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 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/
This article will analyze Dubai weather data collected from January 2018 to March 2020 in “Predicting weather disruption of public transport” case and Dubai traffic accident data collected from June 2019 to May 2020 on Dubai Pulse Dubai Police Traffic category.
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.
Team Army of Ones and Zeros solving the “Netinfo/Vesti.bg” Case for Datathon 2020.
In the past few years DeepMind’s Alpha projects, IBM DeepBlue and OpenAI Five have shown that we are reaching the point of matching and exceeding human performance in complex game environments where there is no silver bullet to achieve a goal. In the push to mimic human performance in games Imperia Online has prepared a case in the game of Baloot. Have you ever heard about Baloot? Sounds like Belote and actually is like Belote but a little bit different.
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.
In : import os import pandas as pd import numpy as np import json import pprint import seaborn as sns from pandas.io.json import json_normalize import matplotlib.pyplot as plt from datetime import datetime, timedelta import time import warnings from statsmodels.formula.api import ols warnings.filterwarnings(‘ignore’) Business understanding¶ The aim of this article is to present an data driven approach […]
For the past two decades we have been witnessing a never seen before access to information on one hand and at the same time the volume of the information has been exponentially growing. The rule: 90% of all data has been created in the past 2 years is still standing. This has lead to information overloading and the rise of recommendation systems. Guiding the user in this pool of data has proven to be critical for business success as we can see from YouTube, Amazon, Netflix and many others. Net Info has prepared another challenge: The next best article.
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 […]
Data Use Case Introduction Climate change is projected to increase the frequency and intensity of some extreme weather events which is likely to damage transportation infrastructure and cause a disruption in the public transport and increase the risk of delays and failure due to storm, flooding and higher temperatures affecting the reliability and capacity of […]