diff --git a/smatejcek.py b/smatejcek.py
new file mode 100644
index 0000000..b6128a7
--- /dev/null
+++ b/smatejcek.py
@@ -0,0 +1,31 @@
+import pandas as pd
+from sklearn import tree
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import cross_val_score
+
+df = pd.read_csv("data.csv")
+
+df["Gender"] = df["Sex"].map({"male":1, "female":0})
+df["Embark_Numeric"] = df["Embarked"].map({"S":1, "C":2, "Q":3})
+
+y_target = df["Survived"]
+X_data = df[["Gender", "SibSp", "Fare"]]
+
+model = tree.DecisionTreeClassifier().fit(X_data, y_target) # X and then y
+X_train, X_test, y_train, y_test = train_test_split(X_data, y_target, test_size = 0.33, random_state = 42)
+
+print("TRAIN SCORE: {}".format(round(model.score(X_train, y_train), 6)))
+# Train Score = 0.919463
+
+print("TEST SCORE: {}".format(round(model.score(X_test, y_test), 6)))
+# Test Score = 0.911864
+
+print("\nBraund, Mr. Owen Harris Survied? {}".format(1 in model.predict([[1, 1, 7.25]])))
+# Predicts Mr. Braund did not survive; and he did not.
+
+print("R2 Cross Validation Score: {}".format(cross_val_score(model, X_data, y_target, cv = 10, scoring = "r2")))
+# Returns: [-0.02857143 -0.02857143  0.00053476 -0.09465241  0.52406417  0.38128342, 0.14331551  0.23850267  0.04812834  0.1372549 ]
+
+print("Accuracy Cross Validation Score: {}".format(cross_val_score(model, X_data, y_target, cv = 10, scoring = "accuracy")))
+# Returns: [ 0.75555556  0.75555556  0.7752809  0.74157303  0.88764045  0.85393258  0.78651685  0.82022472  0.7752809  0.79545455]