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Predict flash point and cetane number of biofuel

Project description

README v3.0 / 14 MARCH 2018

Logo: ![Alt](MyProject/2.png “Team Logo”)

### QSPR MODELING: APPLICATION OF MACHINE LEARNING ALOGRITHMS IN CLASSIFYING THE FAMILY AND PREDICTING FLASH POINTS AND CETANE NUMBER OF BIOFUEL COMPOUNDS

### Introduction

This Biofuel Software will predict the family of the input chemicals and predict thermo-physical properties (flash point and cetane number) according to the family. The GUI is designed by using tkinter. Numerical regression and classification methods, including MLPR, GRNN, OLS, PLS, KNN, SVM, LDA, are used in the machine learning approach to make better predictions of family and properties.

### Usage

To predict the family and the thermo-physical properties of the imported molecule, user can run the software following the instructions below. 1. Git clone our GitHub address git clone https://github.com/Zhangjt9317/Biofuel-Group-Project.git; 2. Then, users input cd Biofuel-Group-Project/MyProject command into bash; 3. Next, users input python Project_GUI.py command to open the Graphic User Interface; 4. Enter the CID number of that chemical and click Get CID to comfirm input. if Get CID is not clicked, no CID will be gotten for the machine learning models; 5. Click Model selection to chose differient machine learning methods and properties, and then click Begin to confirm selection; 6. Then click Result to plot the training and predction result.

### Contribution

### Requirements

This program runs on python. User must have the following packages installed in local environment.

Packages used in this program include: Openbabel, Neupy, Numpy, Matplotlib, Pandas, Pubchempy, Sklearn, tkinter, xlrd. The address of several packages are as following.

### One example

Please see the example for our software on the Demo.ipynb in the example folder.

### Credits

Jingtian Zhang, Cheng Zeng, Renlong Zheng, Chenggang Xi

### Contact

If you are having issues, please contact Cheng Zeng and Jingtian Zhang by [zengcheng95 –At– gmail.com](mailto:zengcheng95@gmail.com), [jtz9317 –At– gmail.com](mailto:jtz9317@gmail.com).

### License

The project is licensed under the MIT license.

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