Machine Learning Projects

AI and Data Analysis

Machine Learning & AI

Projects involving artificial intelligence, machine learning models, and data analysis.

Projects

NLP Project (Dr. Seuss + Shrek Stories)

April 2025

An exploration of creativity and coherence in AI-generated writing using vastly different literary sources:

  • Built to explore creativity and coherence in AI-generated writing using vastly different literary sources
  • Trained an RNN to generate coherent text based on a blend of Shrek scripts and Dr. Seuss stories
  • Focused on NLP model fine-tuning for narrative style and syntactic coherence

This project combined two very different writing styles to create a unique language model that could generate coherent text blending both narrative approaches.

Housing Prices Prediction

Kaggle Competition | March 2025

A machine learning project focused on improving real estate price estimation:

  • Tackled the problem of price estimation inconsistencies in regional real estate data
  • Built an XGBoost model to predict home prices; reduced RMSE from 200K to 80K
  • Discovered bedroom count as a key predictor through feature analysis

This project achieved significant improvement in prediction accuracy, reducing error by 60%.

Model Notebook | Executive Summary

Car Price Prediction Project

Kaggle Competition | February 2025

A neural network approach to used car marketplace valuation:

  • Created to simulate used car marketplace valuation using historical feature-rich datasets
  • Developed a neural network model with log-transformed targets to improve accuracy
  • Validated model improvements with lower prediction variance

Focused on improving prediction accuracy through advanced neural network techniques and data transformations.

Model Notebook

Bike Rental Demand Prediction

Kaggle Competition | January 2025

A business-focused forecasting model for bike rental inventory planning:

  • Targeted business needs to better forecast rental spikes for inventory and maintenance planning
  • Preprocessed and optimized data to train a high-accuracy neural network
  • Tuned hyperparameters and model architecture to improve demand forecasts

This project addressed real-world business needs for inventory and maintenance scheduling.

Model Notebook | Executive Summary

Bank Customer Retention ML Model

Kaggle Competition | January 2025

A customer churn prediction model to help banks retain customers:

  • Designed to help banks proactively identify customers at risk of leaving based on behavioral patterns
  • Created a random forest model to predict customer churn, boosting recall from 25% to 69%
  • Focused on customer segmentation and actionable ML-driven targeting

Achieved a significant improvement in recall, nearly tripling the model’s ability to identify at-risk customers.

EDA Notebook | Executive Summary

AI Chatbot for Truck Dispatching

Smart ETA Technologies - Developer Intern Project

A Python-based AI chatbot designed to improve truck dispatching efficiency and compliance:

  • Built with Ollama for AI model integration
  • Python implementation for natural language processing
  • Enhanced truck dispatching workflow efficiency
  • Improved compliance tracking through intelligent automation
  • Data Integration: Connected with Google Sheets using Python, Pandas, and Seaborn for analysis and visualization
  • Performance Optimization: Structured JSON and optimized query logic to enhance AI response accuracy and performance

This project was developed during my internship at Smart ETA Technologies, where I focused on creating practical AI solutions for logistics and transportation management.

ML Model for Phone Dispatchers

BYU-I Support Center - Software Engineer Project

A proof-of-concept machine learning model built to increase efficiency for 100+ phone dispatchers:

  • Python-based ML model
  • Designed to optimize dispatcher workflows
  • Part of my current role at BYU-I Support Center
  • Integrated into existing support center infrastructure

Technologies & Tools

  • Python: Primary language for ML/AI development
  • Ollama: For AI model integration and deployment
  • Pandas & NumPy: Data manipulation and analysis
  • Seaborn: Data visualization
  • Scikit-learn: Machine learning algorithms
  • XGBoost: Gradient boosting for structured data
  • Neural Networks: Deep learning models for complex predictions
  • RNN: Recurrent neural networks for NLP tasks
  • Random Forest: Ensemble methods for classification
  • Data Analysis: Working with structured and unstructured data for insights
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