TMDB-5000-Movie

Introduction

This repository contains an analysis of the TMDB-5000-Movie dataset. The dataset includes information about movies, such as their titles, genres, budgets, revenues, and user ratings. The analysis aims to explore various aspects of the dataset and draw meaningful insights from it.

Analysis Scope

The analysis in this repository covers the following key areas:

1. Exploratory Data Analysis (EDA): A preliminary exploration of the dataset to understand its structure, missing values, data types, and general statistics.
2. Genre Analysis: Examination of movie genres to identify the most popular genres and their distribution in the dataset.
3. Financial Insights: Investigation of movie budgets, revenues, and profits to analyze their distribution and relationships.
4. Rating Patterns: Investigation of movie ratings to identify highly-rated movies and trends in user ratings.
5. Movie Recommendations: Building a simple recommendation system based on movie genres and user ratings.

Dependencies

To run the analysis code, you will need the following dependencies:

• Python 3
• Jupyter Notebook
• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn

You can install the required packages using the following command:

pip install numpy pandas matplotlib seaborn scikit-learn

Usage

1. Clone the repository: bash git clone https://github.com/homelander-79/TMDB-5000-Movie.git cd TMDB-5000-Movie

2. Run the Jupyter Notebook: bash jupyter notebook

3. Open TMDB-5000-Movie-Analysis.ipynb from the Jupyter Notebook interface.

4. Execute the code cells in the notebook to see the analysis results.

Conclusion

The TMDB-5000-Movie dataset provides valuable insights into the movie industry. Through this analysis, we aim to gain a better understanding of movie genres, financial aspects, and user ratings. Additionally, the created movie recommendation system can help users discover movies based on their preferences.

Contributing

We welcome contributions to enhance the analysis or extend it to cover more aspects of the dataset. If you find any issues or have suggestions, feel free to open an issue or submit a pull request.

Happy analyzing! 🎬🍿