The number of fitness bands and other IoT devices such as sleep trackers etc. has increased exponentially. The amount of data now available through these devices on people from all walks of life has also increased significantly. All of a person's daily activities add up to something, so there needs to be a pattern to how much data is collected through these different devices such as a sleep tracker and fitness band. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not 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 creating an application that will monitor this daily activity data through these devices, evaluate it and find patterns in it using k-means clustering in unsupervised learning. The evaluated data will be collected in a database and stored in the cloud and used as training data and tests will be run on it to find patterns. We further aim to predict user actions and health issues through the collected data. Such an application and the evaluated data can be useful to various institutions, fitness companies, etc. The main objective 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 in today's society. The number of people suffering from mental stress is increasing day by day. Stress is our body's response to prepare itself to face difficult situations. When a person is stressed, their nervous system responds by releasing stress hormones. These hormones prepare our body for emergency actions. In some situations, it becomes dangerous and can put a person into serious mental distress. The long-term effects of stress can be chronic. The chronic effect of stress leads to health problems like hypertension, cardiovascular diseases and memory problems. The feeling 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 detection technology could help people better understand and relieve stress by increasing awareness of high levels of stress that would otherwise go undetected. For this objective, we designed a smart band device to detect different levels of skin conductance and predict whether the person is stressed or not. But skin conductance alone cannot accurately predict stress levels in daily activities. Physiological responses caused by stress can also be caused by physical activities like running, lack of sleep, etc. In order to accurately measure the level of stress, a classification must be carried out. The fit band will be able to detect stress by analyzing different parameters based on skin conductance like activity tracking, sleep quality, etc. The collected data is then transmitted to the user's smartphone via Bluetooth and uploaded to the web from where it can be accessed to find patterns. to further facilitate the user experience. The main objective of this project is to develop an application that uses the collected data and evaluates it to find patterns in it. This can be done by collecting a large sample of data