I am a passionate and results-driven data scientist with a knack for extracting insights from complex datasets. Curious explorer of data landscapes, avid problem solver, and lifelong learner, I’m deeply fascinated by the art of discovering hidden patterns and trends within seemingly chaotic information. The years spent mastering Excel design and automation provided a solid foundation in data manipulation and formula creation. This experience seamlessly led to data science, advancing my skills through an intensive, project-based career accelerator program.

Now proficient in data visualization, statistical analysis, and machine learning algorithms. My approach transforms intricate datasets into actionable insights, enabling the delivery of clear predictive models, trend analyses, and business strategies. This expertise allows me to meet the evolving analytical needs of diverse businesses.

My love for data began in Excel, for my own personal projects, it later develop into an invaluable tool for my business. After several years of using Excel in my personal and busness life, I decided to offered my services on freelancing sites like UpWork and Fiverr exclusively earning 5-star reviews. I honed my skills through the experience I gained tackling diverse projects. I continue to offer my expertise optimizing data management, analysis, and reporting to help businesses make informed decisions and drive efficiency.

• Codeup Data Science Nov 2023
  • Fully-immersive, project-based 20-week in-person career accelerator that provided me with 670+ hours of expert instruction in applied data science. Developed expertise across the full pipeline (planning, acquisition, preparation, exploration, modeling, storytelling), and comfortable working with real-world, messy data to deliver actionable insights to diverse stakeholders.

• New York Health Inspection Prediction

  • Combined New York City's health inspection data with customer reviews from Google Maps to predict the outcomes of restaurant inspections. The team developed a tool to automatically collect and analyze customer feedback, aligning it with the corresponding health inspection periods. This approach included sentiment analysis of the reviews, providing a more nuanced view of each restaurant's performance. As a result, the project created a predictive model that offered a comprehensive understanding of health inspection outcomes, integrating both quantitative data and qualitative customer experiences.

• Zillow Property Value Predictions

  • A predictive model was created using Zillow's dataset to provide accurate property valuations in the dynamic real estate market. The model employed advanced machine learning techniques, such as SelectKbest and Recursive Feature Elimination (RFE), to identify the most influential features for predicting property values. Utilizing the LassoLars model, the project achieved a significant improvement over the baseline, reducing the Root Mean Square Error (RMSE) from $540,000 to $412,000, marking a 25% enhancement in prediction accuracy. This development represents a substantial stride in the field of real estate analytics, offering a more precise tool for property valuation.

• Wine Quality Prediction

  • Collaboratively developed a predictive model for wine quality using Data-World's dataset, focusing on chemical property analysis. Leveraged Python and Random Forest algorithms to create a model achieving 32% improved accuracy from baseline. Conducted statistical tests and generated visualizations to identify key quality determinants, improving understanding of wine assessment criteria. Played a pivotal role in data preprocessing and model optimization, showcasing strong analytical and programming skills.

• LinkedIn: linkedin.com/in/hirejohngarcia/
• Email: [email protected]
• Website/Blog: HireJohnGarcia.com

Feel free to reach out to me if you have any questions, collaboration opportunities, or would like to connect.