diff --git a/demonstration/Dissertation/memory.py b/demonstration/Dissertation/memory.py
new file mode 100644
index 0000000..039e9ea
--- /dev/null
+++ b/demonstration/Dissertation/memory.py
@@ -0,0 +1,58 @@
+import pandas as pd
+import numpy as np
+import os
+import time
+import copy
+import pathlib, tempfile
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+# sns.set()
+
+custom_params = {"axes.spines.right": False, 'grid.color': 'lightgray', 'axes.grid': True, "axes.spines.top": False}
+sns.set_theme(style="ticks", rc=custom_params)
+
+from graphviz import Digraph
+from joblib import Parallel, delayed
+from scipy import stats
+
+from survivors import metrics as metr
+from survivors import constants as cnt
+from survivors import criteria as crit
+from numba import njit, jit, int32, float64
+from lifelines import KaplanMeierFitter, NelsonAalenFitter
+
+import survivors.datasets as ds
+
+import cProfile
+import pstats
+
+
+from sklearn.model_selection import train_test_split, StratifiedKFold
+from survivors.experiments.grid import generate_sample, prepare_sample, count_metric
+
+# X, y, features, categ, sch_nan = ds.load_actg_dataset()
+X, y, features, categ, sch_nan = ds.load_smarto_dataset()
+# X, y, features, categ, sch_nan = ds.load_wuhan_dataset()
+
+qs = np.quantile(y["time"], np.linspace(0.2, 0.8, 4))
+time_discr = np.searchsorted(qs, y["time"])
+
+discr = np.char.add(time_discr.astype(str), y["cens"].astype(str))
+X_TR, X_HO = train_test_split(X, stratify=discr, test_size=0.33, random_state=42)
+X_tr, y_tr, X_HO, y_HO, bins_HO = prepare_sample(X, y, X_TR.index, X_HO.index)
+
+from memory_profiler import memory_usage
+from survivors.tree import CRAID
+import sys
+
+p = {'balance': None, 'categ': categ, 'criterion': 'wilcoxon', 'cut': False, 'depth': 10, 
+     'ens_metric_name': 'IBS_REMAIN', 'l_reg': 0.0, 'leaf_model': 'base_zero_after', 
+     'max_features': 0.9, 'min_samples_leaf': 0.01, 'n_jobs': 1, 'n_jobs_loop': 1, 'signif': 1, 'woe': True}
+
+def train_model():
+    tree = CRAID(**p)
+    tree.fit(X_tr, y_tr)
+
+if __name__ == "__main__":
+    train_model()
\ No newline at end of file