diff --git a/README.md b/README.md
index c5def14..fd64f1d 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,41 @@
-# Chain sampling plans
+# Chain Sampling Analysis Project
 
-Producer risk in manufacturing is the probability that a good product will be rejected by a consumer because a bad batch was observed in a sample that has been tested. Due to the cost of manual quality check, often times we rely on checking the quality of products in samples, and consequtive samples and take a decision whether the lot of produced/manufactured items are acceptable to market/consumption.
+Producer risk in manufacturing is the probability that a good product will be rejected by a consumer because a bad batch was observed in a sample that has been tested. Due to the cost of manual quality checks, often times we rely on checking the quality of products in samples, and consecutive samples and make a decision whether a lot of produced/manufactured items are acceptable to market/consumption.
 
-Here we replicate the constibutions in chain sampling plans from research in academia and made available for consumtpion.
+Here we replicate the contributions in chain sampling plans from research in academia and make them available for consumption.
+
+## Overview
+In this project, we delve deep into the chain sampling method, a pivotal technique in quality control. Using synthetic data, we simulate two distinct real-world scenarios: 
+1. Manufacturing of sneaker insoles
+2. Production of LED bulbs
+
+Our objective is to decode the criteria for batch acceptance or rejection based on defect or failure rates.
+
+## Contents
+
+- **Chain Sampling Analysis Notebook**: This Jupyter notebook contains a detailed analysis, from generating synthetic data to visualizing results. It's equipped with explanations at each step to ensure clarity of the approach and findings.
+  
+- **Sampling Code**: The core sampling functions and methodologies are derived from the code provided in the `sampling_min_sum.py` file.
+
+## Key Highlights
+
+- **Synthetic Data Generation**: We create realistic synthetic datasets for both scenarios, providing a foundation for our analysis.
+  
+- **Sampling Plans**: Utilizing the single sampling plan function, we determine the acceptance or rejection of batches.
+  
+- **Visualization**: A visual representation of defect rates and decisions, aiding in a clearer understanding of the results.
+
+## Getting Started
+
+1. Clone the repository to your local machine.
+2. Navigate to the directory and open the `chain_sampling_analysis.ipynb` notebook.
+3. Execute the notebook cells sequentially to understand the analysis flow.
+
+## Requirements
+
+- Python 3.8+
+- Libraries: pandas, numpy, matplotlib
+
+## Contributions
+
+Feel free to fork this project and enhance it. Pull requests with improvements and optimizations are always welcome.