Datathon

Posted taha-junaid30006 CommentsPosted in Datathon 2020 Solutions, Datathons SolutionsDatathon 2020 Solutions, Datathons Solutions

Weather Disruption of Public Transport Analysis Using Python

The Weather Dataset provided has been preprocessed the traffic data ha been appended after preprocessing.The aim is to find the intersection dates available from both the datasets and do a predictive analsyis after combining traffic and weather datasets.so if future weather conditions are given or predicted by time series analysis ,public trasnport disruption could be interpreted using machine learning models.

(Just a small try by an undergrad engineering student,Hope you like it 🙂 ).

Posted ss3151, ss3151 Mentors: anenov3 CommentsPosted in Datathon 2020 Solutions, Recommendation systemsDatathon 2020 Solutions, Recommendation systems

Datathon2020 – NetInfo Article Recommender – Solution – nextClick

Giving a recommendation to the user to catch his eye on and meet his preferences is essential task for a recommendation system. The amount of data is increasing significantly and the idea is to get some knowledge from it. Taking advantage of user similarities or news similarities will provide useful information to predict which article will the user find interesting.

Posted sv-stefanov Mentors: amjad-ey, kunal8 CommentsPosted in Datathon 2020 Solutions, Datathons SolutionsDatathon 2020 Solutions, Datathons Solutions

Predicting weather disruption of public transport

The purpose of the project was to build a model that tries to predict potential delays in Dubai’s bus transportation schedule, based on the weather conditions. Additional Extreme Gradient Boosting model was built, which is based on the weather conditions by 5 hours ago, which slightly improved the prediction of a few outliers, although this came at the cost of reducing the prediction accuracy for non-outliers. The overall prediction power was unfortunately unimpressive and could potentially be improved by analyzing the bus transportation data at an hourly level, by including additional data, such as global weather forecasts and traffic estimates, but also by exploring more feature engineering options, for example – seasonality, business activity, hourly segments and outlying flags.