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license: apache-2.0 |
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pipeline_tag: tabular-regression |
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# Student Performance Prediction Model |
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## Model Description |
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This model is trained to predict student performance based on various socio-economic and academic factors. It uses a regression approach to estimate the final grades of students. |
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## Dataset |
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The model was trained using the **Student Performance Predictions Dataset** from Kaggle, which includes features such as: |
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- Study time |
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- Parent education level |
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- Previous grades |
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- Absences |
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You can find the dataset [here](https://www.kaggle.com/datasets/student-performance). |
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## Training |
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The model was trained using the following configuration: |
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- **Library**: TensorFlow/Keras |
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- **Model Type**: Regression |
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- **Evaluation Metrics**: Mean Absolute Error (MAE) |
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## Results |
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The model's performance was evaluated using the validation loss (**val_loss**), which was calculated as the **Mean Absolute Error (MAE)**. The model achieved a **MAE** of X on the validation dataset. |
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## Metrics |
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The model was evaluated using **Mean Absolute Error (MAE)** on the validation set, achieving a MAE score of [your score here]. |
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## How to Use |
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You can load the model and use it for prediction as follows: |
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```python |
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from tensorflow.keras.models import load_model |
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model = load_model("student_performance_model.h5") |
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# Use the model for prediction |
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