diff --git a/Problem1.java b/Problem1.java
index e69de29b..08a0b4e2 100644
--- a/Problem1.java
+++ b/Problem1.java
@@ -0,0 +1,38 @@
+//Problem 1: Two Sum
+//Leetcode : https://leetcode.com/problems/two-sum/description/
+
+/*
+    Time Complexity: O(n)
+    - We traverse the array 'nums' exactly once.
+    - Each lookup and insertion in the HashMap takes O(1) on average.
+
+    Space Complexity: O(n)
+    - In the worst case, we store all 'n' elements of the array in the HashMap.
+
+    Approach: One-Pass Hash Map
+    - As we iterate through the array, we check if the 'complement' (target - current number)
+      already exists in our map.
+    - If it exists, it means we found the two numbers that add up to the target.
+    - If not, we store the current number and its index in the map to check against future numbers.
+*/
+
+class Solution {
+    public int[] twoSum(int[] nums, int target) {
+        HashMap<Integer,Integer> map = new HashMap<Integer,Integer>();
+        int[] result = new int[2];
+        for(int i=0; i < nums.length; i++ ){
+            if(map.containsKey(target - nums[i])){
+
+                result[0] = i;
+                result[1] = map.get(target-nums[i]);
+                break;
+
+            }else{
+                map.put(nums[i],i);
+            }
+        }
+
+        return result;
+
+    }
+}
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diff --git a/Problem2.java b/Problem2.java
index e69de29b..b9fa1581 100644
--- a/Problem2.java
+++ b/Problem2.java
@@ -0,0 +1,48 @@
+
+// Problem 2: 0-1-knapsack-problem
+
+
+public class Main {
+
+    /**
+     * Solves the 0/1 Knapsack problem using Dynamic Programming.
+     * Approach: 1D Array Space-Optimized Bottom-Up DP.
+     *
+     * Time Complexity: O(N)
+     * where N is the number of items and W is the maximum capacity.
+     * We iterate through each item and for each item, we iterate through the capacity.
+     *
+     * Space Complexity: O(W)
+     * Optimized from O(N * W) to O(W) by using a 1D array since the current state
+     * only depends on the previous item's results.
+     */
+
+    static int knapsack(int W, int[] val, int[] wt) {
+
+        // dp[j] stores the maximum value that can be attained with capacity j
+        int[] dp = new int[W + 1];
+
+        for (int i = 1; i <= wt.length; i++) {
+            /*
+             * Iterate backwards from W to wt[i-1].
+             * We go backwards to ensure that each item is only used once (0/1).
+             * If we went forwards, we might use the same item multiple times (Unbounded Knapsack).
+             */
+            for (int j = W; j >= wt[i - 1]; j--) {
+                // Decision: Either keep the previous max value for this capacity,
+                // or include the current item and add it to the max value of the remaining capacity.
+                dp[j] = Math.max(dp[j], dp[j - wt[i - 1]] + val[i - 1]);
+            }
+        }
+        return dp[W];
+    }
+
+    public static void main(String[] args) {
+
+        int[] val = {1, 2, 13, 4 , 7};
+        int[] wt = {4, 5, 1, 8 , 4};
+        int W = 10;
+
+        System.out.println(knapsack(W, val, wt));
+    }
+}
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