Productivity Tracker Assistant

Abstract: In today’s world the time which people spend on device while working is to do productive work but many people get into the trap of wasting the time in using social media, entertainment, etc and after a few hours they recognise that the time has been wasted unconsciously by doing unproductive things.Since every work is moving towards the digital solution, which will lead many of the human existence to use the computer or mobile to get there work done. Hence it is necessary to have a monitor of activities that has been performed to avoid the unproductive time spent on their device. By taking this problem into consideration, this paper proposes Python, Deep Learning, Natural Language and Cloud Computing Frameworks for automating the tasks of classifying user activities into productive and nonproductive categories which has 13 subcategories which can be shown to the user in various statistical and tabular formats which will help user able to identify its activities as productive and non productive along with the time spent on the same. The user will be able to visualize its unproductive activities and can become conscious which will help the user to stop doing unwanted activities on desktop or mobile phone and start doing what the user actually wanted to do. For automating these tasks, along with the frameworks mentioned above, we have built a Desktop Application which will act as a productivity assistant for the user on a Desktop but the same can be built on an Android Application as well.

Keywords: Machine Learning, Productivity Assistant, Cloud Computing, Natural Language Processing, Cosine Similarity Algorithm, Desktop Application, Android Application, Electron, Algorithmia, Firebase, Text Classification, Voice Assistant, Website Classification, Chart.js.