diff --git a/scikit-learn/kmeans/README.md b/scikit-learn/kmeans/README.md
index 680453b..59c661d 100644
--- a/scikit-learn/kmeans/README.md
+++ b/scikit-learn/kmeans/README.md
@@ -17,6 +17,8 @@ Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
 import numpy as np
 import matplotlib.pyplot as plt
 
+rng = np.random.default_rng(1)
+
 rng = np.random.default_rng()
 
 a_x = rng.normal(1.5, 1.0, size=(1000))
@@ -31,3 +33,61 @@ plt.show()
 ```
 ![image0](image0.png)
 
+## [sklearn.cluster.KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) and its [fit](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.fit)
+
+```python
+class sklearn.cluster.KMeans(n_clusters=8, *, init='k-means++', n_init='warn', max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, algorithm='lloyd')
+```
+
+> K-Means clustering.
+
+
+Attribute:
+> **cluster_centers_** : ndarray of shape (n_clusters, n_features)
+>    Coordinates of cluster centers. If the algorithm stops before fully converging (see tol and max_iter), these will not be consistent with labels_.
+
+Method: 
+```python
+fit(X, y=None, sample_weight=None)
+```
+
+> Compute k-means clustering
+>   **X**: {array-like, sparse matrix} of shape (n_samples, n_features)
+>    Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+
+rng = np.random.default_rng(1)
+
+a_x = rng.normal(1.5, 1.0, size=(1000))[:, np.newaxis]
+a_y = rng.normal(3.0, 1.0, size=(1000))[:, np.newaxis]
+data_a = np.concatenate((a_x, a_y), axis=1)
+
+b_x = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
+b_y = rng.normal(0.0, 1.0, size=(1000))[:, np.newaxis]
+data_b = np.concatenate((b_x, b_y), axis=1)
+
+data = np.concatenate((data_a, data_b), axis=0)
+
+kmeans = KMeans(n_clusters=2)
+kmeans.fit(data)
+
+
+plt.plot(a_x, a_y, "c.")
+plt.plot(b_x, b_y, "m.")
+plt.plot(
+    kmeans.cluster_centers_[0, 0], kmeans.cluster_centers_[0, 1], "k*", markersize=12
+)
+plt.plot(
+    kmeans.cluster_centers_[1, 0], kmeans.cluster_centers_[1, 1], "k*", markersize=12
+)
+
+plt.show()
+```
+
+![image1](image1.png)
+
+