Machine Learning Tutorial Python - 2: Linear Regression Single Variable

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/master/ML/1_linear_reg/Exercise

Code in this tutorial is here: https://github.com/codebasics/py/tree/master/ML/1_linear_reg (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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Next Video:
Machine Learning Tutorial Python - 3: Linear Regression Multiple Variables: https://www.youtube.com/watch?v=J_LnPL3Qg70&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=3

Very Simple Explanation Of Neural Network: https://www.youtube.com/watch?v=ER2It2mIagI

Code: https://github.com/codebasics/py/tree/master/ML/1_linear_reg
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/master/ML/1_linear_reg/1_linear_regression.ipynb

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