The following project aims to predict class using various technical specifications (features) as input to the logistic regression algorithms.
issue no: #176
Database Description:
Number of Instances: 351
Number of Attributes: 35 including the class attribute
Attribute Information: Target column :-
Class Feature
Columns range- V1- V35
Libraries Involved:
pandas
Numpy
Seaborn
Matplotlib
Sklearn
scikit-plot
pingouin
Conclusion:- The given dataset have target varibale and it is a type of binary class (0,1) its a Supervised machine learning task,after performing varius supervised machine learning models, i found Logistic Regression is most suitable model. hence Logistic Regression is implimented the Area under the curve is given by this model is 93%.
EDA-and-Perform-Modelling-on-Ionosphere-Dataset
Project Description:
The following project aims to predict class using various technical specifications (features) as input to the logistic regression algorithms.
issue no: #176
Database Description:
Number of Instances: 351
Number of Attributes: 35 including the class attribute
Attribute Information: Target column :-
Class Feature Columns range- V1- V35
Libraries Involved:
Conclusion:- The given dataset have target varibale and it is a type of binary class (0,1) its a Supervised machine learning task,after performing varius supervised machine learning models, i found Logistic Regression is most suitable model. hence Logistic Regression is implimented the Area under the curve is given by this model is 93%.