18CSE301J INFORMATION VISUALIZATION

Arjun Dev Singla - RA2011031010074

ABOUT THE DATASET

The Netflix dataset is a collection of user ratings for movies and TV shows that were made available to the public as part of a Netflix Prize competition in 2006. The dataset contains over 100 million ratings from over 480,000 users on over 17,000 movies and TV shows.

There are several reasons why someone might choose to work with the Netflix dataset:

Large dataset: The Netflix dataset is a large and complex dataset, making it a challenging and interesting dataset to work with. It provides an opportunity for data scientists to test their skills and explore new techniques for working with big data.

Real-world data: The Netflix dataset is a real-world dataset that was generated by actual users, providing a more accurate representation of user preferences and behaviors compared to artificially generated datasets.

Wide range of applications: The Netflix dataset can be used for a wide range of applications, such as recommendation systems, predictive modeling, and data visualization. This makes it a versatile dataset that can be used by researchers and data scientists from different fields.

Impactful research: The Netflix dataset has already been used in several research projects that have had a significant impact on the field of machine learning and data science. Working with this dataset provides an opportunity to contribute to ongoing research efforts and potentially make a meaningful impact.
YOU CAN FIND THE LINK OF THE DATASET HERE
ASSIGNMENTS

  1. Tableau

    Tableau is a powerful data visualization tool that allows users to easily analyze and share data in a meaningful way. It enables the creation of interactive dashboards and reports, and provides a wide range of visualization options such as charts, graphs, and maps. Tableau makes it easy to explore data and gain insights that can drive business decisions and help solve complex problems. Tableau

  2. Gephi

    Gephi is an open-source software tool designed for network analysis and visualization. It is built using Java on the NetBeans platform, and provides a range of features for analyzing and visualizing complex networks. With Gephi, users can explore and understand networks through interactive visualizations, and can apply a range of algorithms and metrics to gain insights into the structure and behavior of networks. Its open-source nature also enables customization and extensibility by developers Gephi

  3. Matplotlib and Seaborn

    Python provides powerful tools for data visualization, including the popular libraries Matplotlib and Seaborn. Matplotlib is a low-level library that allows for the creation of custom visualizations, while Seaborn provides a high-level interface for creating more sophisticated plots. Together, they allow for the creation of a wide range of visualizations such as line charts, scatter plots, and heat maps, making Python an ideal language for data exploration and analysis. Python

  4. D3.js

    D3 (Data-Driven Documents) is a JavaScript library for data visualization that provides a range of tools for creating dynamic, interactive visualizations for the web. D3 enables the creation of a wide range of visualizations, including line charts, scatter plots, and heat maps, and provides a range of customization options for creating bespoke designs. Its emphasis on data-driven design makes it an essential tool for creating data visualizations that communicate insights effectively. Tableau