Update pages/4.Life Cycle of ML.py

pages/4.Life Cycle of ML.py

@@ -1,50 +1,27 @@
 import streamlit as st
-import json
+import numpy as np 
+import pandas as pd
 
-# Streamlit: Initialize session state for navigation
-if "pages" not in st.session_state:
-    st.session_state.page = "main"
-
-# Function to navigate between pages
-def navigate_to(page_name):
-    st.session_state.page = page_name
-
-# Main Page
-if st.session_state.page == "main":
-    st.header("Welcome to the Machine Learning Project Life Cycle")
-    st.write("""
-    This application provides insights into the various stages of a Machine Learning project.
-    Navigate through the different sections to explore each stage in detail.
-    """)
-
-    # Buttons for navigation
-
-    if st.button("Problem Statement"):
-        navigate_to("Problem Statement")
-
-# Problem Statement Page
-elif st.session_state.page == "Problem Statement":
-    st.header("Problem Statement")
+st.header(":red[**Life Cycle Of Machine Learning Project**]")
+st.write(":blue[Click the button below to explore detailed steps involved in an ML project:]")
+if st.button("**Problem Statement**"):
+    st.switch_page("pages/5.Problem Statement.py")
     st.write("""
-    A problem statement in machine learning defines the specific issue to solve using data and ML techniques.
-    **Key elements:**
-    - What is the problem?
-    - Why is it important?
-    - What data is available?
-    - What is the expected outcome?
+    **A problem statement in machine learning defines the specific issue you want to solve using data and machine learning techniques. It should clearly explain:**
+    - What the problem is
+    - Why solving it is important
+    - What data is available
+    - What the expected outcome will look like
     """)
-
     st.write("""
-    **Example:**
+    **Examples of ML Problem Statements:**
     - **Predicting House Prices:**
-      - Problem: Predict house prices based on features like size, location, and number of bedrooms.
-      - Importance: Helps buyers make decisions and agents price houses accurately.
-      - Data: Historical house prices and features.
-      - Outcome: A model that predicts house prices.
+    - Problem: We want to predict the price of houses based on features like size, location, number of bedrooms, etc.
+    - Why: This helps buyers make informed decisions and real estate agents price houses correctly.
+    - Data: Historical data about house prices and their features.
+    - Expected Outcome: A model that predicts the price of a house given its features.
     """)
 
-
-
 if st.button("**Data Collection**"):
     st.switch_page("pages/6.Data Collection.py")
     st.write("""