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Title | Daniel Martin-Alarcon - Data |
Description | DanielMartin-Alarcon Data Science | |
Keywords | N/A |
WebSite | martinalarcon.org |
Host IP | 185.199.110.153 |
Location | - |
Site | Rank |
US$1,253,235
Last updated: 2023-05-13 16:00:48
martinalarcon.org has Semrush global rank of 8,445,589. martinalarcon.org has an estimated worth of US$ 1,253,235, based on its estimated Ads revenue. martinalarcon.org receives approximately 144,604 unique visitors each day. Its web server is located in -, with IP address 185.199.110.153. According to SiteAdvisor, martinalarcon.org is safe to visit. |
Purchase/Sale Value | US$1,253,235 |
Daily Ads Revenue | US$1,157 |
Monthly Ads Revenue | US$34,705 |
Yearly Ads Revenue | US$416,460 |
Daily Unique Visitors | 9,641 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
martinalarcon.org. | A | 3600 | IP: 185.199.110.153 |
martinalarcon.org. | A | 3600 | IP: 185.199.109.153 |
martinalarcon.org. | A | 3600 | IP: 185.199.111.153 |
martinalarcon.org. | A | 3600 | IP: 185.199.108.153 |
martinalarcon.org. | NS | 21600 | NS Record: ns-cloud-c2.googledomains.com. |
martinalarcon.org. | NS | 21600 | NS Record: ns-cloud-c4.googledomains.com. |
martinalarcon.org. | NS | 21600 | NS Record: ns-cloud-c1.googledomains.com. |
martinalarcon.org. | NS | 21600 | NS Record: ns-cloud-c3.googledomains.com. |
martinalarcon.org. | MX | 3600 | MX Record: 40 alt4.gmr-smtp-in.l.google.com. |
martinalarcon.org. | MX | 3600 | MX Record: 30 alt3.gmr-smtp-in.l.google.com. |
martinalarcon.org. | MX | 3600 | MX Record: 20 alt2.gmr-smtp-in.l.google.com. |
martinalarcon.org. | MX | 3600 | MX Record: 10 alt1.gmr-smtp-in.l.google.com. |
martinalarcon.org. | MX | 3600 | MX Record: 5 gmr-smtp-in.l.google.com. |
Toggle navigation Daniel Martin-Alarcon About Me Daniel Martin-Alarcon Data Science | Bioengineering Daniel Martin-Alarcon Data Science | Bioengineering Time series forecasting with Prophet and fast.ai Using deep learning and feature engineering to improve on univariate regression models I combine two very different approaches to time series forecasting, applied to a dataset of air pollution in Beijing. I use Prophet to make an univariate additive regression model, then show that it performs similarly to a shallow neural network made with fast.ai. I devise a plan to give the... [Read More] Reproducible data science with Docker and Luigi The case of arsenic and fluoride in Mexican drinking water I describe a workflow that uses Docker and Luigi to create fully transparent and reproducible data analyses. End users can repeat the original calculations to produce all the final tables and figures starting from the original raw data. End users (and the author, at a later date) can easily |
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