prilcool/Intel-devmesh-codeproject-two

We Implement two Statistical Mathematical Algorithms such as Pearson’s Correlation Coefficient & Linear Regression with DPC++ and show you how to implement this algorithms in real life in sales and marketing to forecast Future sales based on advertising expenditure.

Intel-devmesh-codeproject-two

We Implement two Statistical Mathematical Algorithms such as Pearson’s Correlation Coefficient & Linear Regression with DPC++ and show you how to implement this algorithms in real life in sales and marketing to forecast Future sales based on advertising expenditure.

Usage instructions :

Copy the entire structure including all files to Intel dev cloud .

Ensure that the Python 3.7 (Intel OneApi) kernal is running

Ensure that you are using the q file ,regression.sh and Makefile that is provided with this sourcecode.

Ensure that file exist in lab/regression.cpp

Run the following jupyter notebook Interest-on-account-audit-onemillion_records.ipynb

 ! chmod 755 q; chmod 755 regression.sh;if [ -x "$(command -v qsub)" ]; then ./q regression.sh; else ./regression.sh; fi 

On successfull run : You should be able to download the file containing the out put from the left hand side in jupyter notebook named regression.txt containing the regression analysis and working data along with the coefficients.

Result

You can see the The Pearsons correlation is 0.529809
You can see prediction that Using the formula y=a+(b*50) We forcast that spending $50 on advertising can result in $84.4028 in sales You can see the workings.

Cross architecture compatibility GPU & CPU

 This code can Run on both on CPU and GPU of below specs
 
 if you select a GPU device than Device: Intel(R) Graphics Gen9 [0x3e96] will process in 1 second.
 queue q(gpu_selector{});

if you select a CPU device than Device: Intel(R) Xeon(R) E-2176G CPU @ 3.70GHz will process in 3 seconds.
 queue q(=cpu_selector{});