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film-industry

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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.

  • Updated Nov 3, 2024
  • Jupyter Notebook

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.

  • Updated Jan 3, 2025
  • Jupyter Notebook

This project explores a comprehensive movie dataset to uncover trends and insights related to runtime, genre popularity, IMDb ratings, director performance, and box office earnings. Using Python (Pandas, Matplotlib, Seaborn), we visualize patterns across thousands of movies, highlighting key factors influencing commercial and critical success.

  • Updated Oct 30, 2025
  • Jupyter Notebook

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.

  • Updated Apr 26, 2020
  • Jupyter Notebook

A comprehensive Power BI dashboard providing analytical insights into movie industry data including box office performance, ratings, genres, director/actor metrics, and trends. Analyzes budget vs revenue, release timing impact, and audience preferences.

  • Updated Jan 19, 2026

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