Update app.py

app.py

@@ -97,7 +97,7 @@ def main():
     if app_mode == "Instructions":
         st.write("Welcome to the Markov Chain Classifier app!")
         # st.write("This app allows you to generate user visit history, train a Markov Chain Classifier model, and predict conversion.")
-        st.write("This app allows you to generate user visit history, train a Markov Chain Classifier model, and predict conversion.")
+        st.write("This app allows you to generate user visit history and predict conversion of the visitor into customer.")
         st.write("To get started, use the sidebar to navigate to the desired functionality.")
         st.write("1. **Generate User Visit History**: Select the number of users and conversion rate, and click the 'Generate' button to generate user visit history.")
         # st.write("2. **Train Model**: Upload an ML config file using the file uploader, and click the 'Train' button to train the Markov Chain Classifier model.")
@@ -170,4 +170,10 @@ def main():
         #             st.write(f"UserID: {user_id}, Prediction: Visitor is unlikely to convert into a customer.")
 
 if __name__ == "__main__":
-    main()
+    main()
+
+# Description of MarkovChainClassifier
+mcclf_description = "The MarkovChainClassifier is a machine learning classifier that utilizes the concept of Markov chains for prediction. Markov chains are mathematical models that represent a system where the future state of the system depends only on its current state, and not on the previous states. The MarkovChainClassifier uses this concept to make predictions by modeling the transition probabilities between different states or categories in the input data. It captures the probabilistic relationships between variables and uses them to classify new data points into one or more predefined categories. The MarkovChainClassifier can be useful in scenarios where the data has a sequential or time-dependent structure, and the relationships between variables can be modeled as Markov chains. It can be applied to various tasks, such as text classification, speech recognition, recommendation systems, and financial forecasting."
+
+# Display the description in Streamlit app
+st.write(mcclf_description)