Update app.py

app.py

@@ -8,10 +8,10 @@ warnings.simplefilter("ignore", UserWarning)
 MODEL = pickle.load(open('IF_model_anomaly.pkl','rb'))
 
 st.title("Retail Anomaly")
-st.write(""" Anomaly detection (or outlier detection) is the identification of rare items, events or observations which raise suspicions by
-differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such
-as e.g. bank fraud, medical problems, structural defects, malfunctioning equipment etc. This connection makes it very interesting to be able 
-to pick out which data points can be considered anomalies, as identifying these events are typically very interesting from a business perspective.
+st.write(""" An anomaly (also known as an outlier) is when something happens that is outside of the norm, when it stands out or deviates from what is expected. 
+There are different kinds of anomalies in an e-commerce setting, they can be product anomaly, conversion anomaly or marketing anomaly.
+The model used is Isolation Forest, which is built based on decision trees and is an unsupervised model.
+Isolation forests can be used to detect anomaly in high dimensional and large datasets, with no labels.
 """)
 
 def prediction(sales,model):