diff --git a/ContiguousArray.kt b/ContiguousArray.kt
new file mode 100644
index 00000000..8d9ab1bd
--- /dev/null
+++ b/ContiguousArray.kt
@@ -0,0 +1,27 @@
+//TimeComplexity O(n)
+//SpaceComplexity O(n)
+//In this problem we need to find balanced array of 0 or 1, we keep a running sum where we decrement when we observe 0 and increment when we observe 1
+// if the map already contains the running sum, it means we have found a balanced subarray. we check the max of index where we have found the subarray. 
+
+class Solution {
+    fun findMaxLength(nums: IntArray): Int {
+        val map = mutableMapOf<Int,Int>().apply {
+            put(0, -1)
+        }
+
+        var max = 0
+        var rSum = 0
+        for (i in 0..nums.size - 1) {
+            val num = nums[i]
+            if (num == 0) rSum--
+            else rSum++
+
+            if(map.containsKey(rSum)) {
+                max = Math.max(max, i - map.getOrDefault(rSum, 0))
+            } else {
+                map.put(rSum, i)
+            }
+        }
+        return max
+    }
+}
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diff --git a/LongestPalindrome.kt b/LongestPalindrome.kt
new file mode 100644
index 00000000..d83fd819
--- /dev/null
+++ b/LongestPalindrome.kt
@@ -0,0 +1,25 @@
+//TimeComplexity O(n)
+//SpaceComplexity O(1) 52 charackters at the max(26 for smallcase letters and 26 for uppercase letters). 
+// In this problem we need provide length of longest palindrome possible. we store the characters in a hashset, 
+// we add an element when we come across it first time and then pop or remove it when we come across same charater again. 
+// When returning the count we check if there is an element left in the hashset, if true then we add 1 to the count and return the count.  
+
+class Solution {
+    fun longestPalindrome(s: String): Int {
+        val set = HashSet<Char>();
+        var count = 0;
+
+        for(i in 0..s.lastIndex) {
+            if(set.contains(s[i])) {
+                set.remove(s[i])
+                count += 2
+            } else {
+                set.add(s[i])
+            }
+        }
+        if(!set.isEmpty()) {
+            count++
+        }
+        return count;
+    }
+}
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diff --git a/SubarraySum.kt b/SubarraySum.kt
new file mode 100644
index 00000000..fb78d7a5
--- /dev/null
+++ b/SubarraySum.kt
@@ -0,0 +1,21 @@
+//Time Complexity O(n)
+//Space Complexity O(n)
+//In this problem we keep a running Sum and check if runningSum - k (target) has already occured in the hasmap before. If it has we increase the count and return the number of subarrays. 
+class Solution {
+    fun subarraySum(nums: IntArray, k: Int): Int {
+
+        val prefixSum = mutableMapOf<Int,Int>().apply {
+            put(0, 1)
+        }
+
+        var count = 0
+        var runningSum = 0
+
+        for(n in nums) {
+            runningSum = runningSum + n
+            count += prefixSum.getOrDefault(runningSum - k, 0)
+            prefixSum.put(runningSum, prefixSum.getOrDefault(runningSum, 0) + 1)
+        }
+        return count
+    }
+}
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