Have a set of data with no known pattern (approximately 700-900 rows) 9 columns per row and a data range of 1-45 (please note it has nothing to do with the lottery, please don’t misunderstand).
1. preferably using Python.
2. the algorithm can be LSTM or whatever you think is more appropriate.
3. when I provide data, I will delete 5-10 rows of data for testing.
4. I will provide the respective next line of data after you complete training.
5, the requirements of the data should be accurate in more than 4 (including 4) This is also the basic requirement for payment, if the data can be consistently accurate in more than 6, can pay a certain amount of bonus in addition to the original negotiated price.
6. The program requires detailed annotations.
[login to view URL] requirements: the results to Excel output, arranged by probability of occurrence, the best output results greater than 9 columns of data for reference.
37 freelancer chào giá trung bình$161 cho công việc này
I am expert in ML and Deep Learning. I am good in theory and coding (Tensorflow +keras) for the implementation of LSTM, RNN, BiLSTM, GRU, CNN. Thanks for providing opportunity to bid on your project.
Hi, there. I am a machine learning engineer with 5+ years of hands-on experience. Carefully checked your requirements. Hope to work for you with this project. Best regards, Sgadou
Hello, I'm expert in python and ML. I have a M Sc degree in AI and extensive experience in problem solving, so I can easily help you. Looking forward to your reply.
do kindly reach out to me over chat with the requirements and data to get started. Also, I understand your requirements and would deliver as promised
Hi bro, I am Ahmed. I have a Master's degree in Computer Science from Europe. I can do your project. I am a Machine Learning expert. Please contact me to discuss more in detail.
I have made various projects using LSTM and as you described the problem set LSTM best suits it. I have 4+ years of expertise in developing and deploying machine learning and deep learning models.