Categories of Machine Learning Different Categories of Machine Learning Techniques for Different Types of Problems The development of algorithms and models that can evaluate and learn from data to produce predictions, judgments, and recommendations is the exciting topic of machine learning. Several industries, including banking, healthcare, and computer vision, use machine learning techniques. Machine learning approaches fall into various categories, each of which is best suited for a certain issue type. The basic categories of machine learning techniques, such as classification, regression, cluster analysis, and association analysis, will be covered in this article. Classification Predicting the category of the incoming data is the objective of classification. For instance, forecasting whether it will be bright, cloudy, windy, or wet. Sensor data describing the temperature, relative humidity, atmospheric pressure, wind speed, wind direction, etc. would be the input data in this scenario. As we covered, the numerous weather classifications, such as sunny, windy, rainy, and cloudy, would be the aim or what you're trying to anticipate. Classifications refer to the categories that need to be predicted. Another example of classification is to classify a tumor as benign or malignant. In this case, the classification is referred to as binary classification since there are only two categories, but you can have many categories as well, as in the weather prediction problem shown here. Another example of classification is to identify hundreds and digits as being one of 10 categories, zero to nine. In all these examples, the algorithm needs to learn from data to predict the class of new input data. Regression When your model has to predict a numeric value instead of a category, then the task becomes a regression problem. An example of regression is to predict the price of a stock. The stock price is a numeric value, not a category, so this is a regression task. It is not a classification task. If you were to predict whether the stock price will rise or fall, then that would be a classification problem, but if you're predicting the actual price of the stock, then that's a regression problem. That is the main difference between classification and regression. In conclusion, classification predicts a category, while regression predicts a numerical value. Along with the stock example given here, regression can also be used to forecast how much rain will fall in a given area. Recall that we talked about forecasting whether it would rain the following day when we discussed classification. That is making a prediction between the two possibilities of rain or no rain. So, that is a classification issue. Regression would be an issue if we were attempting to estimate the amount of rain, which is a numerical variable. Cluster Analysis In cluster analysis, the goal is to organize similar items in your dataset into groups. A very common application of cluster analysis is referred to as customer segmentation. This means that you're separating your customer base into different groups or segments based on customer types. For example, it would be beneficial to segment your customers into seniors, adults, and teenagers. These groups have likely different likes and dislikes and have different purchasing behaviors. When companies segment customers into different groups like this, they may be able to provide targeted marketing ads for each group's particular interests. Note that cluster analysis is also referred to as clustering in different contexts. Clustering has many applications, such as image segmentation, document clustering, and bioinformatics. Association Analysis Association analysis comes next. Here, creating a set of guidelines to record associations between objects or occurrences is the aim. When things or events happen simultaneously, the rules are utilized to determine when they do. Market basket analysis is a popular way to apply association analysis to comprehend consumer purchase patterns. For instance, association analysis can show that banks clients who have checking or deposit accounts also frequently show interest in other types of investment vehicles, including money market accounts.