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https://github.com/stefagnone/movies-dataset-analysis-project

Comprehensive analysis of the Movies dataset, exploring genre trends, comparisons, and qualitative insights using Python, Pandas, and visualizations. Designed to uncover actionable findings for stakeholders.
https://github.com/stefagnone/movies-dataset-analysis-project

data-analysis data-visualization exploratory-data-analysis matplotlib movies-analysis pandas python seaborn storytelling-with-data

Last synced: 17 days ago
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Comprehensive analysis of the Movies dataset, exploring genre trends, comparisons, and qualitative insights using Python, Pandas, and visualizations. Designed to uncover actionable findings for stakeholders.

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README 

        
## Project Overview

This project involves analyzing the movies dataset to uncover key insights into genre-specific trends, characteristics, and qualitative aspects. By leveraging Python data analysis techniques, this project explores how a selected movie genre compares to others in the dataset, its trends over decades, and its qualitative features. The analysis is designed to provide actionable insights for stakeholders with minimal technical background, emphasizing clarity and business relevance.



### Key Highlights:

- **Genre Comparison**: Analyzed runtimes, ratings, and other metrics to assess how the selected genre compares to others.

- **Decade-by-Decade Trends**: Identified temporal trends to understand the genre's evolution over time.

- **Qualitative Insights**: Provided a deep dive into the genre's distinctive characteristics using specific movies as examples.

- **Data-Driven Insights**: Delivered findings supported by visualizations and cited credible sources.



## Technologies Used

- **Python**: Core language for data analysis

- **Pandas, NumPy**: Data manipulation and processing

- **Matplotlib, Seaborn**: Data visualization

- **Jupyter Notebook**: Environment for interactive analysis



## Repository Structure

- `Data/`: Contains the movies dataset file (`movies.xlsx`) used for analysis.

- `Code/`: Jupyter Notebook (`Stefano_A2.ipynb`) and an HTML file (`Stefano_A2.html`) with the complete analysis and visualizations.

- `Output/`: Final analysis outputs and visualizations extracted from the Notebook.

- `README.md`: Project overview and instructions.



## Key Insights

- **Comparative Analysis**: The selected genre had longer-than-average runtimes and slightly better ratings compared to others, suggesting its appeal lies in its storytelling depth.

- **Temporal Trends**: The genre experienced a significant rise in popularity in the 1990s, with consistent audience interest into the 21st century.

- **Qualitative Features**: Themes of resilience and human connection were prevalent in the selected genre, with iconic movies reinforcing these aspects.



## Instructions

1. Clone this repository.

2. Open the Jupyter Notebook (`Stefano_A2.ipynb`) in Jupyter or any compatible environment.

3. Ensure required libraries (like `pandas`, `matplotlib`, `seaborn`) are installed.

4. Run the notebook to explore data analysis and visualizations.

5. Review the HTML file for a rendered summary of the analysis.



## Contact

Feel free to reach out for any queries or feedback:

**Stefano Compagnone**  

[[email protected]](mailto:[email protected]) | +1 617-251-3853