Health-care-ML

This project provides comprehensive visualizations of healthcare data and machine learning models, aimed at making complex information easy to interpret and analyze. The repository covers a wide range of visualization techniques to help understand both the dataset and the performance of machine learning models. The purpose of this project is to comapare Classification algorithms implemented on Lung Cancer Dataset

Working

I have selected 10 of the following classification algorithms that have been used in this project

1. Logistic Regression
2. K-Nearest Neighbors (KNN)
3. Decision Tree
4. Support Vector Machines (SVM)
5. Naive Bayes
6. Random Forest
7. Gradient Boosting
8. Neural Networks
9. AdaBoost
10. XGBoost

Next, i constructed models for each of the algorithms mentioned above.I compared these algorithms using the following evaluation metrics.

1. Accuracy
2. Precision
3. F1 Score
4. Recall Score
5. Confusion Matrix