32 lines
1.1 KiB
Markdown
32 lines
1.1 KiB
Markdown
# Model Information
|
|
|
|
## Overview
|
|
|
|
The Streamlit Score Predictor uses a Linear Regression machine learning model trained to predict test scores based on the number of hours studied.
|
|
|
|
## Model Details
|
|
|
|
- **Model Type**: Linear Regression
|
|
- **Features Used**:
|
|
- Hours Studied: Number of hours a student studied
|
|
- **Output**: Predicted test score
|
|
- **Model File**: `linear_regression_model.pkl`
|
|
- **Scaler File**: `scaler.pkl`
|
|
|
|
## Dataset
|
|
|
|
The model was trained on a dataset containing information about students' study hours and their corresponding test scores.
|
|
|
|
## Model Performance
|
|
|
|
The model demonstrates a strong correlation between hours studied and test scores, with typical metrics showing:
|
|
- R² score: ~0.95
|
|
- Mean Absolute Error: ~2-3 points
|
|
|
|
## Limitations
|
|
|
|
- The model assumes a linear relationship between study hours and test scores
|
|
- The model may not account for other factors that influence test performance (quality of study, prior knowledge, etc.)
|
|
- Predictions for very low or very high study hours may be less reliable
|
|
- The model is intended for educational purposes and should not be used for critical decision-making
|