Supervised Learning ML

Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. 

The training dataset includes input data and response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset. A test dataset is often used to validate the model. Using larger training datasets often yield models with higher predictive power that can generalize well for new datasets.

Supervised learning includes two categories of algorithms:

Regression: 

• Continuous-response values
• Predict continuous valued output

Ex: House price prediction is an example of supervised learning algorithm
      We provide the dataset of the right price based on the square footage

Classification: for categorical response values, where the data can be separated into specific “classes”

Discrete Valued Output (0 or 1)

Based on other requirements it can be plotted as