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Marketing-Campaign-Click-through-rate-prediction.

AIM:

  • Developed workflow for CTR prediction and suggested important metrics for getting more ROI.
  • Suggested important insights on campaign running time and day, banner positions for display campaigns etc.

PROCESS:

  • Trained the model on available CTR data from now one of the google’s acquired company.
  • Machine Learning models: Obtained best accuracy of results with tuned XGBoost model.

ABOUT THE DATA:

  • Training set - 10 days of click-through data, ordered chronologically.
  • Test set - 1 day of ads for testing your model predictions.
  • id: ad identifier
  • click: 0/1 for non-click/click
  • hour: format is YYMMDDHH, so 14091123 means 23:00 on Sept. 11, 2014 UTC.
  • banner_pos
  • Site features - Site_id, Site_domain, Site_category
  • App features - app_id, app_domain
  • Device features - device_type, device_conn_type
  • C1,C14 - C21 - Anonymized categorical variables

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