--- a
+++ b/Stats/_LogisticRegression.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+#
+# Copyright 2017 University of Westminster. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+""" It is an interface for the 'LogisticRegression' training model (Logistic Regression).
+"""
+
+from typing import Dict, List, Any, TypeVar
+from Stats.Stats import Stats
+from sklearn import linear_model
+
+PandasDataFrame = TypeVar('DataFrame')
+SklearnLogisticRegression = TypeVar('LogisticRegression')
+
+__author__ = "Mohsen Mesgarpour"
+__copyright__ = "Copyright 2016, https://github.com/mesgarpour"
+__credits__ = ["Mohsen Mesgarpour"]
+__license__ = "GPL"
+__version__ = "1.1"
+__maintainer__ = "Mohsen Mesgarpour"
+__email__ = "mohsen.mesgarpour@gmail.com"
+__status__ = "Release"
+
+
+class _LogisticRegression(Stats):
+    def __init__(self):
+        """Initialise the objects and constants.
+        """
+        super(self.__class__, self).__init__()
+        self._logger.debug(__name__)
+        self._logger.debug("Run Logistic Regression.")
+
+    def train(self,
+              features_indep_df: PandasDataFrame,
+              feature_target: List,
+              model_labals: List=[0, 1],
+              **kwargs: Any) -> SklearnLogisticRegression:
+        """Perform the training, using the Logistic Regression.
+        :param features_indep_df: the independent features, which are inputted into the model.
+        :param feature_target: the target feature, which is being estimated.
+        :param model_labals: the target labels (default [0, 1]).
+        :param kwargs: penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1,
+        class_weight=None, random_state=None, solver='liblinear', max_iter=100, multi_class='ovr',
+        verbose=0, warm_start=False, n_jobs=-1
+        :return: the trained model.
+        """
+        self._logger.debug("Train " + __name__)
+        model_train = linear_model.LogisticRegression(**kwargs)
+        model_train.fit(features_indep_df.values, feature_target)
+        return model_train
+
+    def train_summaries(self,
+                        model_train: SklearnLogisticRegression) -> Dict:
+        """Produce the training summary.
+        :param model_train: the instance of the trained model.
+        :return: the training summary.
+        """
+        self._logger.debug("Summarise " + __name__)
+        summaries = dict()
+        # Coefficient of the features in the decision function.
+        summaries["coef_"] = model_train.coef_
+        # Intercept (a.k.a. bias) added to the decision function.
+        summaries["intercept_"] = model_train.intercept_
+        # Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element.
+        summaries["n_iter_"] = model_train.n_iter_
+        return summaries
+
+    def plot(self,
+             model_train: SklearnLogisticRegression,
+             feature_names: List,
+             class_names: List=["True", "False"]):
+        """Plot the tree diagram.
+        :param model_train: the instance of the trained model.
+        :param feature_names: the names of input features.
+        :param class_names: the predicted class labels.
+        :return: the model graph.
+        """
+        self._logger.debug("Plot " + __name__)
+        # todo: plot
+        pass