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Regression in machine learning is a fundamental technique for predicting continuous outcomes based on input
features. It is used in many real-world applications like price prediction, stock market analysis, loan default
prediction, and risk assessment. Let us learn some real-world examples of regression models.
Example 1: Predicting Temperature

In the Meteorological department, the regression model predicts temperature by analysing past weather
data, preprocessing the data for model training, selecting relevant features, applying appropriate regression
algorithms, and systematically evaluating the model's performance to predict temperature on a given day.

Example 2: Predicting House Prices
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For house price prediction, the regression
model generates predictions based on $160,000
patterns found in the data, allowing 15
potential buyers, sellers, and other Price ($10,000)
stakeholders to make informed decisions 10
about property transactions. Using data
about features such as location of house, 5 $700,000
carpet area, type of house, property
circle rates, etc., the model can predict
the price of a house. Size of the house

Pop
Pop Quiz Quiz
Answer the following questions.

1. Which Supervised learning technique is used to predict the energy demand for a region based on
factors like time of day, weather, holidays, and past consumption patterns?
___________________________________________________________________________________________
2. Which Supervised learning technique is used to identify fraudulent insurance claims?
___________________________________________________________________________________________

AI Activity
AI Activity
Machine Learning
AI for Oceans
AI for Oceans is an application that helps us to learn about artificial intelligence, machine learning,
training data, etc. while addressing the global issue, i.e. Water Pollution. To use the application,
follow the given steps:
Step 1: Go to the Web Browser and type the following URL in the address bar: https://code.org/
oceans.
Step 2: Click the Try Now button. The following screen appears.

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