Before you can start training your machine learning model, you need to have a dataset to work with. This dataset will be used to train the model and to test the accuracy of the predictions that the model makes. There are a few different ways that you can get data for your machine learning model.
One way to get data is to collect it yourself. This can be done by web scraping, conducting surveys, or by gathering data from sensors. If you're collecting the data yourself, you'll need to make sure that you have a large enough dataset to train your model. A good rule of thumb is to have at least 10,000 data points.
Another way to get data is to purchase it from a company that specializes in collecting data. This data is usually much cleaner and easier to work with than data that you collect on your own. However, it can be expensive to purchase data in this way.
A third way to get data is to use publicly available datasets. These datasets are usually collected by government agencies or by large companies. Some examples of publicly available datasets are the census, weather data, and stock market data.
Once you have collected or obtained your dataset, you need to split it into two parts: a training set and a test set. The training set will be used to train your machine learning model and the test set will be used to test the accuracy of the predictions that your model makes.
