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CALVIN KREUSSER

CALVIN KREUSSER

Data Scientist | Full-Stack Software Engineer

Technology / Internet

Denver, Denver

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About CALVIN KREUSSER:

Experienced Data Scientist and serverless full-stack engineer with a 2.5-year track record at Lockheed Martin, specializing in AWS utilization. Adept in front and back-end development, excelling as both a team member and lead. Enthusiastic lifelong learner, known for tackling challenging tasks with a positive attitude. Holds a Master of Science – Data Science degree from the University of Denver.

Experience

BINOMIAL DISTRIBUTION ANALYSIS

  • Conducted Binomial Distribution Analysis on Wisconsin Breast Cancer (Diagnostic) Dataset, focusing on key features.
  • Enhanced efficiency through Principal Component Analysis (PCA) dimensionality reduction.
  • Developed six classifiers; Logistic Regression excelled with 0.982 accuracy and strong malignant case recall.

BIKE SHARING DATA ANALYSIS

  • Explored bike-sharing dataset, unveiling peak hours, seasonal trends, and weather impact.
  • Modeled regressions (Decision Tree, Random Forest, Bagging) with R-squared surpassing 0.999.
  • Optimized Random Forest through techniques like Randomized Search CV for robust demand prediction.

INCOME PREDICTION AND INSIGHTS THROUGH CLUSTERING AND CLASSIFICATION

  • Predicted income using Logistic Regression, Decision Trees, Naïve Bayes, and Random Forests.
  • Applied PCA for dimensionality reduction and K-means clustering (up to 99.99% training accuracy).
  • Assessed using clusters for income insights, highlighting the value of tailored classification models.

Education

UNIVERSITY OF DENVER, SCHOOL OF ENGINEERING AND COMPUTER SCIENCE, Denver, CO                   

Master of Science – Data Science                                                                                              Graduated 2023

 

  • 3.94 GPA.
  • Calculus I, II, & III, Discrete Math, Linear Algebra, Python I & II, Probability & Statistics I & II, Algorithms, Data Visualization, Data Mining, Database Organization & Management, Parallel & Distributed Computing, Data Science Tools I & II, Machine Learning, Data Science Capstone.

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