diff --git a/Problem1.java b/Problem1.java
index 8b137891..8b39a3bf 100644
--- a/Problem1.java
+++ b/Problem1.java
@@ -1 +1,62 @@
+//Problem: Finding missing number in a sorted array
+
+class Main {
+    /**
+     * Approach 1: This method uses the property that in a sorted array starting from a specific value,
+     * the difference between the element and its index (nums[i] - i) should remain constant.
+     * If a number is missing, this difference will change for all elements after the gap.
+     *
+     * Time Complexity: O(log N) - Standard binary search where the search space is halved in each step.
+     * Space Complexity: O(1) - Only a few variables are used regardless of input size.
+     */
+
+    private static int search(int[] nums){
+
+        int low = 0;
+        int high = nums.length -1;
+
+        while(high - low > 1){
+            int mid = low + (high - low)/2;
+            if(nums[mid] - mid == nums[low] - low){
+                low = mid;
+            }else{
+                high = mid;
+            }
+        }
+        return (nums[low] + nums[high])/2;
+    }
+
+    /**
+     * Approach 2 : This method checks if the current element (arr[mid]) is exactly one greater
+     * than its predecessor. If it is, the sequence is intact up to that point, so we
+     * search the right half. Otherwise, the break occurred earlier, so we search the left.
+     *
+     * Time Complexity: O(log N) - Each iteration reduces the search range by half.
+     * Space Complexity: O(1) - Constant space used for pointers.
+     */
+
+    private static int search2(int[] arr){
+
+        int low = 0;
+        int high = arr.length-1;
+
+        while(high - low > 1){
+            int mid = low + (high - low)/2;
+            if(arr[mid] == arr[mid - 1] + 1){
+                low = mid;
+            }else{
+                high = mid;
+            }
+        }
+
+
+        return arr[low]+1;
+    }
+    public static void main(String[] args) {
+        int[] nums = { 2,3,4,5,6,7,9};
+
+        System.out.println("Missing number: " + search(nums));
+        //System.out.println("Missing number: " + search2(nums));
+    }
+}
 
diff --git a/Problem2.java b/Problem2.java
index 8b137891..4840a948 100644
--- a/Problem2.java
+++ b/Problem2.java
@@ -1 +1,121 @@
+package Heap;
 
+public class BinaryMinHeap {
+    int heapArray[];
+    int size;
+
+    //Time Complexity: O(1)
+    //Space Complexity: O(N)
+    // Constructor initializes the heap with a given capacity.
+     // We use a 1-based indexing for easier parent-child relationship calculations.
+
+    public BinaryMinHeap(int capacity){
+        this.size = 0;
+        this.heapArray = new int[capacity+1];
+        System.out.println(" Binary Heap of size " + capacity + " has been created");
+    }
+    //Time Complexity: O(1)
+    //Space Complexity: O(1)
+    // Checks if the heap contains any elements by verifying the current size.
+    public boolean isEmpty(){
+        return size == 0;
+    }
+    //Time Complexity: O(1)
+    //Space Complexity: O(1)
+    //Returns the root element (minimum) without removing it.
+    //In a min-heap, the minimum value is always at index 1.
+    public Integer peek(){
+        if(isEmpty()){
+            System.out.println("The Heap is Empty.");
+            return null;
+        }
+        return heapArray[1];
+    }
+    //Time Complexity: O(1)
+    //Space Complexity: O(1)
+    //Returns the current number of elements in the heap.
+    public int sizeOfHeap(){
+        return size;
+    }
+
+    //Time Complexity: O(N)
+    //Space Complexity: O(1)
+    //Iterates through the array from index 1 to size to print elements
+    // in the order they appear in the heap's array representation.
+    public void levelOrderTraversal(){
+        for(int i=1; i<=size; i++){
+            System.out.print(heapArray[i] + " ");
+        }
+        System.out.println("");
+    }
+    //Time Complexity: O(logN)
+    //Space Complexity: O(logN)
+    //Inserts a new value at the end of the heap (next available leaf)
+    //and then bubbles it up to restore the min-heap property.
+    public void insert(int value){
+        heapArray[++size] = value;
+        heapifyBottomUp(size);
+    }
+    //Time Complexity: O(logN)
+    //Space Complexity: O(logN)
+    //Maintains min-heap property by comparing the current node with its parent.
+    //If the current node is smaller, they are swapped, and the process continues upward.
+    public void heapifyBottomUp(int index){
+        int parent = index/2;
+        // base condition
+        if(index <= 1){
+            return;
+        }
+        //swap condition for min heap
+        if(heapArray[index] < heapArray[parent]){
+            int temp = heapArray[index];
+            heapArray[index] = heapArray[parent];
+            heapArray[parent] = temp;
+        }
+        heapifyBottomUp(parent);
+    }
+
+    //Time Complexity: O(logN)
+    //Space Complexity: O(logN)
+    //Maintains min-heap property by comparing the current node with its children.
+    //If a child is smaller than the parent, it swaps with the smaller child and continues downward.
+    public void heapifyToptoBottom(int index){
+
+        int leftChild = 2*index;
+        int rightChild = 2*index + 1;
+
+        if(leftChild <= size && heapArray[leftChild] < heapArray[index]){
+            int temp = heapArray[leftChild];
+            heapArray[leftChild] = heapArray[index];
+            heapArray[index] = temp;
+            heapifyToptoBottom(leftChild);
+        }else if(rightChild <= size && heapArray[rightChild] < heapArray[index]){
+            int temp = heapArray[rightChild];
+            heapArray[rightChild] = heapArray[index];
+            heapArray[index] = temp;
+            heapifyToptoBottom(rightChild);
+        }
+    }
+
+    //Time Complexity: O(logN)
+    //Space Complexity: O(logN)
+    //Removes and returns the minimum element (root).
+    //The last element in the heap replaces the root, the size is decremented,
+    //and heapifyToptoBottom is called to restore the heap property.
+    public int extractMin(){
+        if(isEmpty()){
+            return -1;
+        }
+        int extractedValue = heapArray[1];
+        heapArray[1] = heapArray[size--];
+        heapifyToptoBottom(1);
+        return extractedValue;
+    }
+
+    //Time Complexity: O(1)
+    //Space Complexity: O(1)
+    public void deleteHeap(){
+        heapArray = null;
+        System.out.println();
+    }
+}