I am a data enthusiast with a passion for leveraging machine learning to solve real-world problems. My focus lies in the healthcare domain, where I aim to utilize data-driven approaches to improve patient outcomes and advance medical research.
- Data Processing: Expertise in cleaning, transforming, and preparing datasets for analysis.
- Machine Learning: Proficient in training and tuning various machine learning models.
- Evaluation Metrics: Skilled in evaluating model performance using metrics such as accuracy, precision, recall, F1 score, and AUC curves.
- Programming Languages: Python, R
- Tools and Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
- Data Processing: Implemented robust data cleaning and preprocessing steps to handle missing values, outliers, and feature engineering.
- Model Training: Trained multiple machine learning models, including Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.
- Model Evaluation: Assessed model performance using a variety of evaluation metrics, with a focus on the Area Under the Curve (AUC) for ROC analysis.
- Visualization: Created detailed visualizations to interpret model results and feature importances.
Feel free to reach out if you have any questions or suggestions regarding this project.
- Email: [email protected]
- LinkedIn: Ahmad Ali Rafique
Thank you for visiting my repository!