Portfolio
Joseph Wankelman's
Driving Growth Through Data and Technical Excellence
With a unique blend of data analytics, software engineering, and business strategy, I bring a results-driven approach to solving complex challenges. My portfolio showcases a wide range of capabilities, from building comprehensive software solutions in Java to conducting economic analysis and leveraging data insights to drive strategic decision-making. Whether it’s through AI-driven market analysis or optimizing business operations, I deliver high-impact solutions that maximize growth and efficiency for organizations looking to stay competitive in today's rapidly evolving landscape.
Enhancing Environmental Policy Through Neural Networks and Data Science
Python | Keras | TensorFlow | Neural Networks | Autoencoders | Dimensionality Reduction | Data Preprocessing | Binary Cross-Entropy Loss | Denoising | TensorBoard | Matplotlib | 3D Visualization | Economic Analysis | Data Compression | Hyperparameter Tuning | Business Strategy | Economic Efficiency
This project explores the intersection of environmental policy and economic impact through neural networks and data analytics. By applying machine learning techniques, including natural language processing and sentiment analysis, the study identifies key linguistic patterns in major environmental documents that influence public discourse and market behavior. The project provides actionable insights for policymakers and investors by revealing how language in environmental policies drives economic decisions in renewable energy and sustainable development sectors.
Enhancing Environmental Policy Through Neural Networks and Data Science
Python | TensorFlow | Keras | Deep Learning | Autoencoders | Data Preprocessing | Dimensionality Reduction | Model Optimization | Business Analytics | Machine Learning | Data Visualization (Matplotlib)
This project utilizes neural networks to build an autoencoder model for dimensionality reduction and image denoising on the MNIST dataset, achieving efficient data compression and noise mitigation. By applying TensorFlow and Keras, the model was optimized through hyperparameter tuning and statistical analysis, showcasing the effectiveness of non-linear dimensionality reduction in high-dimensional data for real-world applications such as image processing and data compression.
Comprehensive Mapping of Washington DC Parks and Social Dynamics using ArcGIS
GIS | ArcGIS Pro | ArcGIS Online | ArcGIS Experience Builder | Data Visualization | 3D Mapping | Spatial Analysis | Geospatial Data Integration | Business Analytics | Economic Impact Analysis | Urban Planning | Public Policy Interpretation | Data Preprocessing | Statistical Analysis | Environmental Impact Modeling | Socio-economic Impact Modeling
This project integrates advanced GIS technologies with business and economic analysis to map Washington DC’s parks, tree canopy, and socio-economic factors such as poverty, education, and crime. It demonstrates the intersection of urban planning, public policy, and environmental management, highlighting how green spaces contribute to economic resilience, community health, and social equity.
Economic Insights from Reddit Discussions: A Text-to-Data Sentiment Analysis on Financial Strain
R | Sentiment Analysis | Text Mining | Data Preprocessing | Machine Learning | K-means Clustering | API Integration | Quanteda | ggplot2 | Data Visualization | Economic Forecasting | Risk Management | Parallel Processing | NLP | Business Analytics
This project employed advanced data science and sentiment analysis techniques to analyze discussions of financial strain on Reddit, providing insights into public sentiment surrounding economic hardships in America. By utilizing APIs, text mining, and clustering algorithms like K-means, the project identified key patterns in user sentiment related to wage stagnation, credit issues, and loan defaults, offering valuable data for economic forecasting and policy decisions.
Economic Inequality in the United States: A Data-Driven Exploration
R | Sentiment Analysis | Text Mining | Data Preprocessing | Machine Learning | K-means Clustering | API Integration | Quanteda | ggplot2 | Data Visualization | Economic Forecasting | Risk Management | Parallel Processing | NLP | Business Analytics
This project analyzes wealth and income distribution in the U.S. using public datasets and APIs from sources such as the Census Bureau and Federal Reserve, applying advanced data analytics and economic modeling to highlight trends in inequality. Through geospatial analysis and interactive visualizations, it showcases the effects of policies like Quantitative Easing (QE) on wealth disparity, offering valuable insights for strategic decision-making and economic forecasting.
Time Series Forecasting for Retail Sales Optimization Using Walmart Sales Data
Python | SQL | Machine Learning | Time Series Forecasting | Data Preprocessing | Feature Engineering | NeuralProphet | Greykite | Economic Analysis | Pandas | NumPy | Matplotlib | Seaborn | Data Visualization | Regression Analysis | Statistical Modeling | Business Analytics | Inventory and Demand Forecasting
This project developed a comprehensive time series forecasting model to predict Walmart's future sales using advanced machine learning techniques and historical data. By incorporating external factors like fuel prices, weather, and holidays, the analysis provided actionable insights for optimizing inventory management, pricing strategies, and operations, helping Walmart increase profitability and improve customer satisfaction during peak demand periods.
Predictive Analytics in NBA Performance Using Piecewise and Spline Regression Models
Python | Data Manipulation | Data Visualization (pandas, numpy, matplotlib, seaborn) | Statistical Modeling (Linear, Polynomial, Piecewise, Spline Regression) | Data Imputation (Simple, Iterative, KNN, LGBM Imputation) | Experiment Tracking (ML Foundry) | Model Evaluation (RMSE, MAE, MSE, R², ANOVA) | Machine Learning (Scikit-learn for regression, model fitting, and evaluation) | Data Visualization (seaborn, matplotlib) | Model Fitting | Statistical Significance Testing
This project developed advanced regression models, including Piecewise and Spline Regression, to predict NBA team performance based on fitness and training metrics such as weightlifting, yoga sessions, and water intake. By analyzing non-linear relationships between these factors and points scored, the project identified the most effective models for accurate predictions, using methods such as hypothesis testing, data visualization, and model evaluation to optimize performance.
Predicting Residential Home Prices: An Advanced Data Science Approach
Python | SQL | Scikit-learn | Machine Learning | Regression | Feature Engineering | Data Preprocessing | Statistical Analysis | ANOVA | Data Visualization | Seaborn | Matplotlib | Economic Sensitivity Analysis | Real Estate Market Forecasting | Business Analytics | Decision Trees | Random Forest | Linear Models | Time Series Analysis
This project utilizes data science and machine learning to develop predictive models for housing prices in Ames, Iowa, using 79 explanatory variables describing home characteristics. By applying advanced regression techniques and feature engineering, the analysis offers valuable insights into the relationship between key factors such as square footage, neighborhood, and overall quality, guiding data-driven decisions for real estate stakeholders.
Predicting NBA Team Performance Using Polynomial Regression: A Data-Driven Economic Approach With Python
Python | Scikit-learn | Machine Learning | Polynomial Regression | Linear Regression | Feature Engineering | Data Preprocessing | Outlier Detection | Data Imputation | Statistical Analysis | R-squared | RMSE | AIC | Predictive Modeling | Seaborn | Matplotlib | Data Visualization | Business Analytics | Economic Modeling | Risk Management | ANOVA | Strategic Decision-Making | Sports Performance Forecasting
This project leverages advanced data science and machine learning techniques, specifically polynomial regression, to predict NBA team performance based on physical attributes and training metrics. By modeling non-linear relationships between variables such as weightlifting sessions, water intake, and yoga sessions, the project provides actionable economic insights into player productivity and team efficiency, optimizing resource allocation and strategic decision-making for sports management.
Optimizing Pricing Strategy Based on Demand Elasticity for Café Menu Items
Python | Data Manipulation (pandas, numpy) | Data Visualization (matplotlib, seaborn) | Statistical Modeling (Linear, Polynomial, Spline Regression) | Machine Learning (Scikit-learn) | Price Elasticity Modeling | Profit Maximization | Experiment Tracking (ML Foundry) | Model Evaluation (RMSE, MAE, MSE, R²) | Data Imputation | Business Strategy | Economic Forecasting | Pricing Strategy Optimization
This project optimized the pricing strategy for café items by analyzing demand elasticity to determine ideal pricing points that maximize profitability. Using Ordinary Least Squares (OLS) regression and predictive modeling techniques, the analysis revealed how price changes impact sales for various items, guiding data-driven decisions that balance profit maximization with consumer price sensitivity.
Comprehensive Data Analysis and Response Time Optimization for City Service Requests
Python | Pandas | NumPy | Matplotlib | Plotly | Data Wrangling | Exploratory Data Analysis (EDA) | Geospatial Analysis | Time-Series Analysis | Economic Interpretation | Data Visualization | Machine Learning | Risk Managements
In this project, I analyzed over 364,000 service request records from New York City's 311 system to evaluate complaint types, geographic distribution, and optimize response times using data science and statistical methodologies. By applying Ordinary Least Squares (OLS) regression, geospatial analysis, and time-series forecasting, I provided actionable insights for resource allocation, leveraging economic principles to enhance service efficiency and cost-effectiveness.
Web-Based Music Synthesizer
HTML | CSS | JavaScript | Bootstrap Framework | OOP | Tone.js | Web Components | Speech Synthesis API | JavaScript Event Handling | Data Manipulation and Visualizations | Version Control (Git)
This project demonstrates my ability to develop a web-based music synthesizer using HTML, CSS, and JavaScript, integrating complex sound synthesis algorithms and an interactive user interface to enhance the auditory experience. By combining technical expertise with strategic business principles, I optimized the interface for user engagement while ensuring efficient performance, delivering a product that balances artistic value with market demand.
Escape Artist: Java-Based Text Adventure Game
Java | GUI (Swing) | OOP | Event Handling | Testing (JUnit) | Audio Integration | JSON Parsing | Risk Mitigation | Software Optimization | Multithreading
In the "Escape Artist" project, I utilized Java, GUI design, and Object-Oriented Programming (OOP) principles to create a dynamic, text-based adventure game featuring resource gathering, NPC battles, and puzzle-solving. The game emphasizes modularity, scalability, and economic efficiency through optimized code reusability and resource management, while ensuring an engaging user experience with a Java Swing-based graphical interface and integrated sound effects.
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Strategic Decision-Making with Java: A Battleship Game Development
Java | OOP | Algorithms | Data Structures | Randomization | BufferedReader & PrintStream Handling | Exception Handling | Data Analytics
In this project, I developed an interactive Battleship game using Object-Oriented Programming (OOP) principles in Java, incorporating dynamic player interactions, randomized AI behavior, and ship placement algorithms. By simulating decision-making processes and risk management strategies, the game mirrors real-world economic models of resource allocation and competitive advantage, offering insights into both game theory and business analytics.