We are inspired by

The detection of Mass-Giving field- The Higgs Field by the Large Hadron on 4 July in 2012

The Machine Learning application detects the presence of Higgs Boson

Firstly we used Machine learning and AI Principles using Python. We used TensorFlow, Scikit learn for Random Forest Classifier, Gradient Booster Classifier and Adaboost Classifier. We used h2o software to make use of more AI Tech. We also imported Deep Learning Estimator to calculate model accuracy, and used Matplotlib for visualizations

Interpreting large and complex data using definite capacity was daunting. The limited accuracy of the output also posed a challenge to the comprehension of results.

But we are proud of our project and team

By using the resources at hand, we were able to generate a binary output for the presence of Higgs Boson. We were also able to draw comparison between the output and the defined results

We learned

A systematic way to generate and classify binary outputs. We explored new methods and interfaces to systematically interpret and draw results for the presence/ absence of Higgs Boson

Now that we developed a Machine Learning Binary Interface for the detection of Higgs Boson, Our next step would be to modify and draw results for determining presence of more subatomic particles, dark matter and even theorize one for the yet discovered Graviton

Built With

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