Need help in Machine Learning

Greetings!

I'm a machine learning novice currently grappling with the Heart Disease UCI dataset from Kaggle. My dataset reveals several columns with missing values, and I consider all columns crucial for my analysis. Here's a breakdown of the missing data:

• id: 0 missing values
• age: 0 missing values
• sex: 0 missing values
• dataset: 0 missing values
• cp: 0 missing values
• trestbps: 59 missing values
• chol: 30 missing values
• fbs: 90 missing values
• restecg: 2 missing values
• thalch: 55 missing values
• exang: 55 missing values
• oldpeak: 62 missing values
• slope: 309 missing values
• ca: 611 missing values
• thal: 486 missing values
• num: 0 missing values

Given the significance of all columns, I'm seeking guidance on the most effective approach to handle these missing values. Imputation seems like a possibility, but are there superior methods for this situation? Any advice, particularly with illustrative examples, would be invaluable!

Thank you for your assistance!

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