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---
inference: false
---
---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: pickle
model_file: model.pkl
widget:
structuredData:
Age at enrollment:
- 20
- 19
- 19
Application mode:
- 8
- 6
- 1
Application order:
- 5
- 1
- 5
Course:
- 2
- 11
- 5
Curricular units 1st sem (approved):
- 0
- 6
- 0
Curricular units 1st sem (credited):
- 0
- 0
- 0
Curricular units 1st sem (enrolled):
- 0
- 6
- 6
Curricular units 1st sem (evaluations):
- 0
- 6
- 0
Curricular units 1st sem (grade):
- 0.0
- 14.0
- 0.0
Curricular units 1st sem (without evaluations):
- 0
- 0
- 0
Curricular units 2nd sem (approved):
- 0
- 6
- 0
Curricular units 2nd sem (credited):
- 0
- 0
- 0
Curricular units 2nd sem (enrolled):
- 0
- 6
- 6
Curricular units 2nd sem (evaluations):
- 0
- 6
- 0
Curricular units 2nd sem (grade):
- 0.0
- 13.666666666666666
- 0.0
Curricular units 2nd sem (without evaluations):
- 0
- 0
- 0
Daytime/evening attendance:
- 1
- 1
- 1
Debtor:
- 0
- 0
- 0
Displaced:
- 1
- 1
- 1
Educational special needs:
- 0
- 0
- 0
Father's occupation:
- 10
- 4
- 10
Father's qualification:
- 10
- 3
- 27
GDP:
- 1.74
- 0.79
- 1.74
Gender:
- 1
- 1
- 1
Inflation rate:
- 1.4
- -0.3
- 1.4
International:
- 0
- 0
- 0
Marital status:
- 1
- 1
- 1
Mother's occupation:
- 6
- 4
- 10
Mother's qualification:
- 13
- 1
- 22
Nacionality:
- 1
- 1
- 1
Previous qualification:
- 1
- 1
- 1
Scholarship holder:
- 0
- 0
- 0
Tuition fees up to date:
- 1
- 0
- 0
Unemployment rate:
- 10.8
- 13.9
- 10.8
---
# Model description
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|---------|
| bootstrap | True |
| ccp_alpha | 0.0 |
| class_weight | |
| criterion | gini |
| max_depth | |
| max_features | sqrt |
| max_leaf_nodes | |
| max_samples | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 1 |
| min_samples_split | 2 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_jobs | |
| oob_score | False |
| random_state | |
| verbose | 0 |
| warm_start | False |
</details>
### Model Plot
<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestClassifier</label><div class="sk-toggleable__content"><pre>RandomForestClassifier()</pre></div></div></div></div></div>
## Evaluation Results
| Metric | Value |
|----------|---------|
| accuracy | 0.9041 |
| roc_auc | 0.9157 |
# How to Get Started with the Model
```py
import joblib
from skops.hub_utils import download
download("sulpha/student_academic_success", "path_to_folder")
model = joblib.load(
"model.pkl"
)
```
# Model Card Authors
This model card is written by following authors:
@sulpha
# Model Card Contact
You can contact the model card authors through following channels:
github.com/sulphatet
# Citation
Below you can find information related to citation.
**BibTeX:**
```
Valentim Realinho, Jorge Machado, Luís Baptista, & Mónica V. Martins. (2021). Predict students' dropout and academic success (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5777340
```
# model_description
This is a RandomForest Classifier trained on student academic performance data.
# limitations
This model is trained for educational purposes.
# Confusion Matrix
|