Gaming Addiction Neural Network Model 🧠🎮

This project houses an Artificial Neural Network (ANN) built to predict gaming addiction risk levels based on various behavioral and mental health metrics. It includes a complete machine learning pipeline from data preprocessing to model evaluation.

Project Structure 📁

• ANN.ipynb: The core Jupyter Notebook containing the data analysis, data scaling, categorical encoding, model architecture definition, and training loops using Deep Learning.
• Gaming and Mental Health.csv: The dataset containing metrics such as primary game played, gaming patterns, and related mental health outcomes.

Key Features & Techniques ⚙️

• Data Preprocessing: Utilizes StandardScaler for continuous features and One-Hot Encoding (get_dummies) alongside LabelEncoder for categorical alignment.
• Neural Network Architecture: Uses MLPClassifier with customized hyperparameters (e.g., hidden layers, sigmoid or ReLU activations) designed to classify varying risk levels.
• Performance Evaluation: Validates the model utilizing hold-out test sets to generate robust accuracy, precision, recall, F1 scores, and Confusion Matrices.
• Hyperparameter Tuning: Involves strategies like GridSearchCV to locate optimal node sizes and learning paths.

Getting Started 🚀

1. Clone this repository:

   git clone https://github.com/BassemRamdan/ANN.git
   cd ANN

2. Boot up Jupyter:

   jupyter notebook

3. Open ANN.ipynb and run all cells to evaluate the predictions against the bundled dataset.

Requirements 🛠️

Ensure the following packages are installed:

• python 3.x
• pandas
• numpy
• scikit-learn
• matplotlib / seaborn (for visualization of the confusion metrics)