Fixes collision handling

README.md

@@ -507,15 +507,22 @@ We are grateful to all the contributors who have helped improve this project. Yo
                     <sub><b>Rachita Dashore</b></sub>
                 </a>
             </td>
+            <td align="center">
+                <a href="https://github.com/rajatsinghal02">
+                    <img src="https://avatars.githubusercontent.com/u/112543741?v=4" width="100;" alt="rajatsinghal02"/>
+                    <br />
+                    <sub><b>Rajat singhal</b></sub>
+                </a>
+            </td>
+		</tr>
+		<tr>
             <td align="center">
                 <a href="https://github.com/Kratik1093">
                     <img src="https://avatars.githubusercontent.com/u/153417068?v=4" width="100;" alt="Kratik1093"/>
                     <br />
                     <sub><b>Kratik Mandloi </b></sub>
                 </a>
             </td>
-		</tr>
-		<tr>
             <td align="center">
                 <a href="https://github.com/meghanakn22">
                     <img src="https://avatars.githubusercontent.com/u/172406754?v=4" width="100;" alt="meghanakn22"/>
@@ -551,29 +558,22 @@ We are grateful to all the contributors who have helped improve this project. Yo
                     <sub><b>subashree</b></sub>
                 </a>
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+		</tr>
+		<tr>
             <td align="center">
                 <a href="https://github.com/sejals23">
                     <img src="https://avatars.githubusercontent.com/u/183209584?v=4" width="100;" alt="sejals23"/>
                     <br />
                     <sub><b>Sejal</b></sub>
                 </a>
             </td>
-		</tr>
-		<tr>
             <td align="center">
                 <a href="https://github.com/RanaJay3101">
                     <img src="https://avatars.githubusercontent.com/u/104300431?v=4" width="100;" alt="RanaJay3101"/>
                     <br />
                     <sub><b>Rana Jay</b></sub>
                 </a>
             </td>
-            <td align="center">
-                <a href="https://github.com/rajatsinghal02">
-                    <img src="https://avatars.githubusercontent.com/u/112543741?v=4" width="100;" alt="rajatsinghal02"/>
-                    <br />
-                    <sub><b>Rajat singhal</b></sub>
-                </a>
-            </td>
             <td align="center">
                 <a href="https://github.com/Rahul7raj">
                     <img src="https://avatars.githubusercontent.com/u/69787135?v=4" width="100;" alt="Rahul7raj"/>

docs/Hashing/CollisionHandling.md

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+---
+id: collision-handling-in-hashing
+sidebar_position: 5
+title: Collision Handling in Hashing
+sidebar_label: Collision Handling in Hashing
+tags: [hashing, data structures]
+---
+
+# Collision Handling in Hashing
+
+## Overview
+In hashing, collision handling refers to the methods used to resolve the issue when two keys hash to the same index in a hash table. Collisions are inevitable in hash tables due to the limited number of available slots compared to the potentially infinite number of input keys.
+
+## Methods of Collision Handling
+
+### 1. Chaining (Open Hashing)
+- Chaining stores multiple elements in the same bucket using a data structure like a linked list.
+- When a collision occurs, the new key-value pair is added to the end of the linked list at that index.
+- **Advantages**: Simple to implement, no need to resize the hash table.
+- **Disadvantages**: Requires extra memory for pointers, performance degrades if many elements collide at the same index.
+
+### 2. Open Addressing (Closed Hashing)
+- In open addressing, all elements are stored within the hash table itself, and when a collision occurs, alternative slots are probed to find an empty one.
+- Common probing techniques include:
+  - **Linear Probing**: Increment the index sequentially until an empty slot is found.
+  - **Quadratic Probing**: Probe at quadratic intervals (i.e., 1, 4, 9, ...).
+  - **Double Hashing**: Use a second hash function to determine the probe step.
+- **Advantages**: No need for extra memory for pointers, can be efficient for smaller tables.
+- **Disadvantages**: Can lead to clustering (for linear probing), needs good probing strategies to avoid performance degradation.
+
+## Example: Chaining in Python
+
+```python
+class HashTable:
+    def __init__(self):
+        self.table = [[] for _ in range(10)]  # Hash table with 10 slots
+
+    def insert(self, key, value):
+        index = hash(key) % len(self.table)
+        for pair in self.table[index]:
+            if pair[0] == key:
+                pair[1] = value  # Update if key exists
+                return
+        self.table[index].append([key, value])  # Add new key-value pair
+
+    def search(self, key):
+        index = hash(key) % len(self.table)
+        for pair in self.table[index]:
+            if pair[0] == key:
+                return pair[1]
+        return None
+
+    def delete(self, key):
+        index = hash(key) % len(self.table)
+        for i, pair in enumerate(self.table[index]):
+            if pair[0] == key:
+                del self.table[index][i]
+                return
+
+
+
+# Example usage
+hash_table = HashTable()
+hash_table.insert('apple', 1)
+hash_table.insert('banana', 2)
+hash_table.insert('orange', 3)
+print(hash_table.search('banana'))  # Output: 2
+hash_table.delete('banana')
+print(hash_table.search('banana'))  # Output: None
+
+```
+