Heart disease is a leading cause of death worldwide. Early detection and prevention are crucial for improving patient outcomes. This project aims to develop a machine learning model that can accurately predict the risk of heart disease based on various patient-related factors.
We used a dataset of 412 patients with heart disease and 294 patients without heart disease. The dataset included information on patient demographics, medical history, and lifestyle factors. We preprocessed the data by imputing missing values, normalizing continuous variables, and one-hot encoding categorical variables.
We used a variety of machine learning algorithms to develop the prediction model, including logistic regression, decision trees, random forests, and support vector machines. We tuned the hyperparameters of each algorithm using cross-validation to optimize their performance.
The best-performing model was a logistic regression model with an accuracy of 82.61% on the test set. The confusion matrix and classification report demonstrated that the model effectively distinguished between patients with and without heart disease.
The developed heart disease prediction model exhibited promising results in identifying patients at risk of developing heart disease. The model can be further refined and deployed as a tool to assist healthcare professionals in early detection and prevention of heart disease.
Predicting house prices is a challenging task due to the complex relationship between various factors that influence the value of a property. This project aims to develop a machine learning model that can accurately predict the sale price of houses based on a variety of features.
We used a dataset of 21,613 houses sold in King County, Washington. The dataset included information on house characteristics, location, and sale price. We preprocessed the data by imputing missing values, normalizing continuous variables, and one-hot encoding categorical variables.
We used a variety of machine learning algorithms to develop the prediction model, including linear regression, decision trees, random forests, and support vector machines. We tuned the hyperparameters of each algorithm using cross-validation to optimize their performance.
The model was able to accurately predict the sale price of houses, even for houses with unique features or located in less desirable neighborhoods.
The developed house price prediction model exhibited promising results in predicting the sale price of houses. The model can be further refined and deployed as a tool to assist real estate professionals and homebuyers in making informed decisions.