diff --git a/Sprint-1/JavaScript/calculateSumAndProduct/calculateSumAndProduct.js b/Sprint-1/JavaScript/calculateSumAndProduct/calculateSumAndProduct.js
index ce738c3..8060b10 100644
--- a/Sprint-1/JavaScript/calculateSumAndProduct/calculateSumAndProduct.js
+++ b/Sprint-1/JavaScript/calculateSumAndProduct/calculateSumAndProduct.js
@@ -9,22 +9,22 @@
  *   "product": 30 // 2 * 3 * 5
  * }
  *
- * Time Complexity:
- * Space Complexity:
- * Optimal Time Complexity:
- *
+ * Time Complexity:O(n)
+ * Space Complexity:o(1)
+ * Optimal Time Complexity:o(n)
+ *The original implementation used two separate loops.
+ *This refactor combines them into a single loop to reduce
+ *the constant factor, while maintaining the same Big-O complexity.
+ 
  * @param {Array<number>} numbers - Numbers to process
  * @returns {Object} Object containing running total and product
  */
 export function calculateSumAndProduct(numbers) {
   let sum = 0;
-  for (const num of numbers) {
-    sum += num;
-  }
-
   let product = 1;
   for (const num of numbers) {
     product *= num;
+    sum += num;
   }
 
   return {
diff --git a/Sprint-1/JavaScript/hasPairWithSum/hasPairWithSum.js b/Sprint-1/JavaScript/hasPairWithSum/hasPairWithSum.js
index dd2901f..61e7c9c 100644
--- a/Sprint-1/JavaScript/hasPairWithSum/hasPairWithSum.js
+++ b/Sprint-1/JavaScript/hasPairWithSum/hasPairWithSum.js
@@ -1,21 +1,30 @@
 /**
  * Find if there is a pair of numbers that sum to a given target value.
  *
- * Time Complexity:
- * Space Complexity:
- * Optimal Time Complexity:
- *
+ * Time Complexity:before refactoring O(n²)- after refactoring O(n log n)
+ * Space Complexity:before refactoring O(1)- after refactoring O(1)
+ * Optimal Time Complexity:O(n log n)
+ * https://www.hellointerview.com/learn/code/two-pointers/overview
+ * The refactored solution first sorts the array, which takes O(n log n) time,
+ * and then uses the two-pointer technique to scan the array in a single pass (O(n)). The sorting step dominates the overall complexity.
  * @param {Array<number>} numbers - Array of numbers to search through
  * @param {number} target - Target sum to find
  * @returns {boolean} True if pair exists, false otherwise
  */
 export function hasPairWithSum(numbers, target) {
-  for (let i = 0; i < numbers.length; i++) {
-    for (let j = i + 1; j < numbers.length; j++) {
-      if (numbers[i] + numbers[j] === target) {
-        return true;
-      }
-    }
+  numbers.sort((a,b)=>a-b);
+  let left=0;
+  let right=numbers.length-1;
+  while (left<right) {
+    const current_sum=numbers[left]+numbers[right];
+    if (current_sum === target){
+      return true;}
+    if (current_sum<target){
+      left +=1;
+    } else {
+      right -=1;
+    }  
   }
   return false;
+
 }
diff --git a/Sprint-1/JavaScript/package-lock.json b/Sprint-1/JavaScript/package-lock.json
new file mode 100644
index 0000000..37ca6f0
--- /dev/null
+++ b/Sprint-1/JavaScript/package-lock.json
@@ -0,0 +1,12 @@
+{
+  "name": "module-complexity-sprint-1",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "module-complexity-sprint-1",
+      "version": "1.0.0"
+    }
+  }
+}
diff --git a/Sprint-1/JavaScript/removeDuplicates/removeDuplicates.mjs b/Sprint-1/JavaScript/removeDuplicates/removeDuplicates.mjs
index dc5f771..f13e7dc 100644
--- a/Sprint-1/JavaScript/removeDuplicates/removeDuplicates.mjs
+++ b/Sprint-1/JavaScript/removeDuplicates/removeDuplicates.mjs
@@ -1,36 +1,24 @@
 /**
  * Remove duplicate values from a sequence, preserving the order of the first occurrence of each value.
  *
- * Time Complexity:
- * Space Complexity:
- * Optimal Time Complexity:
- *
+ * Time Complexity:before refactoring O(n²)- after refactoring O(n)
+ * Space Complexity:O(n)
+ * Optimal Time Complexity:O(n)
+ 
+ * The original implementation relied on nested loops to detect duplicates,
+ *resulting in O(n²) time complexity, which does not scale well for large inputs.
+ *The refactored solution uses a Set to perform duplicate checks in constant time.
+ *This removes the need for an inner loop and reduces the overall time complexity
+ *to O(n), which is optimal for this problem.
+ *using  https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Set
  * @param {Array} inputSequence - Sequence to remove duplicates from
  * @returns {Array} New sequence with duplicates removed
  */
 export function removeDuplicates(inputSequence) {
-  const uniqueItems = [];
+  const uniqueItemSet = new Set;
 
-  for (
-    let currentIndex = 0;
-    currentIndex < inputSequence.length;
-    currentIndex++
-  ) {
-    let isDuplicate = false;
-    for (
-      let compareIndex = 0;
-      compareIndex < uniqueItems.length;
-      compareIndex++
-    ) {
-      if (inputSequence[currentIndex] === uniqueItems[compareIndex]) {
-        isDuplicate = true;
-        break;
-      }
-    }
-    if (!isDuplicate) {
-      uniqueItems.push(inputSequence[currentIndex]);
-    }
+  for (let i=0 ;i<inputSequence.length;i++){
+    uniqueItemSet.add(inputSequence[i]);
   }
-
-  return uniqueItems;
+   return [...uniqueItemSet];
 }
diff --git a/Sprint-1/Python/find_common_items/find_common_items.py b/Sprint-1/Python/find_common_items/find_common_items.py
index 478e2ef..024828b 100644
--- a/Sprint-1/Python/find_common_items/find_common_items.py
+++ b/Sprint-1/Python/find_common_items/find_common_items.py
@@ -1,21 +1,24 @@
-from typing import List, Sequence, TypeVar
+from typing import List,  TypeVar
 
 ItemType = TypeVar("ItemType")
 
+# Original version: O(n × m) due to nested loops
+# Refactored version: O(n + m) using a set for constant-time lookups
+# This is optimal and cannot be further reduced because all input
+# elements must be processed at least once.
+# use this link :https://www.w3schools.com/python/ref_set_intersection.asp
 
 def find_common_items(
-    first_sequence: Sequence[ItemType], second_sequence: Sequence[ItemType]
+    first_sequence, second_sequence
 ) -> List[ItemType]:
     """
     Find common items between two arrays.
 
-    Time Complexity:
-    Space Complexity:
-    Optimal time complexity:
+    Time Complexity:O(n + m)
+    Space Complexity:O(n + m)
+    Optimal time complexity:O(n + m)
     """
-    common_items: List[ItemType] = []
-    for i in first_sequence:
-        for j in second_sequence:
-            if i == j and i not in common_items:
-                common_items.append(i)
-    return common_items
+    second_set=set(second_sequence)
+    first_set=set(first_sequence)
+    common_set=first_set.intersection(second_set)
+    return list(common_set)