Add 'train.py'

train.py

@@ -0,0 +1,21 @@
+train_data_x = pandas.read_csv('./X_train.csv')
+
+
+games_all = train_data_x.copy()
+games_predict = train_data_x.pop('User_Score')
+normalize = layers.Normalization()
+normalize.adapt(games_all)
+
+norm_games_model = tensorflow.keras.Sequential([
+  normalize,
+  layers.Dense(64),
+  layers.Dense(1)
+])
+
+norm_games_model.compile(
+    loss=tensorflow.keras.losses.MeanSquaredError(),
+    optimizer=tensorflow.keras.optimizers.Adam())
+
+norm_games_model.fit(games_all, games_predict, epochs=500)
+
+norm_games_model.save('test')