Classification thanks to the SVM model with 7 years of ozone data with Machine Learning

I developed 2 machine learning software that predict and classify ozone day and non-ozone day. The working principle of the two is similar but there are differences. I got the dataset from ics.icu. Each software has a different mathematical model, Gaussian RBF and Linear Kernel, and classifications are visualized in different ways. I would be happy to present the software to you!

Example: model_ozone = PCA(n_components=72).fit(X_train)

model = svm.SVC(kernel='rbf', gamma=0.05, C=3)

model_ozone = svm.SVC(kernel='linear', C=3)

I am happy to present this software to you!

#Accuracy: 0.9602739726027397 Linear Model

#Auc Roc Curve Score: 0.5617836676217765 Linear Model

#Auc Roc Curve Score: 0.5 RBF Model

#Precision Score: 88.06896551724138 Linear Model

#Recall Score: 56.178366762177646 Linear Model

#F1 Score: 59.79183681221629 Linear Model

#Precision Score: 47.80821917808219 RBF Model

#Recall Score: 50.0 RBF Model

#F1 Score: 48.87955182072829 RBF Model

#Linear Confusion Matrix #[[1669 7] [ 115 13]] #Gauss RBF Confusion Matrix #[[1676 0] [ 128 0]]

Data Source: DataSource ###The coding language used:

Python 3.9.6

###Libraries Used:

Sklearn

Pandas

Numpy

Pylab

Matplotlib

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan.bulut@turkiyeyapayzeka.com

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Official Website: https://www.emirhanbulut.com.tr