kaggle-eda-heart-attack-prediction's Introduction

Heart Attack Prediction

Kaggle Data Predictions Regarding Heart Attacks

1.2 Recognizing Variable in Dataset

Variable definitions in the Dataset

Age: Age of the patient
Sex: Sex of the patient
exang: exercise induced angina (1 = yes; 0 = no)
ca: number of major vessels (0-3)
cp: Chest Pain type chest pain type
- Value 1: typical angina
- Value 2: atypical angina
- Value 3: non-anginal pain
- Value 4: asymptomatic
trtbps: resting blood pressure (in mm Hg)
chol: cholestoral in mg/dl fetched via BMI sensor
fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
rest_ecg: resting electrocardiographic results
- Value 0: normal
- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
thalach: maximum heart rate achieved
target: 0= less chance of heart attack 1= more chance of heart attack

Additional variable descriptions

age - age in years
sex - sex (1 = male; 0 = female)
cp - chest pain type (1 = typical angina; 2 = atypical angina; 3 = non-anginal pain; 0 = asymptomatic)
trestbps - resting blood pressure (in mm Hg on admission to the hospital)
chol - serum cholestoral in mg/dl
fbs - fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
rest_ecg - resting electrocardiographic results (1 = normal; 2 = having ST-T wave abnormality; 0 = hypertrophy)
thalach - maximum heart rate achieved
exang - exercise induced angina (1 = yes; 0 = no)
oldpeak - ST depression induced by exercise relative to rest
slope - the slope of the peak exercise ST segment (2 = upsloping; 1 = flat; 0 = downsloping)
ca - number of major vessels (0-3) colored by flourosopy
thal - 2 = normal; 1 = fixed defect; 3 = reversable defect
num - the predicted attribute - diagnosis of heart disease (angiographic disease status) (Value 0 = < diameter narrowing; Value 1 = > 50% diameter narrowing)

for i, z in list(zip(categoric_var, categoric_axis_name)): plt.figure(figsize=(8, 5)) sns.countplot(i, data=df, hue="target") plt.title(i + " - target", fontdict=title_font) plt.xlabel(z, fontdict=axis_font) plt.ylabel("Target", fontdict=axis_font) plt.tight_layout() plt.show()

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