Predicting Weather Disruption of Public Transport

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1. Business Understanding As part of datathon 2020, our case is tasked to build a predictive model to public transport services. The model can detect when the machine plan for less disruption in the wake of severe weather conditions and leverage the emergency management plan as well as providing uninterrupted services and products to citizens. […]

Predicting weather disruption of public transport – provided by Ernst and Young

Posted 3 CommentsPosted in Big Data, Datathon 2020 Solutions

Datathon2020 – Predicting weather disruption of public transport – provided by Ernst and Young¶This Project was inspired from the Business Case of Data Science Society Global 2020 Hackathon hosted from May 15 – 17 , 2020 click here for details about the Business Case and the data dictionary Data Sources :¶The datasets used in this […]

Reinforce Learning in Optimizing Supply Chain For Kaufland

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This article is a work in progress. We are a team of two and we just started exploring the dataset. Below is a link to Github – https://github.com/shamafarabi/Datathon   In [1]: import pandas as pd import matplotlib.pyplot as plt import seaborn as sns In [2]: r=pd.read_csv(‘sales.csv’) masterdata=pd.read_csv(‘item_lookup.csv’) In [3]: r.head() Out[3]: item_id the_date sold_qty 0 40001260 1/2/2019 7 […]

Datathon 2020 Ernst and Young Challengue – Team Solo

Posted 2 CommentsPosted in Datathon 2020 Solutions, Datathons Solutions

Business Understanding: This is the goal of the client: “Can you analyze the weather data to predict public transport service disruption in Dubai? How can we plan for less disruption in the wake of severe weather conditions and leverage the emergency management plan as well as providing uninterrupted services and products to citizens?” Data Understanding: […]

Weather Disruption of Public Transport Analysis Using Python

Posted 7 CommentsPosted in Datathon 2020 Solutions, Datathons Solutions

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 🙂 ).

Minimizing logistical transportation expenses of Retail Supply Chain

Posted 2 CommentsPosted in Datathon 2020 Solutions, Finance

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 are tons of problems and unnecessary costs,that are driving executives crazy.But the main problem,that is causing the most biggest pain to these companies,IS Transportation Expenses.Just imagine how much billions(if not trillions) dollars would be […]

Datathon 2020 – Article recommendation

Posted 6 CommentsPosted in Datathon 2020 Solutions, Datathons Solutions

Article recommendation Team Fire Initial Analysis: The task of recommending and predicting a next best article is modeled as a function of the users POI (point-of-interest), the articles content and contextual information about it such as its popularity. A new user session can be decomposed of the following two parts: Initially selecting an article – […]

Predicting weather disruption of public transport

Posted 8 CommentsPosted in Datathon 2020 Solutions, Datathons Solutions

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.

2020_Kaufland_Raven_Delivery

Posted 3 CommentsPosted in Datathon 2020 Solutions, Datathons Solutions

Datathon 2020 Kaufland – Optimize Retail Supply Chain   Team Name: Raven Delivery Authors Borislav Aymaliev Gabriela Vasileva Irina Naskinova Zainab Lawal  Team Toolset Python, Pandas, MySQL Excel Kaufland  dataset   Business Understanding Client –  Kaufland  In the innovative era we find ourselves in today,we have the ability to not only optimise cost by predicting […]