""" Collect dataset of CSGO

response = requests.get('https://raw.githubusercontent.com/yashLadha/' +

'The_Math_of_Intelligence/master/Week1/ADRvs' +

'Rating.csv')

lines = response.text.splitlines()

data = []

for item in lines:

item = item.split(',')

data.append(item)

data.pop(0) # This is for removing the labels from the list

dataset = np.matrix(data)

len_data, alpha, theta):

""" Run steep gradient descent and updates the Feature vector accordingly_

n = len_data

prod = np.dot(theta, data_x.transpose())

prod -= data_y.transpose()

sum_grad = np.dot(prod, data_x)

theta = theta - (alpha / n) * sum_grad

""" Return sum of square error for error calculation

error = 0.0

prod = np.dot(theta, data_x.transpose())

prod -= data_y.transpose()

sum_elem = np.sum(np.square(prod))

error = sum_elem / (2 * len_data)

""" Implement Linear regression over the dataset

iterations = 100000

alpha = 0.0001550

no_features = data_x.shape[1]

len_data = data_x.shape[0] - 1

theta = np.zeros((1, no_features))

for i in range(0, iterations):

theta = run_steep_gradient_descent(data_x, data_y,

len_data, alpha, theta)

error = sum_of_square_error(data_x, data_y, len_data, theta)

print('At Iteration %d - Error is %.5f ' % (i + 1, error))

""" Driver function """

data = collect_dataset()

len_data = data.shape[0]

data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)

data_y = data[:, -1].astype(float)

theta = run_linear_regression(data_x, data_y)

len_result = theta.shape[1]

print('Resultant Feature vector : ')

for i in range(0, len_result):