Classification Task with 6 Different Algorithms using Python

Here are 6 classification algorithms to predict mortality with Heart Failure; Random Forest, Logistic Regression, KNN, Decision Tree, SVM, and Naive Bayes to find the best Algorithm.

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Introduction

In this blog post, I will use 6 different classification algorithms to predict mortality with heart failure.

To do so, we will use classification algorithms.

Classification- Designed in Canva

Here are the algorithms that I will use;

• Random Forest
• Logistic Regression
• KNN
• Decision Tree
• SVM
• Naive Bayes

And after that, I will compare the results according to the;

• Accuracy
• Precision
• Recall
• F1 score.

That will be longer than my other blog post, yet after reading this article, you will probably have a huge knowledge about Machine Learning Classification Algorithms and Evaluation metrics.

If you want to know more about Machine Learning terms, here is my blog post, Machine Learning A-Z Briefly Explained.

Now let’s start with the data.

Content Table

· Introduction
· Data Exploration
· Data Visualization
· Feature Selection
  ∘ PCA
  ∘ Correlation Graph
· Model Building
  ∘ Train- Test Split
· Model Evaluation Metrics
  ∘ Random Forest Classifier
  ∘ Logistic Regression
· KNN
  ∘ Decision Tree
  ∘ Support Vector Machine
  ∘ Naive Bayes
· Prediction Dictionary
  ∘ Most Accurate Model
  ∘ Model with Highest Precision
  ∘ Model with Highest Recall
  ∘ Model with Highest F1 Score
· Conclusion

Data Exploration

Here is the dataset from the UCI Machine Learning repository, which is an open-source website, you can reach many other datasets, which…