Update pages/0_Problem Statement.py

pages/0_Problem Statement.py

@@ -2,7 +2,7 @@ import streamlit as st
 import pandas as pd 
 import numpy as np 
 
-st.markdown("<h1 style='text-align:center; color:red;'>Problem Statement</h1>",unsafe_allow_html=True)
+st.markdown("<h1 style='text-align:center; color:#00FFFF;'>Problem Statement</h1>",unsafe_allow_html=True)
 
 
 # Title of the Streamlit app
@@ -11,18 +11,19 @@ st.header("Predicting Agoda Room Categories Using Machine Learning")
 # Problem statement section
 st.subheader("Problem Statement")
 st.write("Here, describe the problem statement and the objectives of the project.")
-# Text explaining the problem
-st.write("""
-    **Title:** Predicting Agoda Room Categories Using Machine Learning
-    
-    **Context:** Agoda, a leading online travel booking platform, offers a variety of accommodation options. Each room is categorized based on various features like customer ratings, reviews, cashback offers, discounts, state, and price. Accurately predicting the room category can enhance user experience by recommending relevant options and improving operational efficiency.
-    
-    **Problem Statement:** The goal is to build a predictive model that can classify room listings into predefined categories (e.g., budget, standard, premium, luxury) using the provided features. The challenge lies in choosing the best machine learning model and its hyperparameters to minimize classification error, particularly focusing on log-loss as the evaluation metric.
+st.markdown("""
+**Title:** Predicting Agoda Room Categories Using Machine Learning
+
+**Context:**  
+Agoda, a leading online travel booking platform, offers a variety of accommodation options. Each room is categorized based on various features like customer ratings, reviews, cashback offers, discounts, state, and price. Accurately predicting the room category can enhance user experience by recommending relevant options and improving operational efficiency.
+
+**Problem Statement:**  
+The goal is to build a predictive model that can classify room listings into predefined categories (e.g., budget, standard, premium, luxury) using the provided features. The challenge lies in choosing the best machine learning model and its hyperparameters to minimize classification error, particularly focusing on log-loss as the evaluation metric.
 """)
 
 st.markdown("![Problem_Statement-768x432.jpg](https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/4WmVeJ5lMh1PoSlCYWKp9.jpeg)")
 # Define the URL of the background image (use your own image URL)
-background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/uuRffDOdqb_CQPlKm3_J5.jpeg"
+background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/hIrxQZQVeTsFIegm32tR7.jpeg"
 
 # Apply custom CSS for the background image and overlay
 st.markdown(
@@ -49,7 +50,7 @@ st.markdown(
 
          /* Styling the content to ensure text visibility */
         .stMarkdown {{
-            color: black;  /* White text to ensure visibility */
+            color: white;  /* White text to ensure visibility */
             font-size: 30px;  /* Adjust font size for better readability */
         }}
     </style>