Depression Analysis and Prediction

This repository contains a Jupyter Notebook that analyzes a dataset related to depression and builds a predictive model to classify individuals as depressed or not based on various features.

Dataset

The dataset used in this analysis is depression.csv. It contains several features that are used to predict the target variable depressed. The features include:

• age: Age of the individual
• gender: Gender of the individual
• education: Education level
• marital_status: Marital status
• employment_status: Employment status
• income: Income level
• family_history: Family history of mental illness
• physical_activity: Level of physical activity
• sleep_hours: Average sleep hours per night
• stress_level: Self-reported stress level

The target variable is:

• depressed: Indicates whether the individual is depressed (1) or not (0)

Notebook Overview

The notebook is structured as follows:

1. Data Loading and Exploration: The dataset is loaded, and initial exploratory data analysis (EDA) is performed to understand the distribution of features and the target variable.

2. Data Preprocessing: Steps include handling missing values, encoding categorical variables, and scaling numerical features to prepare the data for modeling.

3. Model Building: A logistic regression model is trained to predict the likelihood of depression based on the input features.

4. Model Evaluation: The performance of the model is evaluated using metrics such as accuracy, precision, recall, and the ROC-AUC score.

5. Conclusion: Insights from the analysis and model performance are summarized.

Requirements

To run the notebook, ensure you have the following Python libraries installed:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

You can install these packages using pip:

pip install pandas numpy scikit-learn matplotlib seaborn

Usage

1. Clone the repository:

   git clone https://github.com/Liantsoarandria0803/Health-mental-disease.git

2. Navigate to the project directory:

   cd Health-mental-disease

3. Open the Jupyter Notebook:

   jupyter notebook Depression.ipynb

4. Run the cells sequentially to perform the analysis and view the results.

Results

Best model : catBoost with F1 score : 0.9621830111721936

Contributing

Contributions are welcome! If you have suggestions for improvements or additional analyses, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.