Heart Disease Prediction Using Machine Learning In this project, I explored the intersection of healthcare and technology by developing a machine learning model aimed at predicting heart disease using logistic regression. The primary objective was to analyze clinical data and identify key indicators that could help classify patients’ risk levels concerning heart disease.
Process Overview: Data Preprocessing: I began with thorough data preprocessing, which included handling missing values, normalization, and feature selection to ensure a clean and robust dataset for analysis.
Exploratory Data Analysis (EDA): Conducting EDA allowed me to visualize and understand the underlying patterns in the data. This step helped identify significant factors such as age, cholesterol levels, blood pressure, and ECG readings that correlate with heart disease.
Model Implementation: Utilizing logistic regression, I developed a model to classify patients based on their clinical features.Inspiration
Built With
- machine-learning
- matplotlib
- numpy
- pandas
- python
- scikit-learn
- seaborn
- statsmodels
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