diff --git a/machine_learning/support_vector_machines.py b/machine_learning/support_vector_machines.py
deleted file mode 100644
index c5e5085d8..000000000
--- a/machine_learning/support_vector_machines.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from sklearn import svm
-from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
-
-
-# different functions implementing different types of SVM's
-def NuSVC(train_x, train_y):
-    svc_NuSVC = svm.NuSVC()
-    svc_NuSVC.fit(train_x, train_y)
-    return svc_NuSVC
-
-
-def Linearsvc(train_x, train_y):
-    svc_linear = svm.LinearSVC(tol=10e-2)
-    svc_linear.fit(train_x, train_y)
-    return svc_linear
-
-
-def SVC(train_x, train_y):
-    # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,
-    # probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,
-    # max_iter=1000, random_state=None)
-    # various parameters like "kernel","gamma","C" can effectively tuned for a given
-    # machine learning model.
-    SVC = svm.SVC(gamma="auto")
-    SVC.fit(train_x, train_y)
-    return SVC
-
-
-def test(X_new):
-    """
-    3 test cases to be passed
-    an array containing the sepal length (cm), sepal width (cm), petal length (cm),
-    petal width (cm) based on which  the target name will be predicted
-    >>> test([1,2,1,4])
-    'virginica'
-    >>> test([5, 2, 4, 1])
-    'versicolor'
-    >>> test([6,3,4,1])
-    'versicolor'
-    """
-    iris = load_iris()
-    # splitting the dataset to test and train
-    train_x, test_x, train_y, test_y = train_test_split(
-        iris["data"], iris["target"], random_state=4
-    )
-    # any of the 3 types of SVM can be used
-    # current_model=SVC(train_x, train_y)
-    # current_model=NuSVC(train_x, train_y)
-    current_model = Linearsvc(train_x, train_y)
-    prediction = current_model.predict([X_new])
-    return iris["target_names"][prediction][0]
-
-
-if __name__ == "__main__":
-    import doctest
-
-    doctest.testmod()