Create README.md

README.md

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+---
+license: mit
+language: en
+datasets:
+- adilshamim8/social-media-addiction-vs-relationships
+tags:
+- tabular-data
+- scikit-learn
+- random-forest
+- classification
+- addiction
+- social-media
+model-index:
+- name: LS-W4-Mini-RF_Addiction_Impact
+  results:
+  - task:
+      name: Tabular Classification
+      type: tabular-classification
+    dataset:
+      name: Students Social Media Addiction
+      type: adilshamim8/social-media-addiction-vs-relationships
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.93
+---
+
+# LS-W4-Mini-RF_Addiction_Impact
+
+## Model Summary
+
+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.
+
+## Usage
+
+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.
+
+```python
+import joblib
+import pandas as pd
+
+# Load the model
+model = joblib.load('LS-W4-Mini-RF_Addiction_Impact.joblib')
+
+# Example of new data to predict on
+new_data = pd.DataFrame({
+    'Gender': ['Female'],
+    'Academic_Level': ['Undergraduate'],
+    'Most_Used_Platform': ['Instagram'],
+    'Relationship_Status': ['Single'],
+    'Age': [20],
+    'Avg_Daily_Usage_Hours': [5.0],
+    'Sleep_Hours_Per_Night': [6],
+    'Mental_Health_Score': [7],
+    'Addicted_Score': [8],
+    'Conflicts_Over_Social_Media': [0]
+})
+
+# Make a prediction
+prediction = model.predict(new_data)
+print("Prediction (1 = Yes, 0 = No):", prediction)
+
+```
+
+## Training Data
+
+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.
+
+## Model Details
+
+* **Model Type**: scikit-learn `RandomForestClassifier`
+* **Pipeline Structure**: The pipeline includes a `ColumnTransformer` for one-hot encoding categorical features and the `RandomForestClassifier` itself.
+* **Key Hyperparameters**: `n_estimators=100`, `random_state=42`.
+
+## Performance
+
+The model's performance was evaluated on a held-out test set from the original dataset.
+
+* **Accuracy**: 0.93
+
+## Limitations and Ethical Considerations
+
+* **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.
+* **Generalizability**: The model was trained on a specific sample of students and may not generalize well to other populations, age groups, or time periods.
+* **Data Bias**: The model's predictions reflect the biases present in the original dataset. The results should be interpreted with caution.