<p><span style="display:block;text-align:center;clear:both"><img src="https://media.istockphoto.com/id/2020299693/vector/set-of-equipment-for-people-with-disabilities-or-elderly-and-social-adaptation-wheelchair.jpg?s=612x612&w=0&k=20&c=fFMyZJUBgBaAkwfHvhtbdE8Yyle-uUgy1huj82JmJpw="></span>Users can simply export personal knowledge from gadgets (e.g., weather station and fitness tracker) and providers (e.g., screentime tracker and commits on GitHub) they use however wrestle to achieve valuable insights. To sort out this problem, we current the self-monitoring meta app called InsightMe,  ItagPro which goals to show customers how data relate to their wellbeing, well being, and efficiency. This paper focuses on temper, which is intently associated with wellbeing. With information collected by one person, we show how a person’s sleep, train, nutrition, weather, air quality, screentime, and work correlate to the typical temper the individual experiences through the day. Furthermore, the app predicts the mood by way of multiple linear regression and a neural community, achieving an explained variance of 55% and 50%, respectively. We try for  ItagPro explainability and transparency by displaying the users p-values of the correlations, drawing prediction intervals. In addition, we conducted a small A/B check on illustrating how the original data affect predictions. We know that our atmosphere and actions considerably affect our temper, health, mental and athletic efficiency.</p><img src="https://itag-pro.com/wp-content/uploads/2025/08/imgi_17_ChatGPT_Image_Jun_21_2025_04_28_00_PM.png"><br/><br/><p>However, there's less certainty about how much our setting (e.g., weather, air quality, noise) or habits (e.g., nutrition, exercise, meditation,  <a href="https://wifidb.science/wiki/The_Ultimate_Guide_To_ITagPro_Tracker:_Everything_You_Need_To_Know">iTagPro website</a> sleep) affect our happiness, productiveness, sports performance, or allergies. Furthermore, sometimes, we're surprised that we're less motivated, our athletic efficiency is poor, or  <a href="https://michaeldnaumann.online/index.php/Answers_About_IPhone">iTagPro website</a> illness signs are more extreme. This paper focuses on every day temper. Our ultimate purpose is to know which variables causally affect our mood to take useful actions. However, causal inference is generally a posh subject and never inside the scope of this paper. Hence, we began with a system that computes how previous behavioral and environmental knowledge (e.g., weather, train, sleep, and screentime) correlate with temper after which use these options to foretell the daily temper by way of multiple linear regression and a neural community. The system explains its predictions by visualizing its reasoning in two different ways. Version A is based on a regression triangle drawn onto a scatter plot, and model B is an abstraction of the previous, the place the slope, top, and width of the regression triangle are represented in a bar chart.</p><br/><br/><p>We created a small A/B examine to test which visualization methodology enables participants to interpret knowledge faster and more precisely. The info used on this paper come from cheap client devices and  <a href="https://michaeldnaumann.online/index.ph

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