In this tutorial we will predict home prices using linear regression. We use training data that has home areas in square feet and corresponding prices and train a linear regression model using sklearn linear regression class. Later on predict method is used on linear regression object to make actual forecast.

Exercise CSV file is here: https://github.com/codebasics/py/tree...

Code in this tutorial is here: https://github.com/codebasics/py/tree... (check the .ipynb file)

To download csv and code for all tutorials: go to https://github.com/codebasics/py, click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file.

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Topics that are covered in this Machine Learning Video:
0:00 Simple linear regression
1:59 Linear equation
2:22 Import data in dataframe
2:43 Import sklearn library
3:52 Plot scatter plot
5:26 Create Linear Regression object
13:35 Exercise at the end to predict canada's per capita income

Topic Highlights:
1) What is linear regression
2) Mean squared error
3) Predict home prices by minimizing mean squared error (or MSE)
4) Exercise at the end to predict canada's per capita income

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Code: https://github.com/codebasics/py/tree...
Correction: at 6:53, use reg.predict([[3300]]) instead of reg.predict(3300) as api specification has changed.
Exercise solution: https://github.com/codebasics/py/blob...

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