Comprehensive Movie Data Analysis and Visualization

Project Overview

This project involves analyzing a movie dataset to explore relationships between budget, gross earnings, and ratings. The analysis includes correlation studies, visualizations, and identification of key trends and top-performing companies.

Features

• Data Exploration: Initial inspection, handling missing values, and outlier detection.
• Correlation Analysis: Calculation and visualization of correlations using Pearson, Kendall, and Spearman methods.
• Data Visualization: Scatter plots, box plots, and heatmaps to illustrate data trends.
• Revenue Analysis: Identification of top-performing companies based on gross revenue.
• Tools Used: Python libraries including Pandas, Seaborn, and Matplotlib.

Getting Started

Prerequisites

• Python 3.x
• Pandas
• NumPy
• Seaborn
• Matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/comprehensive-movie-data-analysis.git

2. Navigate to the project directory:

   cd comprehensive-movie-data-analysis

3. Install the required libraries:

   pip install pandas numpy seaborn matplotlib

Usage

1. Place your dataset in the project directory. The dataset should be named movies.csv.

2. Run the analysis script:

   python analysis.py

   This script performs data exploration, correlation analysis, and generates visualizations.

Project Structure

• analysis.py: Python script containing data analysis and visualization code.
• movies.csv: Dataset containing movie information (budget, gross earnings, ratings, etc.).
• README.md: This file.

Contributing

Feel free to open issues or submit pull requests if you have suggestions or improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.