Predicting Weather Disruption of Public Transport
1. Business Understanding As part of datathon 2020, our case is tasked to build a predictive model to public transport services. The […]
1. Business Understanding As part of datathon 2020, our case is tasked to build a predictive model to public transport services. The […]
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Datathon2020 – Predicting weather disruption of public transport – provided by Ernst and Young¶This Project was inspired from the Business Case of […]
This article is a work in progress. We are a team of two and we just started exploring the dataset. Below is […]
Business Understanding: This is the goal of the client: “Can you analyze the weather data to predict public transport service disruption in […]
KINDLY EVALUATE THE PDF.
THE PDF IS MY FINAL SOLUTION.
MY JUPYTER NOTEBOOK WAS REJECTED BECAUSE 2.1MB > 2.0MB
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 🙂 ).
Business Understanding As you know,lots of companies,related to logistics system,are having troubles with managing their finances,when it comes to transporting and ordering.There […]
Article recommendation Team Fire Initial Analysis: The task of recommending and predicting a next best article is modeled as a function of […]
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.