Heart Disease Prediction (UCI)
Budget: $10 – $30 USD
Overview
This project uses the UCI Heart Disease dataset to predict whether a patient has heart disease (binary classification). The notebook covers EDA, data cleaning, and model training with Logistic Regression and Random Forest.
Workflow
Load dataset (via Kaggle/UCI).
Explore and clean data (missing values, outliers, feature selection).
Train/test split.
Train Logistic Regression & Random Forest models.
Evaluate with accuracy and classification report.
Data
The dataset includes 14 attributes (e.g., age, sex, trestbps, chol, thalach, oldpeak, thal) and a binary target (target).
References
UCI Heart Disease dataset
Kaggle Heart Disease dataset
scikit-learn Logistic Regression
scikit-learn Random Forest
This project uses the UCI Heart Disease dataset to predict whether a patient has heart disease (binary classification). The notebook covers EDA, data cleaning, and model training with Logistic Regression and Random Forest.
Workflow
Load dataset (via Kaggle/UCI).
Explore and clean data (missing values, outliers, feature selection).
Train/test split.
Train Logistic Regression & Random Forest models.
Evaluate with accuracy and classification report.
Data
The dataset includes 14 attributes (e.g., age, sex, trestbps, chol, thalach, oldpeak, thal) and a binary target (target).
References
UCI Heart Disease dataset
Kaggle Heart Disease dataset
scikit-learn Logistic Regression
scikit-learn Random Forest
Related categories:
Python
Data Mining
Big Data Sales
Statistical Analysis
Data Science
Data Visualization
Data Analysis
Classification