS_FR + CHASSIS_FL \
+ CHASSIS_RR + CHASSIS_RL'
model3 = stats.ols(formula3, data = cars).fit()
model3.summary()

cars['CHASSIS'] = cars['CHASSIS_FR'] + cars['CHASSIS_FL'] + cars['CHASSIS_RR'] \
+ cars['CHASSIS_RL'] #Ok, yes ignore the warning, again!

formula3 = 'PRICE ~ MILEAGE + AGE + CHASSIS'
model3 = stats.ols(formula3, data = cars).fit()
model3.summary()

cars['MILEAGE'].corr(cars['AGE'])
#DAMN!
plt.xlim([0,200000])
plt.scatter(cars['MILEAGE'],cars['AGE'])
plt.show()

formula4 = 'PRICE ~ AGE + CHASSIS'
model4 = stats.ols(formula4, data=cars).fit()
model4.summary()

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