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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.

🧠 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