Movies and Machine Learning
In this project, I explored the fascinating intersection of machine learning and film. I built machine learning models and trained them on a collection of movies to assess how likely I am to re-watch some of my favorite films.
Utilizing pairwise comparison and feature engineering, I created preference data and features encompassing themes such as: Genre, Runtime, Budget, and More!
I trained algorithms using this data, including Random Forest and XGBoost to enhance the accuracy of predictions. I then applied the best model to additional movies to generate new predictions and assess their re-watch potential.
Check out the project’s GitHub Repository to see the Python script driving this analysis and the accompanying visuals linked below to learn more about my movie preferences!
Dive Into My Projects
Classifying Country Music
I discovered the heart of Country music through applying natural language processing to explore what lies beneath the twang.
Investigating Inflation
I researched the 10 Year Breakeven Inflation Rate and Oil Price changes, combining time series techniques to deepen understanding of fiscal policy.
Diamonds In Our Hands
I explored how LLMs can be used to analyze user reviews and evaluate investor sentiment related to brokerage apps.
Manhole Bingo
I designed a Tableau dashboard that maps the location of artistic manhole covers across Seattle, bringing more attention to this public art.
Encore
In this dedicated section, I reflected on underling themes present throughout some of my favorite Country songs using data analytics and visualizations.
Riffs
If you’re interested in learning more about how I think, then please explore my blog where I have written stories about music and my life.
