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.