Article Recommender System
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Datathon 2020 Solutions
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 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. […]
ArticioCraker – Would you recommend me an article?
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Predicting weather disruption of public transport – provided by Ernst and Young
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 […]
Datathon 2020 – NetInfo Article Recommender – Newbies
The project aims to build a recommender system for the website called Vesti.bg. As the company runs to serve its huge customer base (as clear from the given data!) completely and for their best interests. And in order to do that it wants to recommend its users with articles that they should read next (based on mimicking their reading pattern). This is expected to save a lot of its users’ time in thinking. Also, with better and faster recommendations come people’s interest and that results in the company’s growth. The Company actually has a huge customer base. So, providing them with what they might like can really help it in making good money.
NewsCo: rapid non-parametric recommender algorithm for NetInfo news articles
Business understanding¶Online news reading has become very popular as the web provides access to news articles around the world. A key challenge of news websites is to help users find the articles that are interesting to read. The purpose of a recommender system is to suggest relevant items to users. Recommender systems can generate a […]
Reinforce Learning in Optimizing Supply Chain For Kaufland
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 […]
ACES solution to article recommender engine case – provided by NetInfo
Authors The ACES team that worked on the solution is listed in alphabetical order: Atanas Blagoev ([email protected]) Atanas Panayotov([email protected]) Emil Gyorev ([email protected]) Georgi Buyukliev ([email protected]) Iliana Voynichka ([email protected]) Slav-Konstantin Ivanov ([email protected]) Ventsislav Yordanov ([email protected]) Business Understanding Even though the news is perceived as one of the most important sources of information to people in […]
2020_Kaufland_Raven_Delivery
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 […]
ShopUp Datathon2020 – Article recommender case
ShopUp is working on the Article recommender as a part of the Datathon2020 check some other researches which they are doing at https://shopup.me/blog/
Datathon 2020 Ernst and Young Challengue – Team Solo
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: […]