# Importing Streamlit for building the web-based interactive application framework import streamlit as st # Function to Display Model Performance def display_hybrid_model_performance(): # Basic HTML table without additional styling html_content = """
This table presents the performance of various regression models for different companies.
Model | MAE | MSE | RMSE | R² Score | Accuracy (%) |
---|---|---|---|---|---|
MLP | 4.631412 | 30.276025 | 5.502365 | 0.583915 | 99.863574 |
Linear Regression | 5.725293 | 39.196283 | 6.260694 | 0.461323 | 100.000000 |
K-Nearest Neighbors (KNN) | 5.575181 | 41.867105 | 6.470479 | 0.424618 | 100.000000 |
SVM | 5.771079 | 47.432490 | 6.887125 | 0.348132 | 99.454297 |
Gradient Boosting | 6.189361 | 52.197185 | 7.224762 | 0.282651 | 100.000000 |
Random Forest | 6.378920 | 60.091005 | 7.751839 | 0.174165 | 100.000000 |
Decision Tree | 6.405307 | 61.140660 | 7.819249 | 0.159740 | 100.000000 |
AdaBoost | 7.360769 | 77.328544 | 8.793665 | -0.062731 | 100.000000 |