Jonathan Eudja’s Portfolio

Welcome to my GitHub portfolio! I’m Jonathan Eudja, a college student studying computer science with a track in data science at DeSales University. Here you’ll find a collection of my projects, showcasing my skills in machine learning and data analysis.

About Me

I am currently pursuing a degree in Computer Science with a track in Data Science at DeSales University . My interests include Sports analytics, and applying statistical methods to solve real-world problems.

Projects

Machine Learning Projects

• Predicting NBA Salaries: Created a machine learning model to predict NBA player salaries based on various performance metrics and other features.
  • Notebook Link
  • Technologies: Python, scikit-learn, pandas, Numpy
  • Key Concepts: Regression, Feature Engineering, Classification, SVM

Data Science Projects

• MLB Umpire Incorrect Call Analysis: Investigated the effect of an umpire making an incorrect call on a Major League Baseball game.
  • Notebook Link
  • Technologies: Python, pandas, matplotlib
  • Key Concepts: Data Analysis, Statistical Testing
• Mental Health in College Students: Analyzed mental health data to understand the challenges faced by college students and propose interventions.
  • Notebook Link
  • Technologies: SQL, Python, pandas
  • Key Concepts: Data Analysis, Mental Health Research
• Netflix Data Science Project:This project aims to analyze the Netflix dataset to explore various machine learning questions, including predicting IMDb scores, classifying age certifications, and evaluating differences in IMDb scores between movies and TV shows.
  • Notebook Link
  • In Depth
  • Technologies: Python, pandas, matplotlib, scikit-learn
  • Key Concepts: Data Analysis, Statistical Testing, Machine Learning, Random Forest, Decision Trees
• Leagues Data Science Project: This project aims to analyze player data from the top five European football leagues to explore machine learning approaches for predicting a player’s market value. The project covers data cleaning, feature engineering, exploratory data analysis, and predictive modeling using ensemble learning methods.
  • Notebook Link
  • In-Depth
  • Technologies: Python, pandas, matplotlib, seaborn, scikit-learn
  • Key Concepts: Data Analysis, Feature Engineering, Market Value Prediction, Random Forest, Gradient Boosting, Model Evaluation

• Data Analysis Projects

• NBA Player Salaries Analysis: Analyzed NBA player salaries from the 1980s to the 2010s to determine the impact of conference on player preferences.
  • Microsoft Word Link
  • Technologies: Excel, Minitab
  • Key Concepts: ANOVA, Statistical Analysis