diff --git a/README.md b/README.md
index 04547d7..0bb4828 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,32 @@ number of clusters is unknown. Unlike K-means, however, DP-means is hard to para
 ### Installation
 `pip install pdc-dp-means`
 
+Installation requires `scikit-learn>=1.2,<1.3` and `numpy >= 1.23.0`.
+### Quick Start
+
+    from sklearn.datasets import make_blobs
+    from pdc_dp_means import DPMeans
+
+    # Generate sample data
+    X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
+
+    # Apply DPMeans clustering
+    dpmeans = DPMeans(n_clusters=1,n_init=10, delta=10)  # n_init and delta parameters
+    dpmeans.fit(X)
+
+    # Predict the cluster for each data point
+    y_dpmeans = dpmeans.predict(X)
+
+    # Plotting clusters and centroids
+    import matplotlib.pyplot as plt
+
+    plt.scatter(X[:, 0], X[:, 1], c=y_dpmeans, s=50, cmap='viridis')
+    centers = dpmeans.cluster_centers_
+    plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)
+    plt.show()
+
+One thing to note is that we replace the `\lambda` parameter from the paper with `delta` in the code, as `lambda` is a reserved word in python.
+
 ### Usage
 Please refer to the documentation: https://pdc-dp-means.readthedocs.io/en/latest/
 
@@ -72,4 +98,4 @@ If you use this code for your work, please cite the following:
 }
 ```
 ### License 
-Our code is licensed under the BDS-3-Clause license.
\ No newline at end of file
+Our code is licensed under the BDS-3-Clause license.
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 57eacee..3ce931a 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,3 @@
-numpy<=1.3.0
+numpy>=1.3.0
 scikit-learn==1.2.2
-pdc-dp-means
\ No newline at end of file
+pdc-dp-means
diff --git a/docs/source/index.rst b/docs/source/index.rst
index d554131..85f6a4a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,12 +1,35 @@
 Welcome to PDC-DP-Means documentation!
 ======================================
 
-**PDC-DP-Means** is a Python library for running fast, scalable DP-Means or Mini-Batch DP-Means. It is built on top scikit-learn and numpy.
+**PDC-DP-Means** is a Python library for running fast, scalable DP-Means or Mini-Batch DP-Means. It is built on top of scikit-learn and numpy.
 
 Check out the :doc:`usage` section for further information, including
-how to :ref:`installation` the project.
+how to :ref:`install <installation>` the project.
 
+Quickstart
+----------
+.. code-block:: python
 
+   from sklearn.datasets import make_blobs
+   from pdc_dp_means import DPMeans
+
+   # Generate sample data
+   X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
+
+   # Apply DPMeans clustering
+   dpmeans = DPMeans(n_clusters=1,n_init=10, delta=10)  # n_init and delta parameters
+   dpmeans.fit(X)
+
+   # Predict the cluster for each data point
+   y_dpmeans = dpmeans.predict(X)
+
+   # Plotting clusters and centroids
+   import matplotlib.pyplot as plt
+
+   plt.scatter(X[:, 0], X[:, 1], c=y_dpmeans, s=50, cmap='viridis')
+   centers = dpmeans.cluster_centers_
+   plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)
+   plt.show()
 
 Contents
 --------
@@ -29,4 +52,4 @@ If you use this package for your reseach, please cite the following paper:
    author={Dinari, Or and Freifeld, Oren},
    booktitle={The 38th Conference on Uncertainty in Artificial Intelligence},
    year={2022}
-   }
\ No newline at end of file
+   }