diff --git a/Problem1.py b/Problem1.py
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+"""
+TC: O(N * 2^N) {where N is the number of elements in `nums`. There are `2^N` possible subsets, and for each one, it takes `O(N)` time to copy it to the result list.}
+SC: O(N * 2^N) {The space is determined by the output array, which stores `2^N` subsets, each with a potential length of `N`.}
+
+Approach:
+
+This problem is solved using a **backtracking** algorithm. The core idea is to systematically generate all possible subsets by exploring a decision tree where at each step, we consider adding the current element to our path. The recursive `backtrack` function takes a `pivot` index to ensure we only consider elements in increasing order, which prevents duplicate subsets.
+
+In each recursive call, the current `arr` (representing a subset) is added to our result list. We then iterate from the `pivot` index to the end of the input `nums` array. For each element, we add it to the `arr` and make a recursive call with the next index (`i+1`). After the call returns, we **backtrack** by removing the last element added. This process continues until all possible subsets have been generated.
+
+The problem ran successfully on LeetCode.
+"""
+from typing import List
+class Solution:
+    def subsets(self, nums: List[int]) -> List[List[int]]:
+
+        def backtrack(pivot, arr):
+            #base case
+           
+            res.append(arr[:])
+
+
+            #logic
+
+            for i in range(pivot, len(nums)):
+                arr.append(nums[i])
+                backtrack(i+1, arr)
+                arr.pop()    
+        
+
+        res = []
+        backtrack(pivot=0,arr=[])
+        return res
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diff --git a/Problem2.py b/Problem2.py
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+"""
+TC: O(N * 2^N) {where N is the length of the string. In the worst case, there are up to `2^(N-1)` partitions, and each palindrome check takes `O(N)` time.}
+SC: O(N) {The space complexity is determined by the recursion stack depth, which can go up to `N`, and the path list.}
+
+Approach:
+
+This problem is solved using a **backtracking** algorithm to find all possible palindrome partitions of the input string. The approach is recursive and works by building a valid partition step by step. A helper function `isPal` is used to check if a substring is a palindrome.
+
+The recursive `backtrack` function iterates through the string, starting from a `pivot` index. For each substring starting at the `pivot`, we check if it's a palindrome. If it is, we add it to our current partition `path` and make a recursive call for the remaining part of the string, with the new `pivot` being the index immediately following the current substring. After the recursive call returns, we **backtrack** by removing the last added substring from our `path`, allowing us to explore alternative partitions. The base case for the recursion is when the `pivot` reaches the end of the string, signifying a complete and valid partitioning.
+
+The problem ran successfully on LeetCode.
+"""
+from typing import List
+class Solution:
+    def partition(self, s: str) -> List[List[str]]:
+        def isPal(char):
+            l,r = 0, len(char)-1
+            while l<r:
+                if char[l] != char[r]:
+                    return False
+                l+=1
+                r-=1
+            return True
+
+        def backtrack(pivot, path):
+            #base case
+            if pivot >= len(s):
+                res.append(path[:])
+                return
+
+            #logic
+            for i in range(pivot, len(s)):
+                curr = s[pivot:i+1]
+                if isPal(curr):
+                    path.append(curr)
+                    backtrack(i+1, path)
+                    path.pop()
+
+        res = []
+        backtrack(pivot = 0, path = [])
+        return res
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