diff --git a/README.md b/README.md
index 99d40f7..14e2491 100644
--- a/README.md
+++ b/README.md
@@ -3,15 +3,35 @@ MLflow Pipeline
 
 Built E2E ML Pipeline with MLflow & AWS.
 
-<H3>Workflow</H3>
+## Workflow
 <p align = "center">
   <img class="center" src = "https://github.com/ronylpatil/mlflow-pipeline/blob/aws/workflow/flow.png" alt = "Drawing">
 </p>
 
-<H3>Blogs </H3>
+## Blogs
 
 - Part I - [Streamline ML Workflow with MLflow - I](https://medium.com/towards-artificial-intelligence/streamline-ml-workflow-with-mlflow%EF%B8%8F-part-i-60857cd511ed)
 - Part II - [Streamline ML Workflow with MLflow - II](https://medium.com/towards-artificial-intelligence/streamline-ml-workflow-with-mlflow-ii-daa8d50016f7)
 - Part III - [Configure DVC with Amazon S3 Bucket](https://medium.com/towards-artificial-intelligence/configure-dvc-with-amazon-s3-bucket-f6d57cd242d4)
 - Part IV - [Deploy MLflow Server on EC2 Instance](https://medium.com/towards-artificial-intelligence/deploy-mlflow-server-on-amazon-ec2-instance-b53d5eb3c4f3)
-<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
+
+## Installation
+
+1. Clone the repository.
+2. Install dependencies using `pip install -r requirements.txt`
+
+## Folder Structure
+- `/.github`: Contains CI/CD workflow file.
+- `/.dvc`: Contains configuration files of DVC
+- `/data`: Stores raw and processed data.
+- `/log`: Store the logs.
+- `/src`: Contains the source code files.
+- `/prod`: Production files.
+- `/tests`: Testing files.
+
+## Dataset
+
+- Download the dataset from [here](https://www.kaggle.com/datasets/yasserh/wine-quality-dataset).
+
+---
+<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. </small></p>