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Machine learning with experiment data

Hello.

If you are good at machine learning and data analysis. Please check out our task below.

We have an experiment about industry material. The data we produced was saved in .csv file. It has 19 columns. Column 1 to 18 are the experiment output data. They are in float type. Column 19 is the result. It has 3 values: pass/fail/unknown.

The result was decided by our chief engineer manually. It's not an easy task. So we would like to create a python program to process the data with machine learning, and then predict the result with new experiment data. So far, we have tried the following method:

1)XGBClassifier

2)RandomForestClassifier

3)Logic regression

4)SVM

The accuracy rate is always around 46%. However, we would like the accuracy rate up to 90% at least.

We have uploaded 100 examples. There are about 2000+ records now. The data increases about 50 records daily.

Please contact us if you have any ideas. Thank you.

Best regards,

YJ

Skills: Python, Machine Learning (ML), Data Mining, Statistics, Statistical Analysis

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About the Employer:
( 0 reviews ) Beijing, China

Project ID: #20304983

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