A data science project utilizing machine learning to predict movie release years and genres based on directors' previous works.
Developed an interactive Power BI dashboard to analyze the factors influencing IMDB movie success. Statistical analysis of genres, language, duration, director, and budget, revealing impact on IMDB scores. Provided valuable insights to producers, directors, and investors for decision-making in the film industry.
Understanding how movie genres can affect your return on investment
An Analysis of IMDb.com's database with the goal of understanding the market and elaborating a strategy to enter this industry.
Movie Insights: Exploring film industry trends and analytics through data analysis techniques.
In this project, I analyzed the correlation between media production in different countries and their GDP. The work involved extensive data preprocessing, including cleaning and transforming variables to ensure data quality. The final analysis aimed to uncover how a country's film production relates to its economic performance.
This repository includes my IMDb Web Scraping-Flatiron School Module 1 Project. In this project, I incorporated data scraping, data wrangling, data cleaning, and visualization techniques.
Investigate the film industry to gain sufficient understanding of what attributes to success and in turn utilize this analysis to create actionable recommendations for companies to enter the industry.
Add a description, image, and links to the film-industry topic page so that developers can more easily learn about it.
To associate your repository with the film-industry topic, visit your repo's landing page and select "manage topics."