Heart disease prediction using machine learning involves building a predictive model that can accurately identify the likelihood of a person having heart disease or not based on various health metrics and risk factors.
- Data Collection
- Data Preprocessing
- EDA
- Scaling the Data Using MinMaxScalar
- Model building
- Model Evaluation
- Hyperparater Tuning (got 94.21% accuracy with RandomForestClassifier)
- Saving the model using pickle
- Created Streamlit app
- Uploaded to Github
Install all project requirements to run this project locally
pip install -r requirements.txtRun the command to see the demo
streamlit run app.py
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Data Preprocessing: The importance of data cleaning and handling missing values. How to use MinMaxScaler for normalization to ensure that features contribute equally to the model performance.
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Algorithm Selection: Comparison of various machine learning algorithms to find the most suitable one for your dataset. Understanding the strengths and weaknesses of algorithms like Random Forest, SVM, and Logistic Regression.
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Hyperparameter Tuning: The impact of hyperparameters on the performance of the model.Techniques for hyperparameter tuning, such as GridSearchCV, to optimize model performance.
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Model Evaluation: Different metrics to evaluate the performance of the model, such as accuracy, precision, recall, and F1-score. The importance of cross-validation to ensure the robustness of the model.
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Building Applications: How to create a web application using Streamlit to make your model accessible to users. The basics of user interface design to create an intuitive and user-friendly application.
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Version Control: The importance of using GitHub for version control and collaboration. How to document your project and make it accessible to others.
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Feature Selection: Identifying which features are most important for predicting heart disease. Used feature importance scores from the Random Forest model to select the most impactful features.
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Model Overfitting: The model performed well on training data but poorly on test data. Implemented cross-validation and regularization techniques to prevent overfitting.
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Hyperparameter Tuning: Finding the optimal hyperparameters was time-consuming. Automated the process using GridSearchCV to systematically search for the best parameters.
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Deployment Issues: Integrating the model into a Streamlit application and ensuring it runs smoothly. Iterative testing and debugging to resolve integration issues and ensure the application works seamlessly.
If you have any feedback, please reach out to me at debopamdeycse19@gmail.com