The number of fitness bracelets and other IoT devices such as sleep trackers etc. has increased exponentially. With the amount of data now available through these devices on people from all walks of life, the amount of data has also increased dramatically. All of a person's daily activities amount to something, so there needs to be a pattern to the amount of data collected through these various devices like a sleep tracker and fitness band. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Currently, there are few applications that evaluate data for the user. Much of this must be done manually by the user. We are building an application that will monitor this daily activity data across these devices, evaluate them, and find patterns within them using k-means clustering in unsupervised learning. The evaluated data will be collected in a database, stored in the cloud and used as training data, and tests will be performed on it to find patterns. We also aim to predict user actions and health problems through the collected data. Such an application and the evaluated data can be useful to various institutions, fitness companies, etc. The main aim of this project is to develop an application that uses the collected data and evaluates it to find patterns in it. Mental stress is one of the growing problems of today's society. The number of people suffering from mental stress is increasing day by day. Stress is our body's response to prepare to face difficult situations. When a person is under stress, their nervous system responds by releasing stress hormones. These hormones make our body ready for emergency actions. In certain situations, it becomes dangerous and can put a person in serious mental disorders. The long-term effects of stress can be chronic. The chronic effect of stress causes health problems such as hypertension, cardiovascular disease and memory problems. The sense of loneliness and hopelessness can lead people to suicide. People may be less likely to notice if they are under high stress or may be generally less sensitive to stress. Stress sensing technology could help people better understand and relieve stress by increasing their awareness of high levels of stress that would otherwise go undetected. For this purpose, we have designed a smart band device that can detect different levels of skin conductance and predict whether the person is under stress or not. But skin conductance alone cannot accurately predict the level of stress in daily activities. Physiological responses caused by stress can also be triggered by physical activities such as running, lack of sleep, etc. To accurately measure your stress level, you need to make a classification. The fit band will be able to detect stress by analyzing different parameters based on skin conductance such as activity tracking, sleep quality etc. The collected data is then transmitted to the user's smartphone via Bluetooth and uploaded to the web where it can be accessed to find patterns. to further facilitate the user experience. The main aim of this project is to develop an application that uses the collected data and evaluates it to find patterns. This can be done by collecting a large sample of data and using it as training data using unsupervised learning methods such as clustering