Data visualization project for Netflix's movies and TV shows would involve analyzing and presenting data related to the content offered by Netflix.
This could include information on the most popular movies and TV shows, the genres that are most in demand, the countries with the highest viewership, and more. The data could be visualized using various techniques, such as bar charts, line graphs, scatter plots, heat maps, and others, to help better understand the data and draw insights.
Here's a brief overview of the libraries i used in this project:
- NumPy (Numerical Python): It is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
- Pandas: It is an open-source data analysis and data manipulation library. It provides data structures for efficiently storing large datasets and tools for working with them. It is widely used for cleaning, transforming, and analyzing data.
- Matplotlib.pyplot: It is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides functions for making publication-quality visualizations such as line plots, scatter plots, histograms, bar charts, error bars, box plots, etc.
- Seaborn: It is a data visualization library based on Matplotlib. It provides high-level interfaces for creating beautiful and informative statistical graphics. It also provides functions for making specialized plots such as violin plots, box plots, and bar plots with theoretical distributions.
- scikit-learn (sklearn): It is a machine learning library for the Python programming language. It provides a range of supervised and unsupervised learning algorithms in a consistent interface. It is used for tasks such as classification, regression, clustering, dimensionality reduction, model selection, etc.