Machine Learning Data Science

Sevilay Kaya Alcicek

Data Scientist

A dedicated and enthusiastic Data Science graduate with strong foundation in predictive modeling, data processing and data mining algorithms. Proficient in scripting languages, particularly Python, to develop robust solutions for complex analytical challenges. Adept at translating intricate data sets into insightful visual representations that facilitate data-driven decision making. Identified, analyzed and interpreted trends on complex data sets using supervised and unsupervised learning techniques.

**********@*****.***

+1-512-***-****

Austin, Texas

www.linkedin.com/in/sevilay-kaya-b18228a2

WORK EXPERIENCE

Careerera boot camp - 6 months internship

01/2022 - 07/2023

● Acquired proficiency in Python for data manipulation, visualization, and machine learning.

● Gained hands-on experience in implementing data science projects from inception to deployment.

Achievements/ Tasks

● Worked on Python Libraries

● Turned intricate raw data into engaging visualizations, enhancing data understanding and driving strategic insights, along with crafting a Tableau dashboard.

● Applied machine learning techniques for feature selection, classifier creation, and optimization, enhancing model performance.

Teacher

PrepNet Academy

02/2022 – current

Achievements/ Tasks

● Developed and executed integrated lessons aligned with state standards.

● Collaborated with teachers, administrators, and parents to support student learning objectives.

● Maintained regular communication with parents, students, and faculty for feedback and instructional strategy discussions. EDUCATION

● Data Science Post Graduate Program- Online Careerera 01/2022 - 07/2023

● Training Educators Certificate program - Online #T.E.A.C.H. 08/2022 - 08/2023

● Master’s Degree in Educational Technologies - CSU Ohio, USA 09/2015 - 07/2016

● Bachelor’s degree in Mathematics- Inonu Uni. Turkey 09/2006 - 06/2010

Languages

● English

● Turkish

TECHNICAL SKILLS

ROLES AND RESPONSIBILITY

● Conducted comprehensive data preprocessing, including processing, cleaning, and ensuring data integrity, to lay a solid foundation for analysis.

● Leveraged diverse data sources, both structured and unstructured, including databases, to empower informed decision-making processes.

● Engineered prediction systems and machine learning algorithms to derive actionable insights from complex datasets.

● Crafted interactive dashboards utilizing Tableau and Power BI, enabling intuitive data visualization for stakeholders.

● Proficiently utilized the R programming language to execute analytical tasks and enhance data-driven

solutions.

CAPSTONE PROJECT

Name: Heart Disease Detection

Role : Predict the disease

Libraries used : Pandas, Numpy, Seaborn, matplotlib Outcome : After evaluating three models (Random Forest, Decision Tree, and Logistic Regression), the Random Forest model exhibited superior performance.

Here are the accuracy results for each model:

Random Forest: Decision Tree: Logistic Regression:

•Train Accuracy: 0.982 Train Accuracy: 0.877 Train Accuracy: 0.778

•Test Accuracy: 0.930 Test Accuracy: 0.877 Test Accuracy: 0.982 Comparing the test accuracies, the Random Forest model achieves a test accuracy of 0.930, which is slightly higher than the Decision Tree's test accuracy of 0.877. The Logistic Regression model has the highest test accuracy of 0.982. Considering both training and test accuracies, as well as concerns about overfitting, the Random Forest model emerges as the optimal choice. With high accuracy across training and test data, it demonstrates strong generalization capabilities and potential for robust performance on unseen data.

Python Machine Learning

Power BI Tableau

Data Preparation MySQL

Data Visualization

Problem solving

R programing

Mathematics Statistics

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