Скачать или смотреть 🎥 Heart Disease Prediction Project | ML Model Comparison & Deployment| Part 4 | Machine Learning

In this video, we dive deep into a Heart Disease Prediction project using multiple machine learning algorithms to identify the most effective model for accurate diagnosis.

✅ Models Tested:

Logistic Regression

Naive Bayes

Support Vector Machine (SVM)

K-Nearest Neighbors (KNN)

Decision Tree

Random Forest

XGBoost (XGB)

Artificial Neural Network (ANN)

📊 We evaluate each model on various performance metrics and select the best-performing one. The final model is saved and deployed for real-time predictions!

💻 Technologies Used:

Python

Scikit-learn

TensorFlow / Keras

XGBoost

Streamlit / Flask (for deployment)

🔧 This project demonstrates the complete ML pipeline:

Data preprocessing

Model training & evaluation

Best model selection

Saving the model

Deployment

If you're working on ML or health-related projects, this video is a must-watch!

Github Link:
Code Notebook: https://github.com/ankitmisk/Heart_Di...


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#MachineLearning #HeartDiseasePrediction #MLModels #Python #HealthcareAI #DataScience #Deployment #AIinHealthcare