- Executed a comprehensive analysis of the Heart Disease Dataset by harnessing the power of Pandas and NumPy for meticulous data manipulation, encompassing cleaning, transformation, and in-depth exploratory analysis.
- Employed Seaborn and Matplotlib to craft compelling visualizations, unveiling key patterns and insights that informed the analysis.
- Developed a robust Logistic Regression model, meticulously trained and tested, resulting in an impressive accuracy rate exceeding 90%.
- Conducted a thorough evaluation of the model’s performance using a suite of metrics, including the accuracy score, confusion matrix, and classification report, ensuring the model’s reliability and effectiveness in predictive tasks.
-
Notifications
You must be signed in to change notification settings - Fork 0
A complete Exploratory Data Analysis on Heart Disease Dataset and then visualizing the insights and information found from the the dataset , then creating our Machine Learning Classification Model of Logistic Regression and will predict the test data and future values of patients and see through our Model if they are Heart Disease Patients or not
HUZIBRO/CardioCare-Predicting-Heart-Disease-with-Data-Analysis
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
About
A complete Exploratory Data Analysis on Heart Disease Dataset and then visualizing the insights and information found from the the dataset , then creating our Machine Learning Classification Model of Logistic Regression and will predict the test data and future values of patients and see through our Model if they are Heart Disease Patients or not
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published