Criteria for Selecting Dataset
The dataset choice significantly impacts the model’s ability to learn relevant patterns, generalize, and achieve high accuracy in various tasks.
Below is the listed steps-
- Step 1: Data quality and format play an important role for organizing well-structured data. It ensure the consistent features and clear label. For example- Suppose there is missing value in your datasets then always practice to alternative datasets.
- Step 2: Secondly, think on larger size of dataset which tend to more reliable results especially in ML tasks.
- Step 3: Consider the relevance of the dataset to your research question.
- Step 4: Explore the data selection method that determine the data point to contain the data in training phase. Thus, always be careful while choosing relevant data.
Top Datasets for data visualization
Data Visualization is a graphical structure representing the data to share its insight information. Whether you’re a data scientist, analyst, or enthusiast, working with high-quality datasets is essential for creating compelling visualizations that tell a story and provide valuable insights.
To help you get started on your visualization projects, we have compiled a list of top datasets that cover a wide range of topics, from classic datasets like the Iris flower measurements to comprehensive collections like COVID-19 case data. This article will explore Top Datasets for Visualization Projects and the criteria for Selecting them.