Python Project- Predicting Cardiovascular Disease. Complete Data Analytic & Machine Learning project
In this video, we take a deep dive into a real-world Python project focused on predicting cardiovascular disease using data analytics and machine learning.
Whether you're a beginner or an experienced data enthusiast, this step-by-step tutorial will help you understand the entire workflow—from data preprocessing to model evaluation.
What You’ll Learn:
✅ Data collection and preprocessing (handling missing values, feature engineering)
✅ Exploratory Data Analysis (EDA) for deeper insights
✅ Building machine learning models (Logistic Regression, Random Forest, etc.)
✅ Model evaluation using accuracy, precision, recall & AUC-ROC
✅ Feature importance and model optimization
✅ Visualizing results and drawing actionable insights
📂 Resources Used:
Jupyter Notebook
Python (Pandas, NumPy, Matplotlib, Seaborn)
Machine Learning Libraries (Scikit-learn)
Cardiovascular Disease Dataset (Kaggle/UCI)
🚀 Perfect for:
Data science beginners looking for hands-on experience
Healthcare analytics enthusiasts
Python learners exploring machine learning applications
📌 Don’t forget to like, comment, and subscribe for more Python projects and data science tutorials!
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Here is the link to this project on my GitHub page https://github.com/FaeyO/Cardiovascul...
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link to my google drive to download dataset used : https://drive.google.com/drive/folder...
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