Hello everyone,
I am a beginner in machine learning, and I am currently working with the Heart Disease UCI dataset downloaded from Kaggle. Upon exploring the data, I noticed that several columns have missing values, and I believe all these columns are important for the analysis. Here is a summary of the missing values in my dataset:
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
Could anyone please guide me on how to handle these missing values effectively, considering all columns are significant? Should I use imputation techniques, or are there better methods for this scenario? Any advice, especially with examples, would be greatly appreciated!
Thank you!
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