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--- |
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license: mit |
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language: en |
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datasets: |
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- adilshamim8/social-media-addiction-vs-relationships |
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tags: |
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- tabular-data |
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- scikit-learn |
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- random-forest |
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- classification |
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- addiction |
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- social-media |
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- Linkspreed |
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- Web4 |
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- Social Networks as a Service |
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model-index: |
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- name: LS-W4-Mini-RF_Addiction_Impact |
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results: |
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- task: |
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name: Tabular Classification |
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type: tabular-classification |
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dataset: |
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name: Students Social Media Addiction |
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type: adilshamim8/social-media-addiction-vs-relationships |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.93 |
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--- |
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# LS-W4-Mini-RF_Addiction_Impact |
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## Model Summary |
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This is a **Random Forest Classifier** trained to predict whether social media use affects a student's academic performance. The model is based on the "Social Media Addiction vs. Relationships" dataset from Kaggle, which contains survey responses from students aged 16 to 25. |
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## Usage |
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The model is packaged within a scikit-learn pipeline and can be easily loaded and used within any Python environment. It expects a pandas DataFrame with the same column structure as the original training data. |
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```python |
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import joblib |
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import pandas as pd |
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# Load the model |
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model = joblib.load('LS-W4-Mini-RF_Addiction_Impact.joblib') |
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# Example of new data to predict on |
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new_data = pd.DataFrame({ |
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'Gender': ['Female'], |
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'Academic_Level': ['Undergraduate'], |
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'Most_Used_Platform': ['Instagram'], |
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'Relationship_Status': ['Single'], |
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'Age': [20], |
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'Avg_Daily_Usage_Hours': [5.0], |
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'Sleep_Hours_Per_Night': [6], |
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'Mental_Health_Score': [7], |
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'Addicted_Score': [8], |
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'Conflicts_Over_Social_Media': [0] |
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}) |
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# Make a prediction |
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prediction = model.predict(new_data) |
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print("Prediction (1 = Yes, 0 = No):", prediction) |
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``` |
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## Training Data |
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The model was trained on the public dataset **[Social Media Addiction vs. Relationships](https://www.kaggle.com/datasets/adilshamim8/social-media-addiction-vs-relationships/data)**. The dataset consists of 705 records and 13 features with survey responses. The training data and the model file are available within the repository. |
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## Model Details |
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* **Model Type**: scikit-learn `RandomForestClassifier` |
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* **Pipeline Structure**: The pipeline includes a `ColumnTransformer` for one-hot encoding categorical features and the `RandomForestClassifier` itself. |
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* **Key Hyperparameters**: `n_estimators=100`, `random_state=42`. |
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## Performance |
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The model's performance was evaluated on a held-out test set from the original dataset. |
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* **Accuracy**: 0.93 |
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## Limitations and Ethical Considerations |
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* **Not a Diagnostic Tool**: This model should be used as a statistical tool for trend analysis and should **not** be used for clinical or psychological diagnosis of addiction. The data is based on self-reported survey responses. |
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* **Generalizability**: The model was trained on a specific sample of students and may not generalize well to other populations, age groups, or time periods. |
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* **Data Bias**: The model's predictions reflect the biases present in the original dataset. The results should be interpreted with caution. |
