Dataset example sentences

"Dataset" Example Sentences

1. The dataset contained information on over 10,000 individuals.
2. We need a larger dataset to accurately analyze the results.
3. The dataset was formatted in a spreadsheet.
4. The accuracy of the results depended on the quality of the dataset.
5. A comprehensive dataset is necessary for future research.
6. The dataset was divided into categories for easier analysis.
7. Our team spent weeks compiling the dataset.
8. The dataset was missing crucial information that we had to manually input.
9. The dataset was collected over a period of three years.
10. The dataset was too large to upload to the server.
11. The dataset was organized by date and location.
12. We had to clean the dataset first to eliminate any errors.
13. The dataset was provided by a government agency.
14. The dataset was stored on an external hard drive.
15. The dataset was analyzed using statistical software.
16. The dataset was used to create a machine learning model.
17. The dataset was anonymized to protect privacy.
18. We had to filter the dataset to remove outliers.
19. The dataset was shared with other researchers on a secure platform.
20. The dataset supported our hypothesis.
21. The dataset was biased towards a certain demographic.
22. The dataset included both qualitative and quantitative data.
23. The reliability of the dataset was questioned by some experts.
24. The dataset contained historical data dating back to the 1800s.
25. We had to pay a fee to access the dataset.
26. The dataset was used to identify patterns and trends.
27. The dataset was incomplete, so we had to gather more data.
28. The dataset was too complex for manual analysis.
29. The dataset was generated by sensors placed throughout the city.
30. The dataset was used to predict future trends in the market.

Common Phases

1. Collecting dataset;
2. Cleaning and preprocessing dataset;
3. Exploring dataset;
4. Splitting dataset into train and test sets;
5. Training models with dataset;
6. Evaluating model performance with dataset;
7. Validating models with dataset;
8. Applying models to new dataset;
9. Updating dataset with new observations;
10. Sharing dataset with collaborators.