diff --git a/HashSet.java b/HashSet.java
new file mode 100644
index 00000000..05abe1da
--- /dev/null
+++ b/HashSet.java
@@ -0,0 +1,60 @@
+
+// Time Complexity : O(1) for add (amortized), remove and contains
+// Space Complexity : O(1)
+// Did this code successfully run on Leetcode : Yes
+// Any problem you faced while coding this : 
+
+// this approach uses double hashing to store the keys in a 2D boolean array. 
+// The primary hash function determines the index of the first dimension, 
+// while the secondary hash function determines the index of the second dimension. 
+// This allows us to efficiently store and retrieve keys while minimizing space usage.
+
+class MyHashSet {
+    int primaryBuckets;
+    int secondaryBuckets;
+    boolean[][] storage;
+
+    public MyHashSet() {
+        primaryBuckets = 1000;
+        secondaryBuckets = 1000;
+        storage = new boolean[primaryBuckets][];
+    }
+
+    int getPrimaryHash(int key){
+        return key%primaryBuckets;
+    }
+    int getSecondaryHash(int key){
+        return key/secondaryBuckets;
+    }
+
+    public void add(int key) {
+        int primaryIndex = getPrimaryHash(key);
+        if(storage[primaryIndex]==null){
+            if(primaryIndex == 0){
+                storage[primaryIndex] = new boolean[secondaryBuckets +1];
+            }else{
+                storage[primaryIndex] = new boolean[secondaryBuckets];
+            }
+        }
+        int secondaryIndex = getSecondaryHash(key);
+        storage[primaryIndex][secondaryIndex] = true;
+    }
+
+    public void remove(int key) {
+        int primaryIndex = getPrimaryHash(key);
+        if(storage[primaryIndex]==null){
+            return;
+        }
+        int secondaryIndex = getSecondaryHash(key);
+        storage[primaryIndex][secondaryIndex] = false;
+    }
+
+    public boolean contains(int key) {
+        int primaryIndex = getPrimaryHash(key);
+        if(storage[primaryIndex]==null){
+            return false;
+        }
+        int secondaryIndex = getSecondaryHash(key);
+        return storage[primaryIndex][secondaryIndex];
+    }
+}
\ No newline at end of file
diff --git a/MinStack.java b/MinStack.java
new file mode 100644
index 00000000..c78f10d1
--- /dev/null
+++ b/MinStack.java
@@ -0,0 +1,47 @@
+// Time Complexity : O(1) for push, pop, top and getMin
+// Space Complexity : O(n) where n is the number of elements in the stack
+// Did this code successfully run on Leetcode : Yes
+// Any problem you faced while coding this : 
+
+// This approach uses two stacks: 
+// a primary stack to store all the elements and 
+// a min stack to keep track of the minimum element at each level of the primary stack. 
+// Whenever a new element is pushed onto the primary stack, 
+// we compare it with the current minimum element (the top of the min stack) and
+// push the smaller of the two onto the min stack.
+
+import java.util.Stack;
+
+class MinStack {
+    Stack<Integer> primaryStack;
+    Stack<Integer> minStack;
+    public MinStack() {
+        primaryStack = new Stack<Integer>();
+        minStack = new Stack<Integer>();
+    }
+    
+    public void push(int val) {
+        primaryStack.push(val);
+        if(minStack.isEmpty() || val < minStack.peek()){
+            minStack.push(val);
+        } else {
+            minStack.push(minStack.peek());
+        }
+    }
+    
+    public void pop() {
+        if(primaryStack.isEmpty()){
+            return;
+        }
+        primaryStack.pop();
+        minStack.pop();
+    }
+    
+    public int top() {
+        return primaryStack.peek();
+    }
+    
+    public int getMin() {
+        return minStack.peek();
+    }
+}
diff --git a/Sample.java b/Sample.java
deleted file mode 100644
index 1739a9cb..00000000
--- a/Sample.java
+++ /dev/null
@@ -1,7 +0,0 @@
-// Time Complexity :
-// Space Complexity :
-// Did this code successfully run on Leetcode :
-// Any problem you faced while coding this :
-
-
-// Your code here along with comments explaining your approach