diff --git a/requirements.txt b/requirements.txt
index 663bd1f..d5d80e1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1,3 @@
-requests
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+requests
+sklearn
+plotly.express
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diff --git a/scripts/algorithm.py b/scripts/algorithm.py
new file mode 100644
index 0000000..0403dba
--- /dev/null
+++ b/scripts/algorithm.py
@@ -0,0 +1,39 @@
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, confusion_matrix
+import numpy as np
+
+import logging
+logger = logging.getLogger()
+
+
+def make_labels(X):
+  return np.hstack([k*np.ones(len(X[k]), dtype=int) for k in range(len(X))])
+
+
+class Model:
+  def __init__(self, nb_tree_per_forest=50, max_depth=10):
+    # Create a random forest model
+    self.model = RandomForestClassifier(n_estimators=nb_tree_per_forest, max_depth=max_depth,
+                                        random_state=0)
+
+
+  def train(self, X):
+
+    # Get features
+    X_features = np.vstack([k for k in X])
+
+    # Get labels
+    y = make_labels(X)
+
+    # Train the model
+    self.model.fit(X_features, y)
+
+
+  def predict(self, X):
+    # Get features
+    X_features = np.vstack([k for k in X])
+
+    # Predict using the trained model
+    prediction = self.model.predict(X_features)
+
+    return prediction
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diff --git a/scripts/analysis.py b/scripts/analysis.py
new file mode 100644
index 0000000..168dda7
--- /dev/null
+++ b/scripts/analysis.py
@@ -0,0 +1,28 @@
+import plotly.express as px
+from sklearn.metrics import confusion_matrix
+import numpy as np
+import itertools
+
+def get_confusion_matrix(prediction_label, true_label):
+    return confusion_matrix(true_label, prediction_label)
+
+def plot_confusion_matrix(cm, classes,
+                          normalize=False,
+                          title='Confusion matrix'):
+    """
+    This function prints and plots the confusion matrix.
+    Normalization can be applied by setting `normalize=True`.
+    """
+    # Normalize if wanted
+    if normalize:
+        cm = cm / np.sum(cm)
+
+    # Create confusion matrix graph
+    fig = px.imshow(cm,
+                    labels=dict(x="Predicted label", y="True label", color="value"),
+                    x=classes,
+                    y=classes,
+                    color_continuous_scale='Blues',
+                    title=title
+                )
+    fig.show()
diff --git a/scripts/test.py b/scripts/test.py
new file mode 100644
index 0000000..93f926f
--- /dev/null
+++ b/scripts/test.py
@@ -0,0 +1,51 @@
+import algorithm
+import database
+import analysis
+import numpy as np
+
+def test1():
+  x = np.arange(len(database.CLASSES))
+  cm = np.dot(x.reshape(len(x),1), x.reshape(1,len(x)))
+  print(cm)
+  print(database.CLASSES)
+  analysis.plot_confusion_matrix(cm, database.CLASSES)
+
+def test():
+
+  # Import train data
+  train = database.get("proto1", 'train')
+  #print(train)
+
+  # Prepare train data
+  # norm = preprocessor.estimate_norm(numpy.vstack(train))
+  # train_normed = preprocessor.normalize(train, norm)
+  
+  # Train algo
+  model = algorithm.Model()
+  model.train(train)
+
+  # Import test data
+  test = database.get('proto1', 'test')
+  
+  # Prepare test data
+  # test_normed = preprocessor.normalize(test, norm)
+  
+  # Make prediction
+  test_predictions = model.predict(test)
+  print(test_predictions)
+
+  # Get real labels 
+  test_labels = algorithm.make_labels(test).astype(int)
+
+  # Get confusion matrix
+  cm = analysis.get_confusion_matrix(test_predictions, test_labels)
+
+  # Plot confusion matrix
+  analysis.plot_confusion_matrix(cm, database.CLASSES, normalize=True)
+
+  # Plot confusion matrix (ignore other activities)
+  # analysis.plot_confusion_matrix(cm[1:, 1:], database.CLASSES[1:], normalize=True)
+
+
+if __name__ == '__main__':
+  test()
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