This is a personal practice mini-project.

It is inspired by the "Classification Practice Project" from the "Build Deep Learning Models with TensorFlow" Skill Path offered by Codecademy.

In this project, I predict whether a patient with heart failure stops living during the follow-up period, based on the following features:

1. "age": Age
2. "anaemia": Decrease of red blood cells or hemoglobin (boolean)
3. "creatinine_phosphokinase": Level of the CPK enzyme in the blood (mcg/L)
4. "diabetes": If the patient has diabetes (boolean)
5. "ejection_fraction": Percentage of blood leaving the heart at each contraction (percentage)
6. "high_blood_pressure": If the patient has hypertension (boolean)
7. "platelets": Platelets in the blood (kiloplatelets/mL)
8. "serum_creatinine": Level of serum creatinine in the blood (mg/dL)
9. "serum_sodium": Level of serum sodium in the blood (mEq/L)
10. "sex": Woman or man (binary)
11. "smoking": If the patient smokes or not (boolean)
12. "time": Follow-up period (days)

Since only 1 outcome has to be predicted for each patient, only 1 "label" exists for this model:

1. "DEATH_EVENT": If the patient deceased during the follow-up period (boolean)

My personal best:

              precision    recall  f1-score   support

           0       0.87      0.87      0.87        39
           1       0.76      0.76      0.76        21

    accuracy                           0.83        60
   macro avg       0.82      0.82      0.82        60
weighted avg       0.83      0.83      0.83        60