LOCKED Dataset

Data Analysis Project

This is a data analysis project that uses Machine Learning techniques to predict outcomes based on a provided dataset. The main goal of this project is to create a predictive model using various algorithms and evaluate its performance.

Technologies Used

• Python: The main language used for analysis and model development.
• Pandas: A library for data manipulation and analysis.
• NumPy: A library for numerical computations.
• Scikit-Learn: A library for implementing Machine Learning models.
• Matplotlib and Seaborn: Libraries for data visualization.

Installation

Follow these steps to set up the project in your local environment:

1. Clone the repository:

   git clone https://github.com/ufvceiec/Impact_of_the_Spanish_lockdown_during_COVID-19_on_psychological_conditions.git

2. Create a virtual environment (optional but recommended):

   python -m venv env

3. Activate the virtual environment:

   • On Windows:

     .\env\Scripts\activate

   • On macOS/Linux:

     source env/bin/activate

4. Install the necessary dependencies:

   pip install -r requirements.txt

Usage

To run the analysis, execute the notebooks in the following order:

1. Preprocessing.ipynb: Preprocess the data.
2. label_Test.ipynb: Create a labels.
3. Classification_models.ipynb: Train the machine learning models.
4. Graphics.ipynb: Generate the visualizations.

Simply open each notebook and run the cells in order to perform the analysis.

Training code

In Classification_models.ipynb:

# Evaluar cada clasificador
for nombre_algoritmo, clasificador in classifiers.items():

    tiempo_inicio = datetime.datetime.now()
    
    # GridSearchCV para modelos con hiperparámetros
    if param_grids[nombre_algoritmo]:  
        grid_search = GridSearchCV(
            estimator=clasificador,
            param_grid=param_grids[nombre_algoritmo],
            scoring='accuracy',
            cv=5,
            n_jobs=-1,
            return_train_score=True,
            error_score='raise'
        )

        grid_search.fit(X_train, y_train)
        mejor_modelo = grid_search.best_estimator_
        mejores_params = grid_search.best_params_

        mean_train_accuracy = np.mean(grid_search.cv_results_['mean_train_score'])
        std_train_accuracy = np.std(grid_search.cv_results_['mean_train_score'])
        mean_val_accuracy = np.mean(grid_search.cv_results_['mean_test_score'])
        std_val_accuracy = np.std(grid_search.cv_results_['mean_test_score'])
        
    else:  
        mejor_modelo = clasificador.fit(X_train, y_train)
        mejores_params = "N/A"
        mean_train_accuracy = accuracy_score(y_train, mejor_modelo.predict(X_train))
        std_train_accuracy = 0
        mean_val_accuracy = accuracy_score(y_test, mejor_modelo.predict(X_test))
        std_val_accuracy = 0

Evaluation code

In Classification_models.ipynb:

    # Evaluación final en conjunto de prueba
    predicciones_test = mejor_modelo.predict(X_test)
    accuracy_test = accuracy_score(y_test, predicciones_test)
    sensibilidad_test, especificidad_test = calcular_sensibilidad_especificidad_conjunto(y_test, predicciones_test)

    # Cálculo de los valores SHAP
    shap_df = calcular_shap_values(mejor_modelo, X_train, X_test, nombre_algoritmo)

    # Agregar cada fila al conjunto de resultados
    for idx, row in shap_df.iterrows():
        resultados_totales.append({
            'dataset': dataset,
            'subset': subset,
            'issue': issue,
            'algorithm': nombre_algoritmo,
            'best_params': mejores_params,
            'mean_train_accuracy': mean_train_accuracy,
            'std_train_accuracy': std_train_accuracy,
            'mean_val_accuracy': mean_val_accuracy,
            'std_val_accuracy': std_val_accuracy,
            'accuracy_test': accuracy_test,
            'sensitivity_test': sensibilidad_test,
            'specificity_test': especificidad_test,
            'shap_feature_values': row.to_dict()
        })

Model Performance

Below is a summary of the performance of the best machine learning models for each category, including their training, validation, and test accuracies:

Category	Best ML Model	Training Accuracy	Validation Accuracy	Test Accuracy
Demographic characteristics
Hostility	DT	91.15% ± 2.50	85.16% ± 1.48	81.36%
Somatization	SVM	81.70% ± 5.86	76.36% ± 3.09	87.50%
Depression	SVM	85.09% ± 4.47	80.27% ± 2.85	87.50%
Obsession-Compulsion	RF	90.96% ± 2.40	82.40% ± 1.50	89.55%
Anxiety	SVM	81.89% ± 5.42	77.81% ± 3.47	89.71%
Interpersonal Sensitivity	DT	89.60% ± 2.42	87.59% ± 1.01	85.29%
Agoraphobia	RF	92.76% ± 1.25	85.89% ± 0.53	90.00%
Paranoid Ideation	DT	92.23% ± 1.69	84.52% ± 1.1	92.31%
Psychoticism	MLP	86.83% ± 7.37	79.95% ± 3.07	87.50%
Living environment
Hostility	SVM	83.94% ± 10.16	76.75% ± 6.93	88.14%
Somatization	SVM	84.14% ± 9.7	76.85% ± 7.98	87.50%
Depression	MLP	73.34% ± 3.67	71.04% ± 3.46	84.38%
Obsession-Compulsion	RF	95.35% ± 2.12	88.36% ± 0.78	89.55%
Anxiety	SVM	82.20% ± 10.67	75.35% ± 7.49	94.12%
Interpersonal Sensitivity	GB	94.44% ± 5.40	80.75% ± 1.74	83.82%
Agoraphobia	MLP	90.60% ± 7.83	81.22% ± 3.77	90.00%
Paranoid Ideation	RF	96.09% ± 1.98	85.63% ± 0.67	90.38%
Psychoticism	RF	96.50% ± 1.63	79.53% ± 1.35	92.86%
Economic status
Hostility	SVM	89.22% ± 7.89	83.76% ± 5.56	91.52%
Somatization	RF	96.3% ± 1.3	87.86% ± 0.91	87.50%
Depression	DT	93.12% ± 2.29	78.73% ± 1.25	85.29%
Obsession-Compulsion	RF	97.64% ± 0.98	88.20% ± 0.71	88.06%
Anxiety	RF	97.17% ± 1.12	85.75% ± 0.51	85.29%
Interpersonal Sensitivity	DT	91.98% ± 3.01	81.55% ± 1.48	91.18%
Agoraphobia	SVM	87.94% ± 9.88	80.12% ± 6.78	94.00%
Paranoid Ideation	RF	95.66% ± 1.56	85.40% ± 1.37	96.15%
Psychoticism	NB	83.92% ± 4.15	82.60% ± 3.74	87.50%
Health impacts
Hostility	DT	95.74% ± 1.15	93.12% ± 0.35	96.61%
Somatization	RF	96.7% ± 0.15	96.15% ± 0.68	92.19%
Depression	GB	96.84% ± 0.79	95.37% ± 0.78	95.31%
Obsession-Compulsion	DT	94.01% ± 0.61	93.51% ± 0.7	98.51%
Anxiety	RF	95.4% ± 0.16	95.31% ± 0.41	92.65%
Interpersonal Sensitivity	DT	95.68% ± 0.63	94.69% ± 0.7	92.65%
Agoraphobia	NB	92.79% ± 8.67	92.61% ± 8.91	98.00%
Paranoid Ideation	DT	92.3% ± 0.68	92.00% ± 1.13	100%
Psychoticism	SVM	87.33% ± 7.45	85.89% ± 7.39	91.07%