This project explores the Netflix Movies and TV Shows dataset using Python to uncover trends and insights about Netflix's content library. The analysis includes data cleaning, exploratory data analysis (EDA), and data visualization.
- Analyze the distribution of Movies and TV Shows.
- Explore content ratings.
- Identify trends in release years and content additions.
- Examine countries producing the most Netflix content.
- Visualize key findings using charts.
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
- VS Code
Dataset: Netflix Movies and TV Shows
The dataset contains information such as:
- Title
- Type (Movie/TV Show)
- Director
- Cast
- Country
- Date Added
- Release Year
- Rating
- Duration
- Description
- Data loading
- Data inspection
- Missing value analysis
- Movies vs TV Shows distribution
- Content ratings analysis
- Country-wise content distribution
- Release year trends
- Data visualization
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- Movies dominate Netflix's content library Out of 8,807 titles in the dataset, 6,131 (69.6%) are Movies, while 2,676 (30.4%) are TV Shows. This indicates that Netflix's catalog is predominantly movie-focused.
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- The United States contributes the most content The United States has the highest number of titles available on Netflix, followed by India and the United Kingdom. Together, these countries account for a significant portion of the platform's content library, highlighting their strong contribution to Netflix's global catalog.
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- 2018 recorded the highest number of titles added Netflix added 1,429 titles (16.2%) in 2018, the highest among all years in the dataset. This was followed by 2017 and 2019, each with approximately 1,277 titles (14.5%), reflecting Netflix's rapid expansion during this period.
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- Rajiv Chilaka is the most frequently appearing director Among all directors in the dataset, Rajiv Chilaka has the highest number of titles, with 19 titles. He is followed by Raúl Campos & Jan Suter (18 titles) and Suhas Kadav and Marcus Raboy (16 titles each).
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- TV-MA is the most common content rating The most common content rating is TV-MA, representing approximately 3,207 titles (36.4%). It is followed by TV-14 (2,160 titles, 24.5%) and TV-PG (863 titles, 9.8%), indicating that Netflix primarily offers content aimed at mature and teenage audiences.
- Clone the repository.
- Install the required libraries.
- Open the notebook.
- Run the notebook cells from top to bottom.
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization
- Python Programming
- Pandas
- NumPy
- Matplotlib