| import streamlit as st |
|
|
| st.title("📄 Problem Statement") |
|
|
| st.markdown(""" |
| ## Problem Statement: |
| The airline industry is highly dynamic, with flight ticket prices varying significantly based on multiple factors such as airline, source and destination cities, number of stops, class of travel, travel duration, booking time, and more. |
| |
| Travellers often face challenges in finding the best time to book flights at an affordable price. |
| Similarly, airlines need to understand pricing trends to optimize their ticket pricing strategies. |
| |
| --- |
| |
| ## Objective: |
| The objective of this project is to: |
| - **Develop a Machine Learning model** that can predict the price of flight tickets based on user-selected features. |
| - **Provide an easy-to-use Streamlit web application** where users can input their travel preferences and get an estimated flight price instantly. |
| - Help users make informed decisions while booking flights. |
| - Demonstrate the end-to-end process of building, training, and deploying a regression model. |
| |
| --- |
| |
| ## Why is this important? |
| - Supports better travel planning for customers. |
| - Offers insights into how various features influence flight prices. |
| - Provides a practical demonstration of predictive modeling using real-world data. |
| |
| --- |
| """) |
|
|
| if st.button('Next'): |
| st.switch_page('pages/2_Data_Understanding.py') |
|
|
| if st.button('Go to Home Page'): |
| st.switch_page('app.py') |
