Heart Disease Prediction Using Machine Learning | End-to-End ML Project in Python

In this video, I demonstrate an end-to-end Machine Learning modeling project on the Heart Disease dataset using Python.
This project is designed for beginners, intermediate learners, and aspiring data scientists who want to understand how Machine Learning is applied in real-world healthcare problems.

🔍 What You Will Learn in This Video

✔ Understanding the Heart Disease dataset
✔ Data cleaning and preprocessing
✔ Exploratory Data Analysis (EDA)
✔ Feature selection & scaling
✔ Implementation of multiple Machine Learning models
✔ Model comparison and evaluation
✔ Accuracy, confusion matrix & performance metrics
✔ Selecting the best model for prediction

🤖 Machine Learning Models Covered

Logistic Regression

K-Nearest Neighbors (KNN)

Decision Tree

Random Forest

Support Vector Machine (SVM)

Naive Bayes

Gradient Boosting / XGBoost (if used)

🧠 Why This Project is Important

Heart disease is one of the leading causes of death worldwide. Using Machine Learning, we can:

Predict heart disease at an early stage

Assist doctors in decision-making

Build intelligent healthcare systems

🛠 Tools & Technologies Used

Python

Pandas, NumPy

Matplotlib, Seaborn

Scikit-Learn

👨‍💻 Who Should Watch This Video?

Data Science Beginners

Machine Learning Students

Python Developers

Healthcare Analytics Enthusiasts

Final Year Project Students

📌 Dataset Used: Heart Disease Dataset
📌 Language: Python
📌 Category: Machine Learning / Data Science

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GitHub link----https://github.com/imtiaz231