fix: descriptive names

Author: Jeel Gajera

machine_learning/ordinary_least_squares_regression.py

@@ -16,7 +16,7 @@
 import numpy as np
 
 
-def ols_regression(x: np.ndarray, y: np.ndarray) -> tuple:
+def ols_regression(x_point: np.ndarray, y_point: np.ndarray) -> tuple:
     """
     Performs Ordinary Least Squares Regression (OLSR) on the given data.
 
@@ -40,16 +40,16 @@ def ols_regression(x: np.ndarray, y: np.ndarray) -> tuple:
 
     # Calculate the mean of the independent variable and
     # the dependent variable.
-    x_mean = np.mean(x)
-    y_mean = np.mean(y)
+    x_mean = np.mean(x_point)
+    y_mean = np.mean(y_point)
 
     # Calculate the slope of the regression line.
-    b = np.sum((x - x_mean) * (y - y_mean)) / np.sum((x - x_mean) ** 2)
+    slope = np.sum((x_point - x_mean) * (y_point - y_mean)) / np.sum((x_point - x_mean) ** 2)
 
     # Calculate the intercept of the regression line.
-    a = y_mean - b * x_mean
+    intercept = y_mean - slope * x_mean
 
-    return a, b
+    return intercept, slope
 
 
 if __name__ == "__main__":
@@ -58,21 +58,21 @@ def ols_regression(x: np.ndarray, y: np.ndarray) -> tuple:
     doctest.testmod()
 
     # Load the data
-    x = np.array([1, 2, 3, 4, 5])
-    y = np.array([2, 4, 6, 8, 10])
+    x_points = np.array([1, 2, 3, 4, 5])
+    y_points = np.array([2, 4, 6, 8, 10])
 
     # Perform OLS regression
-    a, b = ols_regression(x, y)
+    intercept, slope = ols_regression(x_points, y_points)
 
     # Intercept (a) and slope (b) of the regression line
-    print("Intercept:", a)
-    print("Slope:", b)
+    print("Intercept:", intercept)
+    print("Slope:", slope)
 
     # Predict the target variable for a new data point with
     # an independent variable value of 6
     x_new = 6
 
     # Make a prediction
-    y_pred = a + b * x_new
+    y_pred = intercept + slope * x_new
 
     print("Prediction:", y_pred)