feature extraction from deep data set and and measure accuracy after applying DML

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I have a deep data set for EEG signals which is down sampled at 128 hz. It is a per-processed data. The data is for a total of 32 participants. Each participant file contains two arrays 1. data (40X40X8064) i.e. data( video/trial X channel X data) 2. labels (40X4) i.e. label( video/trail X label) We need to apply MFCC for feature extraction afterwards DML is to be applied and in the last stepfeatures we get after applying DML are to be applied to SVN for measuring accuracy, f1 measure , precision, recall.

Python

Machine Learning (ML)

Ταυτότητα εργασίας: 27414082

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@suyashdhoot

Hi I am a very experienced statistician, data scientist and academic writer. I have completed several PhD level thesis projects involving advanced statistical analysis of data. I have worked with data from several companies and have done projects involving high level quantitative analysis and data interpretation skills to study the trends, time behaviour and compare the variables in the data. I can do advanced level analysis in SPSS, R, PYTHON, WEKA, TABLEAU, POWER BI, and EXCEL tools like machine learning, deep learning, AI, NLP, hypothesis testing, forecasting, T-test, ANOVA etc. Looking forward to discussion, Best Regards, Suyash

₹5.000 INR σε 7 ημέρες

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@supreetsingh2020

i have worked on EEG waveform analysis projects and also finding MFCC coefficients in MATLAB. i can work on this project efficiently and to your job satisfaction. Let us discuss the details through chat.

₹2.900 INR σε 5 ημέρες