This project performs Exploratory Data Analysis (EDA) on the Netflix Movies & TV Shows dataset using Python. The analysis focuses on understanding content distribution, release trends, genres, ratings, and geographic presence through data cleaning, visualization, and business insight generation.
- Analyzed 8,807 Netflix titles.
- Performed data cleaning and preprocessing using Pandas.
- Conducted Exploratory Data Analysis (EDA).
- Created professional visualizations using Matplotlib.
- Identified key trends in content distribution and release patterns.
- Generated business insights to support data-driven decision-making.
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
This project uses the Netflix Movies & TV Shows dataset from Kaggle.
📊 Dataset: Netflix Movies & TV Shows Dataset
The notebook contains the complete workflow, including:
- Data Loading
- Data Understanding
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization
- Business Insights
- Recommendations
📘 Notebook: View Jupyter Notebook
Note: If GitHub is unable to render the notebook due to its size, click "Download raw file" to open it locally in Jupyter Notebook or JupyterLab.
The report documents the complete methodology, analysis, visualizations, findings, recommendations, and conclusions.
📑 Project Report: Netflix Movies & TV Shows Analysis Report (PDF)
Note: Download the PowerPoint presentation to view all slides with full formatting and charts.
The analysis includes visualizations such as:
- Movies vs TV Shows
- Top 10 Countries
- Content Ratings Distribution
- Content Added Over Time
- Release Year Trend
- Top Genres
- Top Directors
- Movie Duration Distribution
- Top Release Years
- Content Type Distribution
- Movies represent the majority of Netflix's content library.
- The United States contributes the highest number of titles.
- TV-MA is the most common content rating.
- Netflix experienced significant content growth after 2015.
- International Movies and Dramas are among the most popular genres.
- Most movies have a duration between 80–120 minutes.
python-data-analysis/
│
├── README.md
├── Netflix_Movies_and_TV_Shows_Analysis.ipynb
├── Netflix_Movies_and_TV_Shows_Analysis_Report.pptx
└── Netflix Movies and TV Shows.csv
- Perform advanced statistical analysis.
- Build an interactive dashboard using Plotly or Dash.
- Compare Netflix content trends across multiple years.
- Apply machine learning for content recommendation and prediction.
Lalitha Devi Seri
https://www.linkedin.com/in/lalitha-devi-seri/
🐙 GitHub
https://github.com/serilalithadevi
If you found this project helpful, consider giving it a ⭐ on GitHub.