{'fraction_non_alphanumeric': 0.015783540022547914, 'fraction_numerical': 0.006764374295377677, 'mean_word_length': 5.124137931034483, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analyzing the Impact of Age and Gender for Targeted Advertisements Prediction Model

The practice of targeted advertisements has been gaining popularity, especially in this digital era. There are a lot of aspects to take into consideration when creating an efficiently targeted advertisement, such as advertisement details and user backgrounds. Using this information can increase the likelihood of sending the right advertisements to the right demographic. In this paper, we will explore which features have an influence towards the click-through rate of these targeted advertisements. The best models in our experiment are LightGBM and XGBoost with the ROC-AUC score of 0.76 for LightGBM and 0.78 for XGboost. Adding age and gender can improve the results. Our experiment can be insightful for making a better marketing strategy to reach more segmented users in display advertisements.