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+<!-- REMOVE ALL THE COMMENTED PART AFTER WRITING YOUR DOCUMENTATION. -->
+<!-- THESE COMMENTS ARE PROVIDED SOLELY FOR YOUR ASSISTANCE AND TO OUTLINE THE REQUIREMENTS OF THIS PROJECT. -->
+<!-- YOU CAN ALSO DESIGN YOUR ALGORITHM DOCUMENTATION AS YOU WISH BUT SHOULD BE UNDERSTANABLE TO A NEWBIE. -->
+<!-- FOR REFERENCE, YOU MAY CONSULT THE FILE LOCATED AT 'docs\nlp\projects\twitter_sentiment_analysis.md'. -->
+
+
+# Algorithms Title
+<!-- Provide the name of the algorithm. Keep it concise and descriptive. -->
+
+
+<!-- Uncomment the code block below to display the poster on your webpage. 
+     This section uses an HTML <div> element to center-align the image on the page.
+     The <img> tag within this <div> references a URL to the image, which will be displayed when the code is uncommented. The image should represent relevant content or a visual representation of the algorithm in question. 
+     Ensure the 'src' attribute of the <img> tag points to a valid image URL for the poster to appear as intended. -->
+     
+<!-- <div align="center">
+    <img src="https://static.toiimg.com/thumb/msid-88446922,width-1280,height-720,resizemode-4/88446922.jpg" />
+</div> -->
+
+## Objectives
+<!-- Describe the main goals or objectives of the algorithm. For example, "To optimize sorting operations in large datasets." -->
+
+## Pre-requisites
+<!-- Mention any background knowledge or dependencies required to understand or use the algorithm. Include mathematical concepts, programming prerequisites, or any required libraries. -->
+
+- Linear Algebra Basics
+- Probability and Statistics
+- Libraries: NumPy, TensorFlow, PyTorch (as applicable)
+
+??? quote "LIBRARIES USED"
+
+    - pandas
+    - numpy
+    - scikit-learn
+    - matplotlib
+    - seaborn
+    - tensorflow
+    - keras
+
+## Input
+<!-- Define the expected input(s) to the algorithm, including data formats, structures, or constraints. -->
+
+Example:
+- A dataset with features (X) and labels (y) for supervised learning.
+- Text data for NLP tasks.
+
+## Output
+<!-- Specify the output(s) produced by the algorithm, including formats or structures. -->
+
+Example:
+- Predicted labels for a classification task.
+- Clusters for an unsupervised learning task.
+
+## Algorithm Architecture
+<!-- Provide an overview of the architecture or methodology used in the algorithm. Include any diagrams if applicable. -->
+
+Example:
+- Input Layer
+- Hidden Layers (Dense/Recurrent/Convolutional)
+- Output Layer
+
+## Training Process
+<!-- Outline the process of training the algorithm, including optimization techniques, loss functions, and hyperparameter tuning. -->
+
+Example:
+- Loss Function: Mean Squared Error (MSE)
+- Optimizer: Adam, Learning Rate = 0.001
+- Epochs: 50
+- Batch Size: 32
+
+## Evaluation Metrics
+<!-- Specify the metrics used to evaluate the algorithm's performance. -->
+
+Example:
+- Accuracy
+- Precision, Recall, F1-Score
+- Mean Absolute Error (MAE)
+
+## Code Implementation
+<!-- Provide the link to the notebook where you implemented the algorithm. It could be a Kaggle, Colab, or Drive link. -->
+
+
+## Scratch Code Explanation
+<!-- Provide a step-by-step explanation of the code implementation for better understanding. -->
+
+Example:
+- Step 1: Initialize the parameters.
+- Step 2: Preprocess the input data.
+- Step 3: Perform calculations iteratively.
+- Step 4: Return the result.
+
+## Examples
+<!-- Provide examples of how the algorithm can be applied. Include code snippets or pseudo-code if necessary. -->
+
+Example:
+```python
+# Example usage
+input_data = np.array([...])
+result = example_algorithm(input_data)
+print(result)
+```
+
+
+## APPLICATIONS 
+<!-- Mention at least two real-world applications of this project. -->
+
+=== "Application 1"
+
+    **Headline**
+    
+      - Explain your application
+
+=== "Application 2"
+
+    **Headline**
+    
+      - Explain your application
+
+<!-- AFTER COMPLETEING THE DOCUMENTATION, UPDATE THE `index.md` file of the domian of which your algorithm is a part of.  -->
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
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--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -42,10 +42,11 @@ We want your work to be readable by others; therefore, we encourage you to note
 
 1. File names should be in `kebab-case` letters (e.g., `music-genre-classification-model`, `insurance-cross-sell-prediction`).
 2. Follow the [***PROJECT README TEMPLATE***](./PROJECT-README-TEMPLATE.md) for refrence.
-3. Do not upload images or video files directly. Use a GitHub raw URL in the documentation.
-4. Upload your notebook to Kaggle, make it public, and share the Kaggle link only. Other links are not accepted.
-5. Limit commits to 3-4 unless given permission by project Admins or Mentors.
-6. Keep commit messages clear and relevant; avoid unnecessary details.
+3. Follow the [***ALGORITHMS README TEMPLATE***](./ALGORITHM-README-TEMPLATE.md) for reference.
+4. Do not upload images or video files directly. Use a GitHub raw URL in the documentation.
+5. Upload your notebook to Kaggle, make it public, and share the Kaggle link only. Other links are not accepted.
+6. Limit commits to 3-4 unless given permission by project Admins or Mentors.
+7. Keep commit messages clear and relevant; avoid unnecessary details.
 
 ### Pull Requests Review Criteria 🧲