AID362 Bioassay Classification and Regression (Neuronal Network and Extra Tree) with Machine Learning
I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my regression model, you can predict whether it is Inactive or Inactive (Neural Network or Extra Trees). In classification (Neural Network or Extra Trees), you can easily classify the provided data whether it is Inactive or Active.
Example:
`###Regressor Model
model_emir_regress_predict = ExtraTreesRegressor(criterion="mse",max_features="auto",
n_jobs=-1,n_estimators=1)
model_emir_regress_predict = MLPRegressor(hidden_layer_sizes=(200,),activation="relu",
#solver="adam",batch_size="auto")`
###Classifier Model
`model_ml_emir = ExtraTreesClassifier(n_estimators=23,criterion="gini",max_features="auto",random_state=131)
model_ml_emir = MLPClassifier(activation="relu",
#solver="adam",
#batch_size=200,
#hidden_layer_sizes=(100,),random_state=17,
#learning_rate='constant',
#alpha=0.0006,
#beta_1 = 0.9,
#beta_2=0.4)`
I am happy to present this software to you!
###The coding language used:
Python 3.9.6
###Libraries Used:
Sklearn
Pandas
Numpy
Matplotlib
Pylab
Plotly
business, earth and nature, health, biology, chemistry, biotechnology, Machine Learning, Python, Artificial Intelligence, Neural Networks, Extra Tree Classifier, Extra Tree Regressor, Software
Name-Surname: Emirhan BULUT
Contact (Email) : emirhan.bulut@turkiyeyapayzeka.com
LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/
Data Source: DataSource
Official Website: https://www.emirhanbulut.com.tr
