https://github.com/codewithmayank-py/box-office-analysis-with-seaborn-and-python

This repository contains Python code and datasets for analyzing box office data. Explore trends, patterns, and factors influencing movie performance.
https://github.com/codewithmayank-py/box-office-analysis-with-seaborn-and-python

analysis box-office-data-analysis data-analysis data-visualization dataset jupyter-notebook matplotlib pandas python3 seaborn

Last synced: about 2 months ago
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This repository contains Python code and datasets for analyzing box office data. Explore trends, patterns, and factors influencing movie performance.

• Host: GitHub
• URL: https://github.com/codewithmayank-py/box-office-analysis-with-seaborn-and-python
• Owner: CodeWithMayank-Py
• Created: 2024-04-19T16:51:42.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-19T17:12:35.000Z (9 months ago)
• Last Synced: 2024-04-20T18:21:13.151Z (9 months ago)
• Topics: analysis, box-office-data-analysis, data-analysis, data-visualization, dataset, jupyter-notebook, matplotlib, pandas, python3, seaborn
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/c/tmdb-box-office-prediction/data
• Size: 1.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Box Office Analysis Project

## Overview

This repository contains the Jupyter Notebook file for analyzing box office data using Python. The analysis aims to uncover trends and patterns in movie performance based on box office statistics.

## Dataset

- **Box Office Data**: The dataset includes information about movie titles, release years, budgets, box office gross earnings, genres, ratings, and runtime.

## Analysis

The Jupyter Notebook file (`Box-Office-Analysis.ipynb`) provides detailed analysis and visualization of the box office dataset. Some of the key aspects covered in the analysis include:

- Data cleaning and preprocessing of box office data.

- Exploratory Data Analysis (EDA) to identify patterns and insights.

- Statistical analysis to predict box office success factors.

## How to Use

To replicate or explore the analysis:

1. Clone this repository to your local machine.

2. Ensure you have Jupyter Notebook installed.

3. Open `Box-Office-Analysis.ipynb` using Jupyter Notebook.

4. Follow the step-by-step instructions in the notebook to run the analysis.

## Dependencies

- Python 3.9

- Jupyter Notebook

- Pandas

- Matplotlib

- Seaborn

- Scikit-learn

## Dataset

The box office dataset used in this analysis is sourced from Kaggle. You can download the dataset from [here](https://www.kaggle.com/c/tmdb-box-office-prediction/data).

## Contributions

Contributions to improve the analysis or add new features are welcome! Feel free to fork this repository and submit pull requests.