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Student Gadget Screen Sleep Outdoor Academic Screen
Type Time Hours Activity Score Time Level
Student 5 1 4 6 60 75 1
During training, the AI model studies the data and finds patterns. For example, the model may
learn:
Students who use mobile phones for longer hours are more likely to have high screen time.
Students who get less sleep tend to spend more time on gadgets.
Students who spend less time in outdoor activities usually use gadgets more.
Higher screen time may lead to lower academic performance.
Students who have adequate sleep and limited screen time generally perform better
academically.
Increased outdoor activities usually help in reducing gadget usage.
Balanced routine with study, sleep and outdoor activity leads to healthier screen time
habits.
These patterns help the AI model understand relationships between different factors and make
predictions about students' gadget usage. This learning process is called model training.
Model Evaluation and Refinement
After completing Model Development and Training, the next stage in the AI Project Life Cycle
is Model Evaluation and Refinement. In this stage, the trained model is tested to check how
accurately it performs and whether it can make reliable predictions.
Model evaluation helps us understand:
How well the model has learned
Whether the model is making correct predictions
Whether improvements are required
The model is tested using new data known as test data. This data was not used during training, so
it helps in checking how well the model works in real-life situations.
Suppose, you teach your friend to identify students with high screen time using some examples.
After teaching, you give them new student records. If your friend correctly identifies most of them,
it means they learned properly. This is similar to model evaluation.
From the Gadget Screen Time data, the AI model may learn patterns such as:
Students who use mobile phones for longer hours are more likely to have high screen time.
Students who get less sleep tend to spend more time on gadgets.
AI Project Lifecycle 21

