EDA-and-Perform-Modelling-on-Ionosphere-Dataset-main

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.

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:

1. pandas
2. Numpy
3. Seaborn
4. Matplotlib
5. Sklearn
6. scikit-plot
7. pingouin

Steps Involved:

1. Importing the libraries
2. Loading the dataset
3. Data Preprocessing
4. train and test data split
5. Building the model
6. Compare model performance
7. selection model based on performance
8. Evaluation
9. Plot ROC and AUC curve

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%.