]]> HelpSelf is a lightweight web application designed to make starting and sustaining daily habits simpler. The project addresses a common problem in personal productivity: users often lack a clear, low-friction way to define habits, log consistent progress, and see motivating feedback. HelpSelf provides a focused workflow where a user registers and verifies an email account, defines one or more habits (with unit, verb, increment, and milestone), and then records progress over time.

The backend is implemented in PHP using a minimal microframework to expose a REST API. Authentication relies on JSON Web Tokens (JWT) with short-lived access tokens and email verification, while data is stored in a relational database. The frontend is a single-page interface built with standard web technologies and a responsive UI toolkit.

In practice, the application allows users to: create and manage habits, increment daily progress with one click, update habit details, reset passwords via email, and edit a basic profile.
]]> common consumer usage. The transitional period of the handwritten to the digital age can be
largely improved by focusing on perfecting handwritten character recognition.
This paper and work aims to focus on handwritten digit recognition using the decision tree
classifier machine learning method, implemented, trained and tested on the data set gathered from
the Modified National Institute of Standards and Technology dataset.
The data to be recognized is inputted from a pre-existing reliable set, used both for training and
testing, in order to give a fair result. The system is run through a Python script and the data set is
stored in CSV format, preprocessed and ready for further usage.
Taking into consideration the size of the dataset (42000 rows of data), the system’s overall
performance is satisfactory with an accuracy of 85% and outputs the results in an understandable
manner.
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