PROJECT's KAGGLE LINK :

https://www.kaggle.com/code/dvaser/heart-attact-analysis-prediction/

HEART ATTACK ANALYSIS & PREDICTION

What will you learn from this project?

• EDA
• Missing Value Analysis
• Categoric and Numeric Features
• Standardization
• Box - Swarm - Cat - Correlation
• Outlier Detection
• ML Modelling and Tuning Machine Learning Model

Introduction

• A heart attact, also called a myocardial infarction, happens when a part of the heart muscle doesn't get enough blood.
• The more time that passes without treatment to restore blood flow, the greater the damage to the heart muscle.
• Coronary artery disease (CAD) is the main cause of heart attack.

Analysis Content

1. Python Libraries
2. Data Content
3. Read & Analyse Data
4. Missing Value Analysis
5. Unique Value Analysis
6. Categorical Feature Analysis
7. Numeric Feature Analysis
8. Standardization
9. Box Plot Analysis
10. Swarm Plot Analysis
11. Cat Plot Analysis
12. Correlation Analysis
13. Outlier Detection
14. Modelling
    1. Encoding Categorical Columns
    2. Scaling
    3. Train/Test Split
    4. Logistic Regression
    5. Logistic Regression Hyperparamater Tuning
15. Conclusion

---

Data Content

• 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

---