# Importing Streamlit for building the web-based interactive application framework import streamlit as st # Function to Display Model Performance def display_numerical_model_performance(): # HTML content html_content = """
Regression Model Performance Overview

This table presents the performance of various regression models for different companies. We have utilized an ensemble model approach to enhance prediction accuracy.

Company Linear Regression Ridge Regression Lasso Regression Elastic Net Regression
Apple (AAPL) 0.9998629224782566 0.9998630076573156 0.9993577928176817 0.9996487306800625
Amazon (AMZN) 0.999084553005321 0.9961820949085249 0.9961820949085249 0.9965084504390407
Google (GOOG) 0.9992315300913325 0.9992317269769614 0.9973035403402394 0.9973876081990091
Microsoft (MSFT) 0.9992315300913325 0.9998440164467663 0.9993642265769165 0.9994838523467628
Meta (META) 0.9992315300913325 0.9992317269769614 0.9973035403402394 0.9973876081990091
Netflix (NFLX) 0.9992198305734726 0.9992198288782033 0.9971600744221598 0.9971700356054888
NVIDIA (NVDA) 0.9971063606802831 0.998365397592374 0.9940339650281499 0.9972701576306695
Tata Consultancy Services (TCS) 0.9979612118835919 0.9979612934362246 0.9890586523727406 0.9891227853737217
""" # Display the HTML content st.markdown(html_content, unsafe_allow_html=True)