This project analyzes the Netflix Movies and TV Shows Dataset (from Kaggle) to explore trends in content type, release year, genre, and country distribution.
The goal is to understand how Netflix’s content library has evolved over time.
- Pandas – for data cleaning and manipulation
- NumPy – for numerical operations
- Matplotlib – for static visualizations
- Seaborn – for advanced and beautiful charts
- Data Understanding – explored dataset structure, shape, and column info.
- Data Cleaning – handled missing values and inconsistent data.
- Exploratory Data Analysis (EDA) – examined patterns in release years, genres, and countries.
- Visualization – plotted charts for better understanding.
- Insights & Conclusion – summarized key findings.
- Movies make up the majority of Netflix’s content.
- The United States and India contribute the highest number of titles.
- Most Netflix releases occurred between 2017–2019, showing rapid content expansion.
- Drama and Comedy are among the most popular genres.
🔹 : The Netflix library has more Movies than TV Shows. The pie chart shows that roughly 60–70% of the content is Movies and 30–40% are TV Shows. 🔹 : United States and India have the highest number of titles. The top 5 countries hold a major share of the total content. 🔹 : Drama and Comedy are the most popular genres. The genre-wise content is unevenly distributed, providing useful insights for strategic decisions. 🔹 : Between 2017 and 2019, the rate of content addition was high. In recent years, Netflix has continued to add new titles consistently.
Recommendations: 🔹 : Focus on popular genres and marketing efforts in high-content countries. 🔹 : Explore new content opportunities in countries with lower content representation.
Kaggle - Netflix Movies and TV Shows Dataset
Python, Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, EDA, Data Visualization