Explanation on how to model a linear regression by Hand and with Code for Python and R. Calculating Line Regression by Hand. When there are more than 2 points of data it is usually impossible to find a line that goes exactly through all the points.

The basic idea behind linear regression is quite simple. In mathematical terms we want to predict a dependent variable **Y** using an independent variable **X**. By assuming that the two variables correlate in a linear fashion we can predict **Y** with a simple linear formula:

Linear equation by Author

(The wavy equal sign signifies “approximately”). Simply put, as soon as we know a bit about the relationship between the two coefficients, i.e. we have approximated the two coefficients **α * and *β**, we can (with some confidence) predict Y. Alpha

With the help of linear regression, we can answer a lot of questions; e.g.

- “Is the rise of sea level connected to rising temperatures?”,
- “How expensive will a house with 3 bedrooms be?”
- “How many items do we sell if we increase our marketing budget by 20%?”

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In this article, I will take you through Linear Regression with PyTorch. I will simply use the PyTorch package to build a Linear Regression

PySpark in Machine Learning | Data Science | Machine Learning | Python. PySpark is the API of Python to support the framework of Apache Spark. Apache Spark is the component of Hadoop Ecosystem, which is now getting very popular with the big data frameworks.

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.