I want to lose some weight. It’s high time to reduce my body fat. I’ll definitely start tomorrow. These are the thoughts that most of us ponder over daily. Every morning, you might step on a weighing scale, patting yourself on the back or belittling yourself, which might continue for the next few days. This is where data science can guide you in your weight loss journey. According to reports, data science projects can help us in our day-to-day lives for personal benefits, such as weight loss.
With the help of data science projects, individuals can generate insights, strengthening the understanding of the relationships between various health metrics. For instance, using a weight loss app powered by data science can recommend users to work out on a daily basis in order to maintain their activity level. Once they start committing to this goal, they can easily track their progress.
Data science is all about gaining insights from structured or unstructured data, which helps you to make informed and strategic decisions. Looking at the voluminous data that today’s organizations maintain, data science has become a critical component of IT. However, the application of data science has moved beyond IT and has entered into people’s personal lives.
The interesting part is that for undertaking data science projects for personal use, you don’t need the data or resources of an IT firm. The data gathered by you can prove helpful and let you develop some data science skills based on a real-world example—your weight loss journey—that can yield better results.
Using Reinforcement Learning in Data Science Projects
Reinforcement learning, a branch of machine learning, offers a new form of optimization in which the intensity of each individual’s involvement is continuously attuned depending on patterns of response. This can be illustrated with Micheal Lanham’s example. A few years ago, Micheal, a software and tech innovator in his late 40s, decided to train for a bodybuilding competition. He injured his back, which affected him both mentally and physically, making him almost 181 Kg. This was when he came up with the idea of a weight-loss or agent nutritionist that could assist him by just recommending the food he can or cannot eat.
It isn’t science fiction. Using reinforcement learning, developers can create apps to track the eating habits of individuals, warning them about the food they should and should not eat during their weight loss journey.
Leveraging reinforcement learning, an app could be built that can work as described above. However, in order to be effective, the nutritionist needs to have access to a wide variety of images showing food and consumers. The app needs a broad base of users with various weight or nutritional goals. This is an idea for people who want to try out this real-world data science project to maintain a healthy weight.
Using Machine Learning in Data Science Projects
The incorporation of technologies such as artificial intelligence and machine learning has made it easy to collect, classify, and process available data. Using these technologies, we can make sense of the cryptic information and better understand the human body.
Robust machine learning models can be used to gather insights from available data. Data science provides the necessary tools for us to consolidate all that structured and unstructured data from hundreds and thousands of sources. Leveraging robust machine learning models, it is possible to sort through the sea of data and procure insights that make sense. The insights will help us understand how to take control of those factors and achieve a healthy weight.
People who follow dietary guidelines given by dieticians during their weight loss journey tend to be more successful with weight control. Sometimes, individuals deviate from their dietary guidelines. According to research, these deviations can be predicted in real-time using different physiological, psychological, and environmental indicators. A study sought to employ machine learning algorithms to predict lapses and determine the efficacy of integrating both group- and individual-level data to improve lapse prediction. The predictive algorithm can be applied to provide in-the-moment interventions to check dietary lapses, enhancing weight losses.
Wearable Technology to Track Weight Loss
Today, we see several people wearing gadgets on their wrists. Wearable technology tracks heart rate or the number of steps walked. This type of personal data tracking is gaining traction, especially among the younger generation and those who are fitness conscious. Whether it’s Apple Fitness Tracker, Fitbit, Garmin Vivofit, or other fitness trackers, it’s becoming a rage among people to easily track their progress over time.
The emergence of wearable technology is contributing to the growth of health information, creating opportunities for us to make informed decisions regarding our personal health. Health tech organizations are increasingly launching applications designed for weight loss and management, which enables users to monitor their activities, including sleep and exercise. While some use it to receive feedback and input regarding health, many users leverage the capabilities of these self-help devices to track their calorie balance and motivate themselves. These devices procure massive troves of data relating to health and fitness. The accessibility to these datasets allows us to leverage the information from millions of users and study the individual and social factors affecting weight loss.
Predictive Analytics for Weight Loss Predictions
The data can not only facilitate large-scale weight loss studies but also lead to the development of robust models that can guide and automatically predict weight loss. Predictive analytics can either be used for weight loss predictions or in the development of models for better weight loss management applications. Data analytics can be used to identify the primary factors that contribute to weight loss.
What Can We Infer?
The question you might ask yourself is—“What have I learned from this blog that I can implement now?” In summary, weight loss application using reinforcement learning is a data science project that you can practise your data science skills on. You might not have enough data at your disposal, which is perfectly fine. It indicates that you need to collect more and more data or rethink your approach. In order to follow the path of data science, you need to learn about the concepts of the subject and choose your focus area—advanced machine learning, natural language processing, or deep learning.
Pro tip: to learn from all these concepts and get access to 1:1 mentoring from real-life data scientists, you can enroll in Springboard’s Data Science Career Track to work on real-world data science projects and make a career transition.