Applications of supervised machine learning include :
Email Spam Detection : Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
Healthcare Diagnosis : By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
Sentiment Analysis : This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
Fraud Detection : By training the model to identify suspicious patterns, we can detect instances of possible fraud.
