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Obesity Level Prediction

Task

Develop a machine learning model to predict a person's obesity level based on various personal and lifestyle details.

Dataset

  • Files:

    • train.csv: Training set with details and obesity levels.
    • test.csv: Test set with details but without obesity levels.
    • sample_submission.csv: Sample file showing the correct submission format.

  • Columns:

    • id: Identifier for each sample.
    • uid: Unique identifier for each sample.
    • Location: Area of person's residence.
    • Gender: Self-explanatory.
    • Age: Self-explanatory.
    • Height: Self-explanatory.
    • Weight: Self-explanatory.
    • SMOKE: Whether a person smokes (boolean).
    • Water: Person's water intake levels.
    • Hash: Unknown field.
    • FHO: Family history of obesity.
    • CHCF: Consumption of high caloric value food.
    • CV: Consumption of vegetables.
    • NCP: Number of main meals.
    • CBC: Consumption of beverages with caloric value.
    • CAEC: Consumption of food between meals.
    • CA: Consumption of alcohol.
    • FAF: Physical activity frequency.
    • TI: Time spent on the internet.
    • Mode: Person's mode of transport.
    • Obesity_level: Target value (only in the training set).

Submission

Make predictions on the test dataset and submit a CSV file containing the Obesity_Level predictions mapped against the identifiers (ID). Check sample_submission.csv for the correct format.