Feat/added error handling to linear regression

DIRECTORY.md

@@ -243,6 +243,15 @@
     * [Min Heap](data_structures/heap/min_heap.py)
     * [Randomized Heap](data_structures/heap/randomized_heap.py)
     * [Skew Heap](data_structures/heap/skew_heap.py)
+  * Kd Tree
+    * [Build Kdtree](data_structures/kd_tree/build_kdtree.py)
+    * Example
+      * [Example Usage](data_structures/kd_tree/example/example_usage.py)
+      * [Hypercube Points](data_structures/kd_tree/example/hypercube_points.py)
+    * [Kd Node](data_structures/kd_tree/kd_node.py)
+    * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
+    * Tests
+      * [Test Kdtree](data_structures/kd_tree/tests/test_kdtree.py)
   * Linked List
     * [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
     * [Deque Doubly](data_structures/linked_list/deque_doubly.py)
@@ -285,12 +294,6 @@
   * Trie
     * [Radix Tree](data_structures/trie/radix_tree.py)
     * [Trie](data_structures/trie/trie.py)
-  * KD Tree
-    * [KD Tree Node](data_structures/kd_tree/kd_node.py)
-    * [Build KD Tree](data_structures/kd_tree/build_kdtree.py)
-    * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
-    * [Hypercibe Points](data_structures/kd_tree/example/hypercube_points.py)
-    * [Example Usage](data_structures/kd_tree/example/example_usage.py)
 
 ## Digital Image Processing
   * [Change Brightness](digital_image_processing/change_brightness.py)

machine_learning/linear_regression.py

@@ -17,19 +17,27 @@
     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/ADRvsRating.csv",
-        timeout=10,
-    )
-    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
+    try:
+        response = requests.get(
+            "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
+            "master/Week1/ADRvsRating.csv",
+            timeout=10,
+        )
+        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
+
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching the dataset: {e}")
+        return None
+    except Exception as e:
+        print(f"Unexpected error: {e}")
+        return None
 
 
 def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
@@ -113,5 +121,8 @@
         print(f"{theta[0, i]:.5f}")
 
 
+import doctest
+
 if __name__ == "__main__":
     main()
+    doctest.testmod()