Muslihat Gaffari

Data and analytics professional with a solid seven-year background in transforming complex data into actionable insights, improving decision-making and risk management. Proficient in utilizing advanced analytical tools such as Python, SQL, Tableau, Power BI, and Excel to optimize business processes. Demonstrated ability to communicate intricate findings to technical and non-technical stakeholders effectively. Collaborative team player with data warehousing, dashboard creation, and predictive analytics expertise.

• Google Data Analytics Certificate
• Google Advanced Data Analytics Certificate
• Google Business Intelligence Professional Certificate
• Microsoft Power BI Data Analyst Professional Certificate

• Prediction of Heart Disease Using Clinical Data

Developed a heart disease diagnostic model using clinical features, achieved over 80% accuracy with a k-Nearest Neighbors classifier through data exploration, preprocessing, feature selection, and hyper-parameter tuning, and evaluated performance with a confusion matrix, ROC curve, and AUC metrics.

• Breast Cancer Diagnosis Using Support Vector Machines and Decision Trees

Developed a breast cancer diagnostic model using the Wisconsin Breast Cancer Dataset, optimized a non-linear Support Vector Machine with Grid Search Cross Validation, performed feature selection with Random Forest, and created an interpretable decision tree as a quick and reliable diagnostic tool for clinicians.

• Predicting Student Enrollment in Calculus 2

Collaborated with a local university to develop a feature-driven model predicting Calculus 2 enrollment, used student data such as prior calculus experience, major, GPA, and exam scores, and identified key factors to guide targeted interventions for increasing enrollment.

• Marketing Data Analysis and Insights Report

Collected marketing data from various sources, cleaned it to remove inconsistencies or errors, and analyzed it to find essential patterns to identify key trends in customer behavior, campaign effectiveness, and market segmentation; used Tableau and Power BI visualizations to provide actionable recommendations that helped the company to focus its marketing efforts on the most profitable customer segments, improved engagement with targeted customers, and allocate resources to campaigns with the highest return on investment that contributed to more effective and efficient marketing strategies.

• Complaint Procedure Performance Report

Analyzed the complaint procedures data model over the past two years, tracked status changes, identified resolution time trends, broke down complaints by various dimensions, evaluated client satisfaction, and pinpointed the worst offending brokers to provide targeted recommendations for improvement ahead of the internal audit.