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: github.com/codebasics/py/tree/master/ML/1_linear_reg/Exercise
Code in this tutorial is here: github.com/codebasics/py/tree/master/ML/1_linear_reg (check the .ipynb file)
To download csv and code for all tutorials: go to 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: youtube.com/watch?v=J_LnPL3Qg70&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=3
Very Simple Explanation Of Neural Network: youtube.com/watch?v=ER2It2mIagI
Code: 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: github.com/codebasics/py/blob/master/ML/1_linear_reg/1_linear_regression.ipynb
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