Machine Learning First Model

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Linear Regression

Linear Regression is a machine learning algorithm based on supervised learning. Regression is a method of modelling a target value based on independent predictors. This method is mostly used for forecasting and finding out cause and effect relationship between variables.Regression techniques mostly differ based on the number of independent variables and the type of relationship between the independent and dependent variables. imple linear regression is a type of regression analysis where the number of independent variables is one and there is a linear relationship between the independent(x) and dependent(y) variable.

To follow on, you need python and your awesome self. Using pip we would install the following dependencies

• Numpy
• Pandas
• Matplotlib
We are going to be using a dataset containing head size and brain weight of different people. This dataset is available in this repo.

Hypothesis function for Linear Regression :

hθ(x) = θ0 + θ1x1 + θ2x2 + θ3x3 +…..+ θnxn

..............still continued so wait for some time

Contribution

• Fork and clone the repo.
• To avoid merge conflicts, make sure to set upstream in your git.

  git remote add upstream  https://github.com/sonal-spd/Blog-API.git
  

• Whenever you want to pull changes from main repo, run:

  git pull upstream main
  

• Create your feature branch

  git checkout -b <feature-name>
  

• Commit your changes

  git commit -am "Meaningful commit message"
  

• Push to the branch

  git push origin <feature-name>