Heart Disease Prediction Model
Budget: ₹750 – ₹1,250 INR
I have developed and applied a machine learning model to predict heart disease with an impressive 87% accuracy, marking a 20% improvement over the baseline model. Utilizing Python and scikit-learn, I have preprocessed and analyzed publicly available datasets, effectively handling missing values and outliers to enhance model performance by 10%.
Key Requirements:
- Develop a machine learning model for heart disease prediction
- Use publicly available datasets for model training and validation
- Implement K-Nearest Neighbors imputation to handle missing data
- Conduct in-depth feature engineering to improve model accuracy
Ideal Skills and Experience:
- Proficiency in Python and scikit-learn for machine learning tasks
- Experience with data preprocessing, including handling missing values and outliers
- Strong understanding of feature engineering techniques
- Ability to analyze and interpret model performance metrics, such as F1-score
The project has successfully identified three key features that drive the most accurate heart disease predictions, increasing the model's F1-score by 9%. I'm looking for skilled collaborators to further enhance and validate the model's predictive capabilities.
Key Requirements:
- Develop a machine learning model for heart disease prediction
- Use publicly available datasets for model training and validation
- Implement K-Nearest Neighbors imputation to handle missing data
- Conduct in-depth feature engineering to improve model accuracy
Ideal Skills and Experience:
- Proficiency in Python and scikit-learn for machine learning tasks
- Experience with data preprocessing, including handling missing values and outliers
- Strong understanding of feature engineering techniques
- Ability to analyze and interpret model performance metrics, such as F1-score
The project has successfully identified three key features that drive the most accurate heart disease predictions, increasing the model's F1-score by 9%. I'm looking for skilled collaborators to further enhance and validate the model's predictive capabilities.