| import streamlit as st |
|
|
| st.title("✈️ Flight Ticket Price Prediction App") |
| st.subheader("Welcome to my Machine Learning Project!") |
|
|
| st.markdown(""" |
| ## 📌 Project Introduction: |
| Welcome to the **Flight Ticket Price Prediction App**. |
| In this project, I have developed a machine learning model to predict flight ticket prices based on factors such as airline, source, destination, stops, travel class, duration, and more. |
| |
| I experimented with **multiple regression algorithms** including: |
| - **Linear Regression** |
| - **K-Nearest Neighbors (KNN)** |
| - **Decision Tree** |
| - **Random Forest** |
| - **XGBoost** |
| |
| After comparing their performance using **MAE, RMSE, and R² scores**, the **Random Forest Regressor** was selected as the final model for deployment due to its highest accuracy and lowest errors. |
| |
| --- |
| |
| ### 📚 Project Overview: |
| - Built using Python and Streamlit |
| - Deployed on Hugging Face Spaces |
| - Machine Learning Algorithm: **Random Forest Regressor** |
| |
| --- |
| |
| ### 👨💻 Project by: |
| **Srikanth Gunti** |
| Postgraduate Data Science Student |
| Nizam College, Osmania University |
| |
| --- |
| """) |
|
|
| if st.button('Next'): |
| st.switch_page('pages/1_Problem_Statement.py') |
