Machine Learning Business Intelligence

Nana Adwoa Amoyaw

Ellicott city, Maryland ********@*****.*** 302-***-****

Education:

Bachelor of Science in Information Systems University of Delaware

Technical Skills:

•Programming Languages: Python, JavaScript, R, Shell

•Machine Learning: Regression, Classification, Clustering, Neural Networks

•Data Manipulation: Sale force, Excel, SQL, Azure, Tableau

EXPERIENCE

Data Engineer, Transamerica, Cedar Rapids, Iowa

•Developed robust data integration solutions, leveraging SQL and ETL tools, to consolidate disparate data sources into a unified data warehouse.

•Led the optimization of data storage and retrieval systems, implementing data partitioning and indexing techniques, resulting in a 30% reduction in query response time for business intelligence reporting.

•Assisted in data cleaning, transformation, and analysis, supporting data-driven decision-making processes.

•Performed data quality assessments and ensured data accuracy and completeness.

•Deep analysis of data to identify patterns trends and insights.

Data Analyst, United Health Group, Minneapolis, MN

•Built a salesforce database tracking startups both in healthcare and technology.

•Researched potential channels and developed a market growth strategy to expand Optum’s startup studio.

•Execute client onboarding strategies to drive user buy-in by demonstrating the database to various business unit leadership.

•Created and automated reports and dashboards using tools such as Tableau.

•Manage and manipulate structured data, focusing on building effective business intelligence tools.

Projects:

Marketing Analytics project:

Successfully executed a customer churn prediction project for a telecommunication company. Leveraged machine learning algorithms and historical customer data to identify key churn factors and build a predictive model. Provided actionable insights to implement targeted retention strategies, leading to a significant reduction in customer churn and improved business profitability.

Measuring the correlation between a person’s lifestyle and likelihood of getting heart disease

For this project we used R studio and the libraries: dplyr, corrplot and ggplot2.

Dataset was obtained from the hospital, specifically: Age, Gender, Blood Pressure, Cholesterol Levels, Body Mass Index (BMI), Physical Activity Level, Smoking Status and Heart Disease Status (0 = No, 1 = Yes). To obtain results we used data processing, visualization, and hypothesis testing. Our results proved that. Volunteer work & conferences significant correlations between lifestyle factors and the likelihood of developing heart disease. The findings suggest that maintaining a healthy lifestyle, including regular physical activity, managing blood pressure, and monitoring cholesterol levels, can play a vital role in reducing heart disease risk. These insights can contribute to public health initiatives and personalized recommendations for individuals aiming to prevent heart disease.

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