main.py

import numpy as np
import csv
from collections import namedtuple
from sklearn.linear_model import LinearRegression

Wine = namedtuple('Wine', ['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality'])

def main():
	wines = []
	with open('wines.csv') as f:
		csv_reader = csv.reader(f)
		headers = next(csv_reader)
		for row in csv_reader:
			wine = Wine(*row)
			wines.append(wine)
		qualities = [float(wine.quality) for wine in wines]
		print(sum(qualities)/len(qualities))
		residual_sugars = [float(wine.residual_sugar) for wine in wines]
		fixed_acidity = [float(wine.fixed_acidity) for wine in wines]
		volatile_acidity = [float(wine.volatile_acidity) for wine in wines]
		print(max(residual_sugars))
		print(min(fixed_acidity))
		ids = list(range(len(qualities)+1))
		LR = LinearRegression()
		LR.fit(list(zip(ids, volatile_acidity)), qualities)
		print(LR.coef_)

if __name__ == '__main__':
	main()