This repository contains a Python-based machine learning project that predicts the presence of heart disease using the UCI Heart Disease dataset. The project compares multiple classification algorithms and evaluates their performance using various metrics.
📁 Project Structure Main.ipynb — Jupyter Notebook containing data exploration, preprocessing, model training, evaluation, and improvement attempts.
📦 Required Dependencies To run this project, you’ll need:
pandas
numpy
matplotlib
seaborn
scikit-learn
Install them via:
bash Copy Edit pip install ucimlrepo pandas numpy matplotlib seaborn scikit-learn 📈 Workflow Overview Data Fetching
Loads the Heart Disease dataset using ucimlrepo.
Data Exploration
Inspects feature and target datasets for structure and missing values.
Visualizes distributions and correlations.
Preprocessing
Standardizes numerical features using StandardScaler.
Splits data into training and testing sets.
Model Training & Evaluation
Trains multiple models:
Logistic Regression
Decision Tree Classifier
Random Forest Classifier
Evaluates models using:
Accuracy
Recall
ROC AUC
Classification Reports
Model Improvement
Experiments with model parameters and approaches for better performance.
📊 Results Performance metrics for each classifier are displayed in the notebook, along with visualizations and improvement strategies.
📌 Notes The dataset is sourced directly from the UCI Machine Learning Repository.
Future improvements could include hyperparameter tuning, feature engineering, or trying advanced models like XGBoost.
📑 License This project is open-source under the MIT License.