diff --git a/data_structures/binary_tree/basic_binary_tree.py b/data_structures/binary_tree/basic_binary_tree.py
index 65dccf247b51..7695688b4c34 100644
--- a/data_structures/binary_tree/basic_binary_tree.py
+++ b/data_structures/binary_tree/basic_binary_tree.py
@@ -12,89 +12,103 @@ def __init__(self, data: int) -> None:
         self.right: Node | None = None
 
 
-def display(tree: Node | None) -> None:  # In Order traversal of the tree
+class BinaryTree:
     """
-    >>> root = Node(1)
-    >>> root.left = Node(0)
-    >>> root.right = Node(2)
-    >>> display(root)
-    0
-    1
-    2
-    >>> display(root.right)
-    2
+    A BinaryTree has a root node and methods for creating and manipulating binary trees.
     """
-    if tree:
-        display(tree.left)
-        print(tree.data)
-        display(tree.right)
 
-
-def depth_of_tree(tree: Node | None) -> int:
-    """
-    Recursive function that returns the depth of a binary tree.
-
-    >>> root = Node(0)
-    >>> depth_of_tree(root)
-    1
-    >>> root.left = Node(0)
-    >>> depth_of_tree(root)
-    2
-    >>> root.right = Node(0)
-    >>> depth_of_tree(root)
-    2
-    >>> root.left.right = Node(0)
-    >>> depth_of_tree(root)
-    3
-    >>> depth_of_tree(root.left)
-    2
-    """
-    return 1 + max(depth_of_tree(tree.left), depth_of_tree(tree.right)) if tree else 0
-
-
-def is_full_binary_tree(tree: Node) -> bool:
+    def __init__(self, root_data: int) -> None:
+        self.root = Node(root_data)
+
+    def display(self, tree: Node | None) -> None:
+        """
+        In Order traversal of the tree
+        >>> tree = BinaryTree(1)
+        >>> tree.root.left = Node(0)
+        >>> tree.root.right = Node(2)
+        >>> tree.display(tree.root)
+        0
+        1
+        2
+        >>> tree.display(tree.root.right)
+        2
+        """
+        if tree:
+            self.display(tree.left)
+            print(tree.data)
+            self.display(tree.right)
+
+    def depth_of_tree(self, tree: Node | None) -> int:
+        """
+        Recursive function that returns the depth of a binary tree.
+        >>> tree = BinaryTree(0)
+        >>> tree.depth_of_tree(tree.root)
+        1
+        >>> tree.root.left = Node(0)
+        >>> tree.depth_of_tree(tree.root)
+        2
+        >>> tree.root.right = Node(0)
+        >>> tree.depth_of_tree(tree.root)
+        2
+        >>> tree.root.left.right = Node(0)
+        >>> tree.depth_of_tree(tree.root)
+        3
+        >>> tree.depth_of_tree(tree.root.left)
+        2
+        """
+        return (
+            1 + max(self.depth_of_tree(tree.left), self.depth_of_tree(tree.right))
+            if tree
+            else 0
+        )
+
+    def is_full_binary_tree(self, tree: Node) -> bool:
+        """
+        Returns True if this is a full binary tree
+        >>> tree = BinaryTree(0)
+        >>> tree.is_full_binary_tree(tree.root)
+        True
+        >>> tree.root.left = Node(0)
+        >>> tree.is_full_binary_tree(tree.root)
+        False
+        >>> tree.root.right = Node(0)
+        >>> tree.is_full_binary_tree(tree.root)
+        True
+        >>> tree.root.left.left = Node(0)
+        >>> tree.is_full_binary_tree(tree.root)
+        False
+        >>> tree.root.right.right = Node(0)
+        >>> tree.is_full_binary_tree(tree.root)
+        False
+        """
+        if not tree:
+            return True
+        if tree.left and tree.right:
+            return self.is_full_binary_tree(tree.left) and self.is_full_binary_tree(
+                tree.right
+            )
+        else:
+            return not tree.left and not tree.right
+
+
+def main() -> None:
     """
-    Returns True if this is a full binary tree
-
-    >>> root = Node(0)
-    >>> is_full_binary_tree(root)
-    True
-    >>> root.left = Node(0)
-    >>> is_full_binary_tree(root)
-    False
-    >>> root.right = Node(0)
-    >>> is_full_binary_tree(root)
-    True
-    >>> root.left.left = Node(0)
-    >>> is_full_binary_tree(root)
-    False
-    >>> root.right.right = Node(0)
-    >>> is_full_binary_tree(root)
-    False
+    Main function for testing.
     """
-    if not tree:
-        return True
-    if tree.left and tree.right:
-        return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
-    else:
-        return not tree.left and not tree.right
-
-
-def main() -> None:  # Main function for testing.
-    tree = Node(1)
-    tree.left = Node(2)
-    tree.right = Node(3)
-    tree.left.left = Node(4)
-    tree.left.right = Node(5)
-    tree.left.right.left = Node(6)
-    tree.right.left = Node(7)
-    tree.right.left.left = Node(8)
-    tree.right.left.left.right = Node(9)
-
-    print(is_full_binary_tree(tree))
-    print(depth_of_tree(tree))
+    tree = BinaryTree(1)
+    tree.root.left = Node(2)
+    tree.root.right = Node(3)
+    tree.root.left.left = Node(4)
+    tree.root.left.right = Node(5)
+    tree.root.left.right.left = Node(6)
+    tree.root.right.left = Node(7)
+    tree.root.right.left.left = Node(8)
+    tree.root.right.left.left.right = Node(9)
+
+    print(tree.is_full_binary_tree(tree.root))
+    print(tree.depth_of_tree(tree.root))
     print("Tree is: ")
-    display(tree)
+    tree.display(tree.root)
 
 
 if __name__ == "__main__":
diff --git a/dynamic_programming/knapsack.py b/dynamic_programming/knapsack.py
index 489b5ada450a..3010021a3ca9 100644
--- a/dynamic_programming/knapsack.py
+++ b/dynamic_programming/knapsack.py
@@ -7,20 +7,19 @@
 """
 
 
-def mf_knapsack(i, wt, val, j):
+def mf_knapsack(i, wt, val, j, f):
     """
     This code involves the concept of memory functions. Here we solve the subproblems
     which are needed unlike the below example
     F is a 2D array with -1s filled up
     """
-    global f  # a global dp table for knapsack
     if f[i][j] < 0:
         if j < wt[i - 1]:
-            val = mf_knapsack(i - 1, wt, val, j)
+            val = mf_knapsack(i - 1, wt, val, j, f)
         else:
             val = max(
-                mf_knapsack(i - 1, wt, val, j),
-                mf_knapsack(i - 1, wt, val, j - wt[i - 1]) + val[i - 1],
+                mf_knapsack(i - 1, wt, val, j, f),
+                mf_knapsack(i - 1, wt, val, j - wt[i - 1], f) + val[i - 1],
             )
         f[i][j] = val
     return f[i][j]
@@ -36,7 +35,7 @@ def knapsack(w, wt, val, n):
             else:
                 dp[i][w_] = dp[i - 1][w_]
 
-    return dp[n][w_], dp
+    return dp[n][w], dp
 
 
 def knapsack_with_example_solution(w: int, wt: list, val: list):
@@ -91,6 +90,7 @@ def knapsack_with_example_solution(w: int, wt: list, val: list):
             )
             raise TypeError(msg)
 
+    f = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(num_items + 1)]
     optimal_val, dp_table = knapsack(w, wt, val, num_items)
     example_optional_set: set = set()
     _construct_solution(dp_table, wt, num_items, w, example_optional_set)
@@ -137,10 +137,17 @@ def _construct_solution(dp: list, wt: list, i: int, j: int, optimal_set: set):
     wt = [4, 3, 2, 3]
     n = 4
     w = 6
-    f = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     optimal_solution, _ = knapsack(w, wt, val, n)
     print(optimal_solution)
-    print(mf_knapsack(n, wt, val, w))  # switched the n and w
+    print(
+        mf_knapsack(
+            n,
+            wt,
+            val,
+            w,
+            [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)],
+        )
+    )  # switched the n and w
 
     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4