Used
Tools:PythonAPIExcelTableau
Project
Type:E2EData GatheringData CleaningExploratory Data AnalysisData Visualization
Relevant Link
https://github.com/r-uiz/Letterboxd-Data-Analysis-Project
Type
Personal
Back to Homepage
Detailed Reports
Project Overview: README.md
Data Gathering: consolidate_data.ipynb
Analysis: EDA.working_data.ipynb
Tableau Dashboard:
Letterboxd All-Time Stats
public.tableau.com
Letterboxd Data Analysis Project
Project Objectives | Business Questions
- What are some trends in my movie-watching habits?
- Specifically rewatch patterns, genres, cast & crew, and release/watch dates.
- Have I been generally watching movies that I end up rating highly?
- Based on highly-rated attributes, what movies should I watch next?
Summary of Insights
‣
Viewing Habits and Trends:
- There was a huge spike in movie-watching during 2023 because of a more conscious effort to watch more films. The most-watched months are January and April. With release dates, it's clear that I watched more movies released in the last two decades.
Rewatch Strategy:
- My ratings generally increase on rewatch, particularly for films initially rated above 3 stars, while some like “Train to Busan” rated lower. This insight is complemented by the finding that movies with initially high ratings are more likely to be rewatched and often maintain or improve in ratings.
High-Rating Patterns:
- High ratings are often given to genres like Music, War, History, Mystery, Family, and Drama; directors like Christopher Nolan, Richard Linklater, Richard Curtis, Luca Guadagnino, and Denis Villeneuve have at least two watched-moves that were highly rated; actors like Michael Stuhlbarg, Ethan Hawke, Jake Gyllenhaal, Dave Bautista, Linda Cardellini, Kyle Bornheimer, Haruka Abe, Tom Stourton, Julie Delpy, Angela Bassett, and Domhnall Gleeson are highly rated.
Recommendations and Next Steps
- Continue the momentum of watching more films and make sure to make a conscious decision of it. Also, consider exploring older movies to appreciate the evolution of cinema.
- Implement a strategy to rewatch films I rated lower than 3 stars to see if my perceptions change over time, providing deeper insights into my rating patterns and preferences. Maybe even surfacing new favorites that I initially overlooked.
- Dive into movies with these specific highly-rated attributes to see if my interest aligns with these high ratings. If not, I could normalize my data to provide a deeper understanding of my preferences.
‣
General Information
To gather and enrich the data, I exported my data from Letterboxd then used Python to request additional data from the TMDb API. The data was then cleaned, processed, and explored in Python (some cleaning were made in Excel as well), before being visualized in Tableau.
Check out the Letterboxd All-Time Stats - Tableau Workbook.
For more about my projects and data journey, visit my Portfolio.
Detailed Reports
Project Overview: README.md
Data Gathering: consolidate_data.ipynb
Analysis: EDA.working_data.ipynb
Tableau Dashboard:
Letterboxd All-Time Stats
public.tableau.com