Linear regression by python--refer issue no. 487 (#542)

* decision tree

* linear_regression

* Rename Machine_Learning/linear_regression.py to Machine_Learning/Linear_Regression/Linear_Regression.py

Machine_Learning/Linear_Regression/Linear_Regression.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env-python3
+import requests
+import numpy as np
+
+
+def collect_dataset():
+
+    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):
+
+    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):
+
+    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)
+    return error
+
+
+def run_linear_regression(data_x, data_y):
+
+    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():
+
+    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()