End To End Machine Learning Project with Deployment | Heart Disease Prediction

🌐 Welcome to PrecisionDataScience! 📊 In today's video, we're diving into Heart Disease prediction dataset from Kaggle.
This dataset offers a playground for RandomForest as a Classification model, to test the capabilities to make accurate predictions. We'll unravel the nuances of this algorithm, dissecting its parameters with GridSearchCV method to make accurate predictions.
📈 Join us as we navigate through the features of the Heart Disease prediction dataset, understanding how Random Forest Classifier can be used to predict the Heart disease based on customer data. Our goal is to equip you with a solid understanding of Random Forest Classifier’s application in Heart disease prediction analysis and, deployment for your own data science projects.
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Channel: ‪@precisiondatascience‬ #DataScience #MachineLearning #RandomForest #Kaggle #HeartDiseasePrediction 🌐🔍

[@michaeljayanth6182]

Sprb explanation sir we are expecting much more but i have one doubt why random forest why not any other ml algo

[@precisiondatascience]

Thank you, sure will make more videos. Generally, Bagging and boosting algorithms tend to perform better. Since this was a toy dataset, I preferred Random forest as it provides better generalization and reduces overfitting. You can compare the accuracies from UCI ML repository.

[@precisiondatascience]

archive.ics.uci.edu/dataset/45/heart+disease You can compare the accuracies here.