diff --git a/backtracking/knapsack01 b/backtracking/knapsack01
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+# Function to solve 0/1 Knapsack problem with backtracking 
+def knapsack_backtracking(weights,values,capacity,n): 
+    stack=[(0,0,0,[])] 
+    best_value=0 
+    best_items=[] 
+    while stack: 
+        current_index,current_weight,current_value,included_items=stack.pop() 
+        if current_index==n: 
+            if current_value>best_value: 
+                best_value=current_value 
+                best_items=included_items[:] 
+            continue 
+        stack.append((current_index+1,current_weight,current_value,included_items[:]))
+ if current_weight+weights[current_index]<=capacity: 
+            new_list=included_items[:] 
+            new_list.append(current_index) 
+            stack.append((current_index+1,current_weight+weights[current_index],current_value+values
+ [current_index],new_list)) 
+    print("Items included (0-indexed):", best_items) 
+    return best_value 
+n=int(input('Enter number of items:')) 
+weights=[] 
+values=[] 
+for i in range(n): 
+    x=int(input(f"Enter weight of item-{i}:")) 
+    weights.append(x) 
+    y=int(input(f"Enter profit of item-{i}:")) 
+    values.append(y) 
+print('weights:',weights) 
+print('Values/profits:',values) 
+capacity=int(input('Enter knapsack capacity:')) 
+max_value=knapsack_backtracking(weights,values,capacity,n) 
+print('The max profit is:',max_value)
+ if current_weight+weights[current_index]<=capacity: 
+            new_list=included_items[:] 
+            new_list.append(current_index) 
+            
+stack.append((current_index+1,current_weight+weights[current_index],current_value+values
+ [current_index],new_list)) 
+    print("Items included (0-indexed):", best_items) 
+    return best_value 
+n=int(input('Enter number of items:')) 
+weights=[] 
+values=[] 
+for i in range(n): 
+    x=int(input(f"Enter weight of item-{i}:")) 
+    weights.append(x) 
+    y=int(input(f"Enter profit of item-{i}:")) 
+    values.append(y) 
+print('weights:',weights) 
+print('Values/profits:',values) 
+capacity=int(input('Enter knapsack capacity:')) 
+max_value=knapsack_backtracking(weights,values,capacity,n) 
+print('The max profit is:',max_value)
+ if current_weight+weights[current_index]<=capacity: 
+            new_list=included_items[:] 
+            new_list.append(current_index) 
+            
+stack.append((current_index+1,current_weight+weights[current_index],current_value+values
+ [current_index],new_list)) 
+    print("Items included (0-indexed):", best_items) 
+    return best_value 
+n=int(input('Enter number of items:')) 
+weights=[] 
+values=[] 
+for i in range(n): 
+    x=int(input(f"Enter weight of item-{i}:")) 
+    weights.append(x) 
+    y=int(input(f"Enter profit of item-{i}:")) 
+    values.append(y) 
+print('weights:',weights) 
+print('Values/profits:',values) 
+capacity=int(input('Enter knapsack capacity:')) 
+max_value=knapsack_backtracking(weights,values,capacity,n) 
+print('The max profit is:',max_value)
+ if current_weight+weights[current_index]<=capacity: 
+            new_list=included_items[:] 
+            new_list.append(current_index) 
+            
+stack.append((current_index+1,current_weight+weights[current_index],current_value+values
+ [current_index],new_list)) 
+    print("Items included (0-indexed):", best_items) 
+    return best_value 
+n=int(input('Enter number of items:')) 
+weights=[] 
+values=[] 
+for i in range(n): 
+    x=int(input(f"Enter weight of item-{i}:")) 
+    weights.append(x) 
+    y=int(input(f"Enter profit of item-{i}:")) 
+    values.append(y) 
+print('weights:',weights) 
+print('Values/profits:',values) 
+capacity=int(input('Enter knapsack capacity:')) 
+max_value=knapsack_backtracking(weights,values,capacity,n) 
+print('The max profit is:',max_value)
+ if current_weight+weights[current_index]<=capacity: 
+            new_list=included_items[:] 
+            new_list.append(current_index) 
+            
+stack.append((current_index+1,current_weight+weights[current_index],current_value+values
+ [current_index],new_list)) 
+    print("Items included (0-indexed):", best_items) 
+    return best_value 
+n=int(input('Enter number of items:')) 
+weights=[] 
+values=[] 
+for i in range(n): 
+    x=int(input(f"Enter weight of item-{i}:")) 
+    weights.append(x) 
+    y=int(input(f"Enter profit of item-{i}:")) 
+    values.append(y) 
+print('weights:',weights) 
+print('Values/profits:',values) 
+capacity=int(input('Enter knapsack capacity:')) 
+max_value=knapsack_backtracking(weights,values,capacity,n) 
+print('The max profit is:',max_value)
+# https://condor.depaul.edu/ichu/csc491/notes/wk8/knapsack.html#:~:text=Bounding%20function%20is%20needed%20to,and%20any%20of%20its%20descendants.