From Data to Diagnosis: Predicting Heart Disease with ML

?? Excited to share my latest project on Heart Disease Prediction! ??

I've been working on building and evaluating machine learning models to predict heart disease using a dataset from the UCI Machine Learning Repository. This project has been a fantastic journey, covering everything from data preprocessing and exploratory data analysis (EDA) to model selection, evaluation, and hyperparameter tuning.

??? Key Highlights:

• Data Preprocessing: Handled missing values and outliers to ensure clean, reliable data.
• Exploratory Data Analysis: Visualized feature relationships and identified key correlations.
• Model Evaluation: Tested multiple classifiers, including Logistic Regression, Random Forest, and Gradient Boosting, to find the best-performing model.
• Hyperparameter Tuning: Used GridSearchCV to fine-tune the best model and achieve optimal accuracy.

?? Technologies Used:

Python, Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.

?? Results:

Achieved a comprehensive understanding of the dataset, identified the most effective model, and improved its performance through careful tuning.

?? Check out the full project on GitHub: [Heart Disease ML Model]

I'm excited to apply these insights and techniques to more complex datasets and real-world problems. Feel free to connect and share your thoughts or collaborate on future projects!

#MachineLearning #DataScience #Python #HeartDiseasePrediction #ArtificialIntelligence #Healthcare #EDA #GitHub