Update pages/Life cycle of ML.py

pages/Life cycle of ML.py

@@ -0,0 +1,81 @@
+import streamlit as st
+
+def home_page():
+    st.title("Machine Learning Lifecycle")
+    st.write("Explore the stages of a Machine Learning Project Lifecycle.")
+
+    # Buttons for navigation
+    if st.button("1. Problem Definition"):
+        st.experimental_set_query_params(page="problem_definition")
+    if st.button("2. Data Collection"):
+        st.experimental_set_query_params(page="data_collection")
+    if st.button("3. Data Preparation"):
+        st.experimental_set_query_params(page="data_preparation")
+    if st.button("4. Modeling"):
+        st.experimental_set_query_params(page="modeling")
+    if st.button("5. Evaluation"):
+        st.experimental_set_query_params(page="evaluation")
+    if st.button("6. Deployment"):
+        st.experimental_set_query_params(page="deployment")
+
+    st.image("https://via.placeholder.com/800x400?text=ML+Lifecycle", caption="Visual Representation of the ML Lifecycle")
+
+def problem_definition_page():
+    st.title("1. Problem Definition")
+    st.write("Define the problem you are solving with your machine learning model.")
+    st.info("Key Questions: What is the goal? What are the metrics?")
+    st.button("Back to Home", on_click=lambda: st.experimental_set_query_params(page="home"))
+
+def data_collection_page():
+    st.title("2. Data Collection")
+    st.write("Gather data relevant to the problem.")
+    st.info("Focus: Data quality, diversity, and sources.")
+    st.button("Back to Home", on_click=lambda: st.experimental_set_query_params(page="home"))
+
+def data_preparation_page():
+    st.title("3. Data Preparation")
+    st.write("Clean and preprocess the data for analysis.")
+    st.info("Focus: Missing values, normalization, and splitting.")
+    st.button("Back to Home", on_click=lambda: st.experimental_set_query_params(page="home"))
+
+def modeling_page():
+    st.title("4. Modeling")
+    st.write("Train and tune machine learning models.")
+    st.info("Focus: Algorithms, hyperparameters, and feature selection.")
+    st.button("Back to Home", on_click=lambda: st.experimental_set_query_params(page="home"))
+
+def evaluation_page():
+    st.title("5. Evaluation")
+    st.write("Evaluate model performance using metrics.")
+    st.info("Key Metrics: Accuracy, precision, recall, F1-score, etc.")
+    st.button("Back to Home", on_click=lambda: st.experimental_set_query_params(page="home"))
+
+def deployment_page():
+    st.title("6. Deployment")
+    st.write("Deploy the model to production for real-world use.")
+    st.info("Methods: APIs, dashboards, and live systems.")
+    st.button("Back to Home", on_click=lambda: st.experimental_set_query_params(page="home"))
+
+def main():
+    query_params = st.experimental_get_query_params()
+    page = query_params.get("page", ["home"])[0]
+
+    if page == "home":
+        home_page()
+    elif page == "problem_definition":
+        problem_definition_page()
+    elif page == "data_collection":
+        data_collection_page()
+    elif page == "data_preparation":
+        data_preparation_page()
+    elif page == "modeling":
+        modeling_page()
+    elif page == "evaluation":
+        evaluation_page()
+    elif page == "deployment":
+        deployment_page()
+    else:
+        st.error("Page not found!")
+
+if __name__ == "__main__":
+    main()