In : import s3fs import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdates import seaborn as sns import numpy as np import pywt In : fs = s3fs.S3FileSystem(anon=True) fs.ls(‘datacases/datathon-2018-2/’) Out: [‘datacases/datathon-2018-2/kaufland’, ‘datacases/datathon-2018-2/nsi’, ‘datacases/datathon-2018-2/ontotext’, ‘datacases/datathon-2018-2/telelink’, ‘datacases/datathon-2018-2/telenor’] In : fs.ls(‘datacases/datathon-2018-2/kaufland’) Out: [‘datacases/datathon-2018-2/kaufland/20180820_Kaufland_case_IoT_and_predictive_maintenance_events.xlsx’, ‘datacases/datathon-2018-2/kaufland/20180920_Kaufland_case_IoT_and_predictive_maintenance.csv’, ‘datacases/datathon-2018-2/kaufland/sample_Kaufland_case_IoT_and_predictive_maintenance.csv’] Events¶ In : with fs.open(‘datacases/datathon-2018-2/kaufland/20180820_Kaufland_case_IoT_and_predictive_maintenance_events.xlsx’, ‘rb’) as f: df_events = pd.read_excel(f) In : df_events Out: […]
Dear Society, you should register for the Global Datathon 2018 – http://bit.ly/2wadU9C in order to see the case descriptions! 🙂
Overview Our approach to the problem is as follow: Data Augmentation – in the data augmentation phase we are creating synthetic images based on the dataset we have. This helps us to have larger dataset for model training and validation Object detection – There are several challenges with the object detection. First, sample is imbalanced […]
What we tried to do to solve the Kaufland case for the Global Datathon 2018. This article just contains our exploratory data analysis in the form of many plots and some explanations. There isn’t any modeling stage described here.
Kaufland-Case 1. Business Understanding Industrial vibration analysis is a measurement tool used to identify, predict, and prevent failures. Implementing vibration analysis on the machines will improve the reliability of the machines and lead to better machine efficiency and reduced down time eliminating mechanical or electrical failures. Vibration analysis are used to identify faults in machinery, plan machinery […]
1. Business Understanding A Kaufland store is a very big thing. It has a sales floor of up to 12.000 square meters and provides more than 30.000 products. A lot of events can occur on our shelves that are likely to be overlooked. Items can get sold out, other items might be placed on the […]
Case Summary A Kaufland store is a very big thing. It has a sales floor of up to 12.000 square meters and provides more than 30.000 products. A lot of events can occur on our shelves that are likely to be overlooked. Items can get sold out, other items might be placed on the wrong […]
In this paper we propose the use of a combination of LSTM and EDM models to address the issue of anomaly classification and prediction in time series data. Working with sensor data for automated storage and retrieval systems for a German hypermarket chain, we show that predictors based on variance and median methods show sufficient promise in the handling of anomalies.