text: """
  Linear regression is the most basic type of regression commonly used for
  predictive analysis. The idea is pretty simple: we have a dataset and we have
  features associated with it. Features should be chosen very cautiously
  as they determine how much our model will be able to make future predictions.
  We try to set the weight of these features, over many iterations, so that they best
  fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
  Rating). We try to best fit a line through dataset and estimate the parameters.gi
  and there is also a thing called cost function through which we can measure the accurancy of the hypothesis
  """
  import numpy as np
  import requests
  
  
  def collect_dataset():
      """Collect dataset of CSGO
      The dataset contains ADR vs Rating of a Player
      :return : dataset obtained from the link, as matrix
      """
      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)
      return dataset
  
  
  def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
      """Run steep gradient descent and updates the Feature vector accordingly_
      :param data_x   : contains the dataset
      :param data_y   : contains the output associated with each data-entry
      :param len_data : length of the data_
      :param alpha    : Learning rate of the model
      :param theta    : Feature vector (weight's for our model)
      ;param return    : Updated Feature's, using
                         curr_features - alpha_ * gradient(w.r.t. feature)
      """
      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 theta
  
  
  def sum_of_square_error(data_x, data_y, len_data, theta):
      """Return sum of square error for error calculation
      :param data_x    : contains our dataset
      :param data_y    : contains the output (result vector)
      :param len_data  : len of the dataset
      :param theta     : contains the feature vector
      :return          : sum of square error computed from given feature's
      """
      prod = np.dot(theta, data_x.transpose())
      prod -= data_y.transpose()
      sum_elem = np.sum(np.square(prod))
      error = sum_elem / (2 * len_data)
      return error
  
  
  def run_linear_regression(data_x, data_y):
      """Implement Linear regression over the dataset
      :param data_x  : contains our dataset
      :param data_y  : contains the output (result vector)
      :return        : feature for line of best fit (Feature vector)
      """
      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))
  
      return theta
  
  
  def main():
      """ 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):
          print("%.5f" % (theta[0, i]))
  
  
  if __name__ == "__main__":
      main()
  