Whether you’re a researcher, a student, and or an enterprise, the only way to make a machine learning or natural language processing project a success you’ll need a large dataset.

The dataset will need to be large enough to create a sample size that will give accurate results. While machine learning is used to identify current trends, we often wrongly assume that current behavior is inherently similar to past behavior. This is not always the case.

Before you start collecting data for your machine learning project, you’ll need to define your goal and find a sample dataset that is both large enough and good enough to develop a strong model for data analysis.

Once the model is developed, you’ll need to decide how to measure the data.

For example, say you want to develop a machine learning model that can predict stock movements. It can be quite difficult to develop an accurate machine learning model for stocks without a large dataset that has known outcomes. If you were able to test the model against historical data with stocks that you knew in advance would either skyrocket or fall, you could test your model and make a prediction with greater accuracy according to certain events.

Many leading organizations from around the world use Webz.io’s historical archived datasets to build AI models for financial analysis.

Webz.io has a range of free datasets that include blog posts, online message boards, and forums, news articles from different languages and categories, as well as negative and positive reviews of hotels, companies, and movies.

Whether you’re a Fintech company looking to gather historical data for predictive analytics and risk modeling, a researcher seeking training data for NLP, sentiment analysis or AI machine learning, Webz.io’s free datasets can deliver insights and identify trends in a range of different industries. Webz.io also offers the ability to create your own customized dataset using our historical database of over 100TB and multiple sources.

Organizations from all over the world are using our datasets to conduct leading market research for competitive intelligence, data-driven marketing and digital trends data. For example, one of the content analyses included health news studies that were carried out to determine the prevalence of nurses’ opinions in health news stories.

Our data has also been used to develop classification models built on reverse plagiarism and natural language processing for fake news detection. The models have so far been successfully identifying fake news more accurately than humans would otherwise.