Welcome to the Netflix Analysis repository! In this project, I have conducted a detailed analysis of Netflix using a dataset downloaded from Kaggle. The analysis includes various insights and visualizations created using Pandas for data manipulation and Plotly for interactive visualizations.
Netflix has revolutionized the entertainment industry, and this project aims to gain insights into its content using data analysis techniques. By leveraging Python libraries like Pandas and Plotly, we explore the dataset to uncover trends and patterns within Netflix's offerings.
The dataset used for this analysis is sourced from Kaggle, containing information about Netflix shows and movies. It provides valuable attributes such as titles, directors, cast, release years, and more.
To explore the Netflix Analysis project, follow these steps:
- Clone this repository:
git clone https://github.com/weedu34/Netflix_Analysis - Install the required dependencies:
pip install -r requirements.txt - Download the dataset from Kaggle using the Kaggle API.
- Load and preprocess the dataset using Pandas in a Jupyter Notebook environment. The link to the file is Notebook
- Run the analysis scripts to generate insights and visualizations.
- Explore the generated visualizations in the Jupyter Notebook or saved image files.
The analysis covers a wide range of aspects related to Netflix's content, including:
- Distribution of content by release year.
- Director and cast analysis.
- Content duration and release frequency.
- Content released country-wise
The heart of this project lies in its visualizations. Using the Plotly library, we've created interactive bar graphs, pie charts, and other visual representations that make it easy to grasp the insights gained from the dataset.
This project is licensed under the MIT License.
Feel free to contact me at weedu34@gmail.com if you have any questions or suggestions. I hope this analysis sheds light on interesting trends within Netflix's content and provides valuable insights to anyone interested in the world of streaming entertainment.