https://github.com/ninadpatil09/heart_disease_detection_analysis

The Heart Disease Detection Analysis aims to create a predictive model for identifying individuals at risk of heart disease. Using a dataset with attributes like age, sex, and health metrics, the project focuses on distinguishing patients with and without heart disease.
https://github.com/ninadpatil09/heart_disease_detection_analysis

data-analysis data-cleaning data-science data-visualization machine-learning

Last synced: 8 days ago
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The Heart Disease Detection Analysis aims to create a predictive model for identifying individuals at risk of heart disease. Using a dataset with attributes like age, sex, and health metrics, the project focuses on distinguishing patients with and without heart disease.

• Host: GitHub
• URL: https://github.com/ninadpatil09/heart_disease_detection_analysis
• Owner: ninadpatil09
• Created: 2023-08-18T20:28:32.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-18T11:36:08.000Z (8 months ago)
• Last Synced: 2024-05-07T17:59:18.410Z (6 months ago)
• Topics: data-analysis, data-cleaning, data-science, data-visualization, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 699 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Heart_Disease_Detection_Analysis

The Heart Disease Detection Analysis aims to create a predictive model for identifying individuals at risk of heart disease. Using a dataset with attributes like age, sex, and health metrics, the project focuses on distinguishing patients with and without heart disease. Through data preprocessing and machine learning, the model provides early detection and risk assessment, aiding medical professionals in timely interventions. This analysis merges healthcare and machine learning to enhance heart health predictions, fostering better patient outcomes.

# About the Dataset

- Age: Age of the patient in years.

- Sex: Sex of the patient, categorized as Male (M) or Female (F).

- Chest Pain Type: Describes the type of chest pain experienced by the patient. Categories include Typical Angina (TA), Atypical Angina (ATA), Non-Anginal Pain (NAP), and Asymptomatic (ASY).

- Resting Blood Pressure (RestingBP): Resting blood pressure measured in mm Hg.

- Cholesterol: Serum cholesterol level measured in mm/dl.

- Fasting Blood Sugar (FastingBS): Indicates whether fasting blood sugar is greater than 120 mg/dl (1) or not (0).

- Resting Electrocardiogram (RestingECG): Describes the results of the resting electrocardiogram. Categories include Normal (Normal), ST-T wave abnormality (ST), and left ventricular hypertrophy (LVH).

- Maximum Heart Rate Achieved (MaxHR): The maximum heart rate achieved during exercise, a numeric value between 60 and 202.

- Exercise-Induced Angina (ExerciseAngina): Indicates whether the patient experienced exercise-induced angina. Categorized as Yes (Y) or No (N).

- Oldpeak: Represents the ST depression induced by exercise, measured in depression units.

- ST Slope: Describes the slope of the peak exercise ST segment. Categories include Upsloping (Up), Flat (Flat), and Downsloping (Down).

- Heart Disease (Output Class): The target output class, indicating whether the patient has heart disease (1) or is considered normal (0).

# Python libraries used 

- Pandas

- Numpy

- Seaborn

- Matplotlib

- Sklearn

# Workflow

- Importing Libraries

- Loading the Dataset

- Explore Dataset

- Data Cleaning and manipulate

- Handling Outliers

- Data Visualization

- Conclusion