Pet projects on Machine Learning
This folder contains several projects on Machine Learning using Python and necessary libraries and frameworks such as scikit-learn, numpy, Tensorflow, Keras, PyTorch etc.
HEART-DISEASE project: the used data can be found by the following link: https://archive.ics.uci.edu/ml/datasets/Heart+Disease The features in the processed database indicate the following categories:
- #3 (age) 3 age: age in years
- #4 (sex) 4 sex: sex (1 = male; 0 = female)
- #9 (cp) 9 cp: chest pain type (Value 1: typical angina; Value 2: atypical angina; Value 3: non-anginal pain; Value 4: asymptomatic)
- #10 (trestbps) 10 trestbps: resting blood pressure (in mm Hg on admission to the hospital)
- #12 (chol) 12 chol: serum cholestoral in mg/dl
- #16 (fbs) 16 fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
- #19 (restecg) 19 restecg: resting electrocardiographic results (Value 0: normal; Value 1: having ST-T wave abnormality; Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria)
- #32 (thalach) 32 thalach: maximum heart rate achieved
- #38 (exang) 38 exang: exercise induced angina (1 = yes; 0 = no)
- #40 (oldpeak) 40 oldpeak = ST depression induced by exercise relative to rest
- #41 (slope) 41 slope: the slope of the peak exercise ST segment (Value 1: upsloping; Value 2: flat; Value 3: downsloping)
- #44 (ca) 44 ca: number of major vessels (0-3) colored by flourosop
- #51 (thal) 51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
- #58 (num) (the predicted attribute) 58 num: diagnosis of heart disease (angiographic disease status)