gopiashokan / IMDB-Movie-Analysis-with-PowerBI

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.

IMDB Movie Analysis With Power BI

Introduction

We have developed an interactive Power BI dashboard to analyze the factors influencing IMDB movie success. Conducted descriptive statistical analysis of genres, language, duration, director, and budget, revealing the impact on IMDB scores. Provided valuable insights to producers, directors, and investors for decision-making in the film industry.

Table of Contents

1. Key Technologies and Skills
2. Installation
3. Usage
4. Features
5. Contributing
6. License
7. Contact

Key Technologies and Skills

• Python
• Power BI
• Pandas
• Pillow

Installation

To run this project, you need to install the following packages:

pip install pandas
pip install Pillow

Usage

To use this project, follow these steps:

1. Clone the repository: git clone https://github.com/gopiashokan/IMDB-Movie-Analysis-with-PowerBI.git
2. Install the required packages: pip install -r requirements.txt
3. Run the Streamlit app: streamlit run app.py
4. Access the app in your browser at http://localhost:8501

Features

Data Preprocessing:

• Handling Missing Values: Addressing any gaps in the data to ensure a comprehensive and reliable dataset.

• Removing Duplicates: Eliminating redundant entries to maintain data integrity and accuracy.

• Converting Data Types: Ensuring appropriate data types for variables to facilitate smooth analysis.

• Feature Engineering: Exploring and creating new features to uncover potential correlations and enhance the depth of analysis.

Data Analytics

In this section, we dive into the realm of data analytics to gain valuable insights into the IMDB Movies Dataset. The initial general analysis covers various aspects such as total movies, budgets, gross earnings, total actors, likes, and reviews.

• Movie Genre Analysis: Explore the distribution of movie genres and their impact on IMDB scores. Determine the most common movie genres in the dataset. For each popular genre, calculate descriptive statistics (mean, median, mode, range, variance, standard deviation) of IMDB scores.

• Movie Duration Analysis: Analyze the distribution of movie durations and their impact on IMDB scores. Examine the distribution of movie durations. Identify the relationship between movie duration and IMDB scores. Calculate descriptive statistics (mean, median, mode, range, variance, standard deviation) of IMDB scores.

• Language Analysis: Examine the distribution of movies based on language. Determine the most common languages used in movies. Analyze the impact of language on IMDB scores using descriptive statistics.

• Director Analysis: Explore the influence of directors on movie ratings. Identify top directors based on their average IMDB scores. Analyze the contribution of directors to the success of movies. Calculate descriptive statistics (mean, median, mode, range, variance, standard deviation) of IMDB scores.

• Budget Analysis: Explore the relationship between movie budgets and financial success. Analyze the correlation between movie budgets and gross earnings. Identify movies with the highest profit margin.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.

License

This project is licensed under the MIT License. Please review the LICENSE file for more details.

Contact

📧 Email: [e-mail]

🌐 LinkedIn: linkedin.com/in/gopiashokan

For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.