diff --git a/Dockerfile b/Dockerfile
index 6691742..4cefcff 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -17,6 +17,7 @@ RUN pip3 install keras
 RUN pip3 install sklearn
 RUN pip3 install pymongo
 RUN pip3 install sacred
+RUN pip3 install mlflow
 
 CMD python3 data_expl.py
 CMD python3 nn_train.py
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diff --git a/MLProject b/MLProject
new file mode 100644
index 0000000..b6b6b10
--- /dev/null
+++ b/MLProject
@@ -0,0 +1,10 @@
+name: s444517_train
+docker_env:
+   image:  kambobdocker420/ium:mlflow
+entry_points:
+  main:
+    parameters:
+      epochs: {type: int, default: 200}
+      first_activation_funct: {type: str, default: "relu"}
+      second_activation_funct: {type: str, default: "softmax"}
+    command: "python nn_train_mlflow.py {epochs} {first_activation_funct} {second_activation_funct}"
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diff --git a/nn_train_mlflow.py b/nn_train_mlflow.py
new file mode 100644
index 0000000..cd5d4b4
--- /dev/null
+++ b/nn_train_mlflow.py
@@ -0,0 +1,88 @@
+import pandas as pd
+import numpy as np
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score
+from keras.utils import np_utils
+from tensorflow import keras
+import mlflow
+import sys
+
+mlflow.set_experiment("s444517")
+
+# reading data
+def read_data():
+    all_data = []
+    for name in ['train', 'test', 'validate']:
+        all_data.append(pd.read_csv(f'apps_{name}.csv', header=0))
+    return all_data
+
+def data_prep():
+    train_set, test_set, validate_set = read_data()
+    train_set = train_set.drop(columns=["Unnamed: 0"])
+    test_set = test_set.drop(columns=["Unnamed: 0"])
+    validate_set = validate_set.drop(columns=["Unnamed: 0"])
+    numeric_columns = ["Rating", "Reviews", "Installs", "Price", "Genres_numeric_value"]
+
+    # train set set-up
+    x_train_set = train_set[numeric_columns]
+    y_train_set = train_set["Category"]
+    encoder = LabelEncoder()
+    encoder.fit(y_train_set)
+    encoded_Y = encoder.transform(y_train_set)
+    dummy_y = np_utils.to_categorical(encoded_Y)
+
+    # validation set set-up
+    x_validate_set = validate_set[numeric_columns] 
+    y_validate_set = validate_set["Category"]
+    encoder = LabelEncoder()
+    encoder.fit(y_validate_set)
+    encoded_Yv = encoder.transform(y_validate_set)
+    dummy_yv = np_utils.to_categorical(encoded_Yv)
+
+    #test set set-up
+    x_test_set = test_set[numeric_columns]
+    y_test_set = test_set["Category"]
+    y_class_names = train_set["Category"].unique()
+    encoder = LabelEncoder()
+    encoder.fit(y_test_set)
+    encoded_Ytt = encoder.transform(y_test_set)
+    dummy_ytt = np_utils.to_categorical(encoded_Ytt)
+    return x_train_set, dummy_y, x_validate_set, dummy_yv, x_test_set,  y_test_set, y_class_names
+
+
+with mlflow.start_run():
+    epoch = int(sys.argv[1]) if len(sys.argv) > 1 else 200
+    first_activation_funct = int(sys.argv[2]) if len(sys.argv) > 2 else "relu"
+    second_activation_funct = int(sys.argv[3]) if len(sys.argv) > 3 else "softmax"
+
+    x_train_set, dummy_y, x_validate_set, dummy_yv, x_test_set,  y_test_set, y_class_names = data_prep()
+
+    number_of_classes = 33
+    number_of_features = 5
+    model = Sequential()
+    model.add(Dense(number_of_classes, activation=first_activation_funct))
+    model.add(Dense(number_of_classes, activation=second_activation_funct,input_dim=number_of_features))
+    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy', 'categorical_accuracy'])
+    model.fit(x_train_set, dummy_y, epochs=epoch, validation_data=(x_validate_set, dummy_yv))
+    
+    model.save("my_model/")
+
+    
+    #model predictions
+    yhat = model.predict(x_test_set)
+    y_true = []
+    y_pred = []
+    for numerator, single_pred in enumerate(yhat):
+        y_true.append(sorted(y_class_names)[np.argmax(single_pred)])
+        y_pred.append(y_test_set[numerator])
+
+    mlflow.log_param("epoch", epoch)
+    mlflow.log_param("1st_activation_funct", first_activation_funct)
+    mlflow.log_param("2nd_activation_funct", second_activation_funct)
+    mlflow.keras.log_model(model, 'my_model')
+    mlflow.keras.save_model(model, "my_model")
+    mlflow.log_metric("accuracy", accuracy_score(y_true, y_pred))
+    
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