diff --git a/python/Bellman-FordAlgorithm.py b/python/Bellman-FordAlgorithm.py
new file mode 100644
index 00000000..cd5b7314
--- /dev/null
+++ b/python/Bellman-FordAlgorithm.py
@@ -0,0 +1,49 @@
+def bellman_ford(vertex_count, edges, source):
+    dist = [float('inf')] * vertex_count
+    pred = [None] * vertex_count
+    dist[source] = 0
+    for _ in range(vertex_count - 1):
+        updated = False
+        for u, v, w in edges:
+            if dist[u] != float('inf') and dist[u] + w < dist[v]:
+                dist[v] = dist[u] + w
+                pred[v] = u
+                updated = True
+        if not updated:
+            break
+    for u, v, w in edges:
+        if dist[u] != float('inf') and dist[u] + w < dist[v]:
+            return None, None
+    return dist, pred
+
+def reconstruct_path(pred, target):
+    path = []
+    while target is not None:
+        path.append(target)
+        target = pred[target]
+    return path[::-1]
+
+if __name__ == "__main__":
+    vertex_count = 5
+    edges = [
+        (0, 1, 6),
+        (0, 2, 7),
+        (1, 2, 8),
+        (1, 3, 5),
+        (1, 4, -4),
+        (2, 3, -3),
+        (2, 4, 9),
+        (3, 1, -2),
+        (4, 3, 7)
+    ]
+    source = 0
+    dist, pred = bellman_ford(vertex_count, edges, source)
+    if dist is None:
+        print("Graph contains a negative-weight cycle")
+    else:
+        for v in range(vertex_count):
+            if dist[v] == float('inf'):
+                print(f"Vertex {v}: unreachable")
+            else:
+                path = reconstruct_path(pred, v)
+                print(f"Vertex {v}: distance = {dist[v]}, path = {path}")
diff --git a/python/knapsak.py b/python/knapsak.py
new file mode 100644
index 00000000..47fe9b14
--- /dev/null
+++ b/python/knapsak.py
@@ -0,0 +1,25 @@
+def fractional_knapsack(values, weights, max_capacity):
+    n = len(values)
+    items = [[values[i], weights[i]] for i in range(n)]
+    items.sort(key=lambda x: x[0] / x[1], reverse=True)
+
+    total_value = 0.0
+    remaining_capacity = max_capacity
+
+    for value, weight in items:
+        if weight <= remaining_capacity:
+            total_value += value
+            remaining_capacity -= weight
+        else:
+            total_value += (value / weight) * remaining_capacity
+            break
+
+    return total_value
+
+
+if __name__ == "__main__":
+    values = [60, 100, 120]
+    weights = [10, 20, 30]
+    max_capacity = 50
+
+    print(fractional_knapsack(values, weights, max_capacity))