Smartphone Addiction Prediction using Machine Learning

This project predicts the likelihood of smartphone addiction based on various behavioral and psychological features. It's built using Python, TensorFlow, and Flask.

Features

• Machine Learning model trained using TensorFlow/Keras.
• Preprocessing with MinMaxScaler.
• Flask-based web app for user input and prediction.
• Simple HTML form interface.
• Runs locally on PyCharm or terminal.

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🧠 Model Inputs

The model expects 21 numerical features:

• Age
• Gender (0 = Male, 1 = Female)
• School Grade
• Daily Usage Hours
• Sleep Hours
• Academic Performance
• Social Interactions
• Exercise Hours
• Anxiety Level
• Depression Level
• Self Esteem
• Parental Control
• Screen Time Before Bed
• Phone Checks Per Day
• Apps Used Daily
• Time on Social Media
• Time on Gaming
• Time on Education
• Phone Usage Purpose
• Family Communication
• Weekend Usage Hours