"Learn IT, Girl" fourth edition projects
Report A Reckless Driver
Kelly McCain
Mentor: Khushboo Peswani
JavaScript
Report a Reckless Driver is a mobile app that alerts drivers and local authorities in the immediate area of an unsafe driver. The app can be used virtually anywhere in the world. Technology used to implement the product: JavaScript, Node, Google’s DialogFlow and Google’s Firebase Database.
BoredGamers
Katarzyna Chmielecka
Mentor: Daniel Hofmann
Python
BoredGamers is a social networking app for board game players. It is designed to help people find other board gamers interested in playing together. It allows the users to create an account, set up a user profile and fill it with personal information like location, age, favourite games etc. Users can also browse games and create gaming events to let other people know when and where they are hosting a game.
Mr Fever
Katarzyna Machura
Mentor: Mukunthan Tharmakulasingam
Java
Mr Fever is an assistant app for parents. It will be helpful during the time of illness of their child. This app can help them to monitor their child's temperature levels. It is also suitable as a manager to apply medicines for fever. It is also possible to create profile of more than one child.
Person of Interest Classifier
Anukriti Jain
Mentor: Haritha Paul
Python
The Enron dataset is a trove of information regarding the Enron Corporation, an energy, commodities, and services company that infamously went bankrupt in December 2001 as a result of fraudulent business practices. The aim of this project was to develop a Machine Learning Model that can identify the persons of interest (POIs) from the features within the data. The POIs are the individuals who were eventually tried for fraud or criminal activity in the Enron investigation. This involved studying and cleaning the dataset, engineering the features, picking and tuning an algorithm, evaluating, and testing the identifier using an available list of actual POIs in the fraud case. The text within the emails and the financial information acted as input for the model. The ultimate objective of investigating the Enron dataset was to be able to predict cases of fraud or unsafe business practices in general, and far in advance using Machine Learning.
Lynn - Lean On Me
Ruby Atieno
Mentor: Gunjan Tank
Python
It's alarming at the rates of depression among campus students. There have been several cases of suicide experienced where the victims go real quiet and go on in life smiling at everyone, yet not all is well. Comes in Lynn, a counselor chat bot that aims to provide a platform for campus students to talk to when they are having their low moments to help uplift their spirits. In future, Lynn, when fully grown and more intelligent, will be able to contact individual's friends so that they don't rely on a bot because it cannot do as much as what humans can do. Human support and encouragement will definitely go a long way!
Data Storytelling for Historians
Giuditta Parolini
Mentor: Laura Fernández Gallardo
Python
Historians make a living studying the past and it's paper archives but they can also benefit from the use of digital technologies in their storytelling. The project uses Python coding and data science methods, such as visualisations and natural language processing, to explore the history of a research field – agricultural meteorology – using its scientific publications.