This project builds a Machine Learning model to predict the likelihood of heart failure based on clinical and health-related attributes. The objective is to analyze patient data, identify important risk factors, and develop a predictive model that can assist in early diagnosis and preventive healthcare.
The project follows a complete ML pipeline including data preprocessing, exploratory data analysis (EDA), feature selection, model training, and evaluation.
The dataset contains medical attributes such as:
- Age
- Sex
- Chest Pain Type
- Resting Blood Pressure
- Cholesterol
- Fasting Blood Sugar
- Resting ECG Results
- Maximum Heart Rate Achieved
- Exercise Induced Angina
- ST Depression
- Target Variable (Heart Disease Presence)
The target variable indicates whether a patient is at risk of heart failure.
- Perform Exploratory Data Analysis (EDA)
- Identify important health indicators
- Build classification models
- Evaluate model performance
- Interpret results and extract insights
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
- Data Cleaning & Preprocessing
- Handling Missing Values
- Feature Encoding
- Data Visualization
- Train-Test Split
- Model Training (SVM, ANN)
- Model Evaluation (Accuracy, Confusion Matrix, etc.)
- Evaluation Metrics Used:
- Accuracy
- Precision
- Recall
- F1 Score
- Confusion Matrix
Accuracy-84.9 (ANN) Accuracy-80 (SVM)
- Download the repository.
- Install dependencies:
pip install -r requirements.txt- Open
Heart_Rate_Failure_Prediction.ipynb - Run all cells.
This is an end-to-end Machine Learning classification project focused on healthcare analytics.
Zeeshan Ali
AI & Data Science Student
Aspiring Machine Learning Engineer