# Importing Streamlit for building the web-based interactive application framework import streamlit as st # Function to Display Model Performance def display_text_model_performance(): # Basic HTML table without additional styling html_content = """
Regression Model Performance Overview

This table presents the performance of various regression models for different companies.

Metric Logistic Regression Naive Bayes Random Forest
Accuracy 0.6516 0.6672 0.9900
Class 0 Precision 0.62 0.65 0.99
Class 0 Recall 0.82 0.77 1.00
Class 0 F1-Score 0.71 0.70 0.99
Class 0 Support 2395 2395 2395
Class 1 Precision 0.72 0.70 1.00
Class 1 Recall 0.48 0.56 0.98
Class 1 F1-Score 0.57 0.62 0.99
Class 1 Support 2304 2304 2304
Macro Avg Precision 0.67 0.67 0.99
Macro Avg Recall 0.65 0.67 0.99
Macro Avg F1-Score 0.64 0.66 0.99
Weighted Avg Precision 0.67 0.67 0.99
Weighted Avg Recall 0.65 0.67 0.99
Weighted Avg F1-Score 0.64 0.66 0.99
Total Support 4699 4699 4699
""" # Display the HTML content st.markdown(html_content, unsafe_allow_html=True)