--- a
+++ b/conditional_probability/logistic.py
@@ -0,0 +1,50 @@
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import roc_auc_score
+from util import roc_results, results
+
+
+def lr_training(x_train, y_train):
+    """
+    :param x_train: the x-values we want to train on (2D numpy array)
+    :param y_train: the y-values that correspond to x_train (1D numpy array)
+    :return: sklearn LogisticRegression object that can now be used for predictions
+    """
+    prob = LogisticRegression(penalty='l1', C=0.185, solver='saga', fit_intercept=True, random_state=0)
+    prob.fit(x_train, y_train)
+    return prob
+
+
+def lr_probability(prob, x_test):
+    """
+    :param prob: trained sklearn LogisticRegression object
+    :param x_test: the x-values we want to get predictions on (2D numpy array)
+    :return: a 2D numpy array containing the probabilities for both classes
+    """
+    return prob.predict_proba(x_test)
+
+
+def lr_classification(y_pred, threshold=0.5):
+    """
+    :param y_pred: a 1D numpy array containing the probabilities of x belonging to the positive class
+    :param threshold: determines which class a probability estimate belongs to
+    :return: a 1D numpy array containing the predictions
+    """
+    for i in range(y_pred.shape[0]):
+        y_pred[i] = 1 if y_pred[i] > threshold else -1
+    return y_pred
+
+
+def lr_pipeline(x_train, y_train, x_test, y_test):
+    """
+    :param x_train: the x-values we want to train on (2D numpy array)
+    :param y_train: the y-values that correspond to x_train (1D numpy array)
+    :param x_test:  the x-values we want to test on (2D numpy array)
+    :param y_test:  the y-values that correspond to x_test (1D numpy array)
+    :return: the roc auc score
+    """
+    prob = lr_training(x_train, y_train)
+    y_pred = lr_probability(prob, x_test)
+    y_pred_class = lr_classification(np.copy(y_pred[:, 1]), threshold=0.436)
+    roc_results(y_pred[:, 1], y_test, 'Logistic Regression')
+    return roc_auc_score(y_test, y_pred[:, 1]), results(y_pred_class, y_test)