Concluído

Time series forecasting

I need a jupyter notebook and CSV file with best RMSE score for a common kaggle database Sun spot prediction.

Most of code or even notebook is available on kaggle or medium. I'm here since I am on vacation. Need it by Monday.

Requirement: Using the monthly average sunspot data available from 1749 to 2010, build a model to forecast the monthly average sunspot count for the period of 2011-2020.

Habilidades: Machine Learning (ML)

Sobre o Cliente:
( 6 comentários ) Bangalore North, India

ID do Projeto: #32327668

Concedido a:

marvynbealu

Hi, I would like to help you with your project, I already have an almost finished notebook that will be easily optimized within 3-4 hours.

₹3000 INR em 1 dia
(3 Comentários)
2.8

7 freelancers estão ofertando em média ₹4036 nesse trabalho

(42 Comentários)
6.6
sajjadtaghvaeifr

Hi, I hope you are doing fine. I have almost 10 years of experience in machine learning algorithms. I can implement various types of artificial intelligence algorithms including yours with Matlab, Python and etc. I hav Mais

₹12500 INR in 7 dias
(11 Comentários)
4.6
datacode0023

Hi, I have +5 years of experience dealing with machine learning algorithms and worked on multiple projects in this field, Please contact me to discuss more. Have a nice day

₹2500 INR in 7 dias
(8 Comentários)
3.2
teekayzitsi

Hi In my current Profession -I perfom the DBA and Developer duties in high traffic OLTP environment -Python programing, -Data Scraping using python, data analysis using python and R -Database development in (Mysql Mais

₹3000 INR in 7 dias
(3 Comentários)
3.1
ishanagrawal1201

Greetings! How are you today? Thanks for posting this job. I have checked your project description. I'm Al engineer also A Software Developer with 4+ years Experience. I have a good hands on working with Python, Al Mais

₹2500 INR in 3 dias
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0.0
ashutosh683

I have good knowledge in ML and I explored the method to do your task so I can do it within the time period I mentioned.

₹2250 INR in 2 dias
(0 Comentários)
0.0