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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.
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