Power Bi Machine Learning

YAYA BARROW

347-***-**** / *********@*****.***

https://barrow719.github.io/Data/Projects/index.html https://www.linkedin.com/in/yaya-barrow/

Bronx, NY 10472

EDUCATION

University of Wisconsin-La Crosse (UWL), La Crosse, WI December 2024

• Master of science in Applied Statistics

University of The Gambia, The Republic of the Gambia December 2019

• Bachelor of science in Mathematics

SKILLS

Excel

Tools: Pivot Table, Pivot Chart,

VLOOKUP, HLOOKUP, VBA

Power Bi

Tools: DAX, API, Power Query, Bookmarks

SQL

Software: MySQL, PostgreSQL, Pgadmin

R Programming

Packages: Caret, Tidyverse, Car, dplyr

Python

Libraries: Pandas, Matplotlib, NumPy

Tableau

Tools: Calculated field, Aggregations

PROJECTS

● CONNECTICUT TRAFFIC STOP ANALYSIS - EXCEL VISUALIZATION: Cleaned and transformed 268,669 entries with 25 variables, analyzing trends by gender, age, and time. Created dashboards showing higher speeding violations among male and younger drivers, with more traffic stops in peak months (March, October) and weekdays (Friday, Saturday), reflecting increased law enforcement activity during these periods.

● COMPREHENSIVE ORDER TRACKER Power BI Visualization: Analyzed product order data, visualizing metrics such as price, items, discounts, and revenue across regions and demographics. Identified key trends, including higher revenue from adults and consistent purchasing behavior across age groups. Recommended marketing strategies tailored to regional payment preferences, with a focus on optimizing sales processes, reducing cancellations, and targeting peak sales periods to enhance revenue and efficiency.

● SQL Analysis of Pizza Sales and Consumer Behavior: Leveraged advanced SQL techniques, including JOIN, CASE, GROUP BY, and aggregate functions, to extract actionable insights from complex datasets, uncovering key business trends such as sales patterns, revenue by category, and consumer purchasing behaviors.

● Machine Learning for Purchase Prediction: Developed and optimized models (Logistic Regression, Random Forest, Decision Tree) in Python to predict customer purchase behavior, improving marketing strategies with data-driven insights.

● Student Depression Prediction: Developed machine learning models to predict student depression based on factors like academic pressure, study satisfaction, and dietary habits. Conducted extensive EDA and data visualization using Python libraries like Scikit-learn, Pandas, Seaborn, and Matplotlib. Achieved 84.2% accuracy and 0.92 AUC-ROC. WORK EXPERIENCE

University of Wisconsin-La Crosse, Mathematics and Statistics Department, La Crosse, WI Statistics Tutor August 2023 – January 2025

Mentored students in elementary statistics, specializing in statistical computing and analytics. Adapted teaching methods to diverse learning styles, providing hands-on guidance using Excel, SPSS, and R for data visualization, analysis, and interpretation. The Gambia National Petroleum Corporation

Trainee Geophysicist January 2022 – August 2023

Processed and analyzed onshore and offshore data using Excel, Kingdom software, and Python, ensuring data accuracy and clarity. Cleaned and organized datasets, improving data quality and reliability. Created visualizations in Python to extract insights and support decision-making in collaboration with senior team members.

Sifoe Senior Secondary School, The Gambia

Mathematics Teacher December 2020 – June 2021

Provided mathematics instruction, emphasizing core concepts and developing analytical and problem-solving skills. Guided students in solving mathematical problems, encouraging critical thinking. Managed and organized student data and grading records in Excel, utilizing advanced functions and formulas for accurate data entry and analysis.

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