In order to complete a data analytics or machine learning project successfully, there are a few key steps that must be followed. First, it is important to understand the problem that you are trying to solve and have a clear goal in mind. Once you know what you are trying to achieve, you can then begin to gather the data that will be used for your analysis. It is important to have high-quality data that is relevant to your problem in order for your machine learning algorithm to produce accurate results.
After you have collected your data, it is time to start cleaning and preprocessing it. This step is crucial in ensuring that your data is ready for use in training your machine learning model. During preprocessing, you will also want to split your data into training and test sets so that you can assess the performance of your model on unseen data.
Once your data has been preprocessed and split into training and test sets, it is time to train your machine learning model. This step will involve using the training set of data to fit a model that can then be used to make predictions on the test set ofdata. After testing your model on the unseendata, it is important to evaluate its performance in order to understand how well it general