Anuhya Balineni

Data Science graduate skilled in machine learning, data visualization, and SQL, with a strong track record in improving model accuracy and decision-making efficiency. Passionate about leveraging data-driven insights to solve real-world business challenges. Proficient in Python, Power BI, and Tableau with hands-on experience in predictive modeling and data storytelling.

• Power BI Data Analyst Associate, Microsoft
• Python for Data Science and Machine Learning, Coursera
• SQL for Data Science, Coursera

Mall Customer Segmentation, Conducted exploratory data analysis (EDA) on 5,000+ customer profiles, segmenting consumers for targeted marketing., Applied K-Means clustering to categorize purchasing behaviors, improving campaign effectiveness., Developed Python-based visualizations, reducing analysis time for marketing teams by 30%. 

Chronic Disease Prediction, Processed patient health data (age, BMI, blood pressure, glucose levels) for early disease detection., Optimized Logistic Regression, Random Forest, and XGBoost models, achieving 80% accuracy., Integrated feature selection techniques to enhance model interpretability and prediction reliability. 

Employee Attrition Prediction, Conducted EDA on workforce data to uncover attrition patterns, leveraging correlation analysis., Built classification models, boosting prediction accuracy to 85%, aiding HR in proactive retention strategies., Documented findings on GitHub, making project insights accessible to developers and stakeholders.