Create algorithms.py

machine_learning/algorithms.py

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+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+
+class Algorithms:
+    @staticmethod
+    def k_means(X, k):
+        return KMeans(n_clusters=k).fit(X)
+
+    @staticmethod
+    def pca(X, n_components):
+        return PCA(n_components=n_components).fit(X)
+
+    @staticmethod
+    def random_forest(X, y, n_estimators=100, max_depth=None):
+        return RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth).fit(X, y)
+
+    @staticmethod
+    def logistic_regression(X, y, penalty='l2', C=1.0):
+        return LogisticRegression(penalty=penalty, C=C).fit(X, y)
+
+    @staticmethod
+    def k_nearest_neighbors(X, y, n_neighbors=5):
+        return KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)
+
+    @staticmethod
+    def svm(X, y, kernel='linear', C=1.0):
+        return SVC(kernel=kernel, C=C).fit(X, y)