A complete Exploratory Data Analysis (EDA) project on the Netflix dataset using Python, Pandas, NumPy, Matplotlib, and Seaborn. This project demonstrates an end-to-end data analysis workflow, covering data cleaning, preprocessing, exploratory data analysis, business problem solving, visualization, and dashboard creation.
- 📊 Dashboard
- 📌 Project Overview
- 🛠️ Technologies Used
- 📂 Dataset Information
- 🧹 Data Cleaning
- 📈 Exploratory Data Analysis
- 📊 Visualizations
- 📷 Sample Visualizations
- 💡 Key Insights
- 📁 Project Structure
- 🚀 Future Improvements
- 👨💻 Author
Netflix Dataset Dashboard
This project analyzes Netflix's Movies and TV Shows catalog to discover trends, patterns, and business insights.
The project demonstrates the complete data analytics workflow:
- Importing libraries and loading the dataset
- Data exploration
- Data cleaning
- Handling missing values
- Removing duplicate records
- Data type conversion
- Feature engineering
- Exploratory Data Analysis (EDA)
- Solving 30 business problem statements
- Data visualization
- Dashboard creation
- Business recommendations
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
The dataset contains information about Netflix Movies and TV Shows, including:
- Show ID
- Category (Movie / TV Show)
- Title
- Director
- Cast
- Country
- Release Date
- Rating
- Duration
- Genre (Type)
- Description
Netflix Movies and TV Shows dataset obtained from Kaggle for educational and portfolio purposes.
The following preprocessing steps were performed:
- Checked dataset dimensions
- Examined data types
- Identified missing values
- Filled missing values where appropriate
- Removed duplicate records
- Converted
Release_Dateinto datetime format - Separated Movies and TV Shows into different DataFrames
- Converted movie duration and TV show seasons into numeric values
The notebook includes:
- Dataset structure
- Data types
- Missing value analysis
- Duplicate analysis
- Movies by Director
- Movies by Country
- Movies by Rating
- Long-duration Movies
- Multi-season TV Shows
The project solves 30 real-world business questions, including:
- Total Movies vs TV Shows
- Percentage distribution of content
- Top 10 Countries
- Top Directors
- Most Common Ratings
- Release Year Analysis
- Longest & Shortest Movies
- Average Movie Duration
- Most Frequent Actors
- Rating Distribution
- Country-wise Analysis
- Monthly Release Trends
- Content Type Distribution
- Director Analysis
- Family-Friendly Content Trend
- Mature Content Analysis
- Text Analysis
- Business Insights
- KPI Dashboard
The project includes multiple visualizations such as:
- Bar Charts
- Horizontal Bar Charts
- Line Charts
- Histogram
- Pie Chart
- Multi-chart KPI Dashboard
- Movies constitute the majority of Netflix's content library.
- The United States is the leading contributor of Netflix titles.
- TV-MA is the most frequently assigned content rating.
- Netflix experienced rapid content growth after 2015.
- Most movies have durations between 90 and 120 minutes.
- Family-friendly content has steadily increased over the years.
- Several directors have contributed to both Movies and TV Shows.
- Missing values are mainly concentrated in Director, Cast, and Country columns.
netflix_data_analysis/
│
├── graphs/
│ ├── netflix_dashboard.png
│ ├── content_rating_by_category.png
│ ├── family_friendly_content_trend.png
│ └── movie_vs_tv_shows.png
│
├── Netflix Dataset.csv
├── netflix_eda_dashboard.ipynb
├── .gitignore
└── README.md
- Build an interactive dashboard using Plotly
- Develop a Streamlit web application
- Perform Genre-wise analysis
- Create Country-wise heatmaps
- Analyze actor collaborations
- Add sentiment analysis using movie descriptions
Asish Amitansu Rout
Data Analyst Intern | MCA Student | Python & Data Analytics Enthusiast
- GitHub: https://github.com/Enthuasish
- LinkedIn: https://www.linkedin.com/in/asish-amitansu-rout
If you found this project helpful or learned something from it, consider giving it a ⭐ on GitHub. Your support is greatly appreciated!