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Jeff Johannsen

Data Scientist • AI Engineer

Fort Collins, CO

Data Scientist focused on Sports Analytics, Local Tourism, AI Safety, and Supply Chain Optimization.

Python SQL AWS Deep Learning GenAI PyTorch Pandas Sklearn AutoML Flask Airflow Scrapy Data Visualization Git/Github Jupyter

Featured Project

NBA AI

NBA AI

Leverages core datasets and cutting-edge AI models to streamline and enhance the accuracy of NBA game predictions.
Data Management: Processes and stores play-by-play data from the NBA Stats API in an optimized SQLite database. Prediction Engines: Utilizes models like Ridge Regression, XGBoost, and MLP, with ongoing development of a custom deep learning and GenAI engine for superior accuracy. Web App: Delivers a user-friendly platform for accessing game predictions and scores, offering seamless interaction and real-time updates.

NBA Stats and Analytics Sports Betting Python SQLite AWS Web App Deep Learning PyTorch GenAI RAG LLM Transformers Pandas ETL API Hugging Face Sklearn XGBoost Seaborn Flask HTML/CSS/JS Bootstrap

Past Work

NBA Betting

NBA Betting

Using data analytics and machine learning to create a comprehensive and profitable system for predicting the outcomes of NBA games.
• Data acquisition architecture that leverages Scrapy, Airflow, and RDS Postgres to analyze and store data on NBA teams, players, and games from a diverse range of data sources. • Data modeling setup employing AutoML for quick iteration and Deep Learning Transformer models for optimized performance. • Public-facing web application and dashboard to showcase predictions and results.

NBA Stats and Analytics Sports Betting Python PostgreSQL AWS Dashboard Web App Statistical Analysis Web Scraping AutoML Pandas Jupyter Scrapy Sklearn Apache Airflow Seaborn PyCaret Flask Plotly Dash HTML/CSS/JS Bootstrap
Yelp Project

Predicting Yelp Review Quality

Utilizing the Yelp Open Dataset, this project predicts review quality to enhance user engagement and satisfaction. It leverages Apache Spark for ETL processing and AWS RDS for database hosting, while incorporating advanced feature engineering techniques and machine learning models. Through text analysis and sentiment analysis, it offers improved insights into user behavior and drives data-driven decision-making on Yelp.

Data Analysis Data Cleaning Feature Engineering Data Visualization NLP Machine Learning Random Forest Yelp Reviews Python Pandas Jupyter Matplotlib Sklearn Spacy XGBoost AWS EC2 and S3
Fraud Detection Project

Fraud Detection

In this project, I implemented machine learning and natural language processing techniques to predict fraudulent events from transaction data. The results were visualized through an intuitive Flask web application, deployed on AWS. This project highlights my ability to transform complex data into actionable insights.

Exploratory Data Analysis (EDA) ETL Data Visualization & Interpretation Natural Language Processing (NLP) Machine Learning Logistic Regression Random Forest Python PostgreSQL AWS EC2 & RDS Flask Web App Google Data Studio Dashboard
AP Staffing

AP Staffing - Optimizing Distribution Center Operations

Implemented data-driven analysis to develop an efficient employee scheduling system that effectively balances consistent and fluctuating work demands. Achieved substantial cost savings aligned with the company's evolving objectives, simultaneously enhancing customer satisfaction, minimizing employee stress levels, and mitigating associated quality concerns.

Data Analysis Data Visualization Supply Chain and Logistics Distribution Center Excel Powerpoint