Predicting Heart Disease with Classification Machine Learning" is a project that involves building a machine learning model to predict the presence or absence of heart disease in a patient based on various features such as age, sex, blood pressure, cholesterol levels, etc.
Here are the general steps involved in this project:
Data collection: Collect a dataset of patient records that includes information about their age, sex, blood pressure, cholesterol levels, and other relevant features.
Data preprocessing: Clean and preprocess the data by removing missing values, handling outliers, and scaling the numerical features if necessary.
Exploratory data analysis: Perform exploratory data analysis to gain insights into the data and identify any patterns or relationships between the features and the target variable.
Feature engineering: Create new features or transform existing features to improve the performance of the machine learning model.
Model selection: Choose a classification machine learning algorithm, such as Logistic Regression, Decision Trees, Random Forest, or Support Vector Machines, and train it on the preprocessed and engineered data.
Model evaluation: Evaluate the performance of the model using various metrics such as accuracy, precision, recall, F1-score, and confusion matrix.
Hyperparameter tuning: Optimize the hyperparameters of the model using techniques such as grid search, random search, or Bayesian optimization to improve its performance.
Model deployment: Once the model has been trained and optimized, deploy it to a production environment where it can be used to make predictions on new patient records.
Overall, this project aims to use machine learning to predict the presence or absence of heart disease in patients, which can potentially help doctors and healthcare professionals make better decisions and improve patient outcomes.