This notebook looks into using various machine learning and data science libraries in attempt to build a machine learning model capable of predicting wheather or not someone has heart disease based on their medical attributes
approaches
- problem defination
- data
- evaluation
- features
- modeling
- exprimentation
In a statement,
Given clinical parameters about a patient, can we predict wheather or not they have heart diseases ?
The original data is came from the cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+disease . Data is also available on kaggle https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data
if we can reach 95% or more accuracy whether or not patient has heart disease during the proof of concept, we'll pursue the project
This is where you get each and every information about the data Create a data dictionary
- id (Unique id for each patient)
- age (Age of the patient in years)
- sex (1-Male/0-Female)
- cp chest pain type ([typical angina, atypical angina, non-anginal, asymptomatic])
- trestbps resting blood pressure (resting blood pressure (in mm Hg on admission to the hospital))
- chol (serum cholesterol in mg/dl)
- fbs (if fasting blood sugar > 120 mg/dl)
- restecg (resting electrocardiographic results)
- -- Values: [normal, stt abnormality, lv hypertrophy]
- thalach: maximum heart rate achieved
- exang: exercise-induced angina (True/ False)
- oldpeak: ST depression induced by exercise relative to rest
- slope: the slope of the peak exercise ST segment
- ca: number of major vessels (0-3) colored by fluoroscopy
- thal: [normal; fixed defect; reversible defect]
- target: [0 - not having heart disease, 1 - has heart disease]