diff --git a/scikit-learn/svm/README.md b/scikit-learn/svm/README.md
new file mode 100644
index 0000000..e72a5f1
--- /dev/null
+++ b/scikit-learn/svm/README.md
@@ -0,0 +1,62 @@
+# Supprt Vector Machine
+{:.no_toc}
+
+<nav markdown="1" class="toc-class">
+* TOC
+{:toc}
+</nav>
+
+## Top
+
+Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
+
+
+## [sklearn.svm.SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)
+
+```python
+sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)
+```
+
+> **kernel** : {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’
+> 
+> Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).
+
+### [fit](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.fit)
+
+```python
+fit(X, y, sample_weight=None)
+```
+
+> Fit the SVM model according to the given training data.
+>
+> **X** : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)
+> 
+> Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
+>
+> **y** : array-like of shape (n_samples,)
+> 
+> Target values (class labels in classification, real numbers in regression).
+
+### [predict](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.predict)
+
+```python
+predict(X)
+```
+> For an one-class model, +1 or -1 is returned.
+>
+> Parameters:
+> 
+> **X** : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train)
+>
+> Returns:
+> 
+> **y_pred** : ndarray of shape (n_samples,)
+> 
+> Class labels for samples in X.
+
+```python
+```
+
+
+```shell
+```