Stephanie Nworgu

Data Scientist with expertise in predictive analytics, machine learning, and data visualization. Skilled in Python, R, SQL, and Tableau for developing data-driven solutions. Proven track record in sustainability initiatives, predictive modeling, and delivering actionable insights to drive decision-making.

• Daly Memorial Endowed
• Mathematics Development
• Increased student engagement through sustainability campaigns, driving awareness and participation in eco-friendly practices.

Employee Attrition Prediction, Python, Random Forest Classifier, Pandas, Scikit-learn, Developed a machine learning model to predict employee attrition using a Random Forest Classifier., Preprocessed and analyzed key features such as Age, Monthly Income, Job Satisfaction, and Distance from Home., Evaluated the model's performance using precision, recall, F1-score, and confusion matrix., Delivered insights to improve employee retention strategies by identifying key factors contributing to attrition. Backues Neural Net / Cellpose Explainer Project 2024, Python, TensorFlow, Cellpose 2.0, Analyzed intermediate outputs and weights of U-Net layers to understand segmentation decisions in APBs, advancing explainable deep learning techniques., Compared segmentation models using saliency maps, testing their robustness to boundary and texture variations in TEM images., Investigated the impact of feature alterations on model predictions, leading to refined segmentation styles for APB detection. Predictive Analytics Dashboard, R, Tableau, Built a dashboard to forecast academic performance, achieving an R² of 0.87 for prediction accuracy.

• Data Science Methodology, IBM, 10/01/24, 4TF3JXTN2K2B
• Python for Data Science, AI & Development, IBM, 10/01/24, 7ZZPLISNYOZE
• Learn to Program in Java, edX, 08/01/20, abf9b580de82426780c59f4127b8dd13