insert code in the calculate_r_squared function in pykitzoid/algorithms/linear_regression.go. bear in mind the format in which the dataset will be received (csv) and change the parameters and return types as you require. refer https://medium.com/geekculture/linear-regression-from-scratch-in-python-without-scikit-learn-a06efe5dedb6

Insert the code in the calc_weights function in pykitzoid/algorithms/polynomial_regression.go. This algorithm should aim at minimising the sum of squared differences between the predicted values (based on the line) and the actual Y values in the dataset.

Link the repository's license in the README.md file.

Insert the code in the calculate_r_squared function in pykitzoid/algorithms/polynomial_regression.go. This algorithm should aim at indicating how well the independent variable (X) explains the variability in the dependent variable (Y).

insert code in the plot_regression_line function in pykitzoid/algorithms/linear_regression.go. use either a GO package to plot or research and implement the code yourself. refer here

Insert the code in the plot_data_points function in pykitzoid/algorithms/polynomial_regression.go. This algorithm should aim at creating a scatterplot for the data points for the regression line using the "gonum/plot" package.

add the feature report and bug report using the following folder structure:
pykitzoid/.github/ISSUE_TEMPLATE/feature_report.md and
pykitzoid/.github/ISSUE_TEMPLATE/bug_report.md

Insert the code in the main function in pykitzoid/algorithms/polynomial_regression.go.

Add a demo Python script that will call on a Go function, helloWorld from pykitzoid/demo/library.go. The script should be saved as follows: pykitzoid/demo/demo_app.py.

Refer here

Note that helloWorld from library.go does not take any parameters.

insert code in the plot_data_points function in pykitzoid/algorithms/linear_regression.go. bear in mind the format in which the dataset will be received (csv) and change the parameters and return types as you require. refer https://medium.com/geekculture/linear-regression-from-scratch-in-python-without-scikit-learn-a06efe5dedb6

Insert the code in the predict_y function in pykitzoid/algorithms/polynomial_regression.go. This algorithm should aim at predicting value of the dependent variable (Y) based on the given independent variable (X) and the polynomial regression model's coefficients.

add a sample Go program, pykitzoid/demo/library.go containing a function that can be called by a Python script to the project as follows:

package main

import (
   "C"
   "log"
)

//export helloWorld
func helloWorld(){
   log.Println("Hello World")
}

func main(){

}

Refer here for more information.

insert code in the mean function in pykitzoid/algorithms/linear_regression.go. bear in mind the format in which the dataset will be received (csv) and change the parameters and return types as you require. refer here

Add a file saved as pykitzoid/algorithms/polynomial regression/ polynomial_regression.go that has the template for writing a polynomial regression algorithm. Refer to PR #10 for an example of how the template should look like . Refer here and here for an idea on how the algorithm should like

insert code in the calculate_slope_and_intercept function in pykitzoid/algorithms/linear_regression.go. bear in mind the format in which the dataset will be received (csv) and change the parameters and return types as you require. refer here

Add the read_csv function in the pykitzoid/algorithms/polynomial Regression/ polynomial_regression.go. the file already contains necessary information needed to write the code. incase you have any doubts feel free to ask away

Add a Makefile to the repository, with commands that assist in building golang files into C shared object files, and to build source into a python package.

The following commands would be required:

• dist : Creates a distributable of the python package using pyproject.toml as described here

• binaries : Compiles all golang code into binaries and places them in thier respective directories

• all : Builds binaries and compiles code into a python package (equivalent of running make binaries and then make dist)

• clear : removes all build files (including binaries and distributable files)

Insert the code in the plot_regression_line function in pykitzoid/algorithms/polynomial_regression.go. This algorithm should aim at creating a line plot for the regression line using the "gonum/plot" package.

Feature Request

Integrate GitHub Action, where Golang CI Lint is triggered on every PR ensuring proper linting of .go files and goimports take place.

Reference

A reference can be taken from https://github.com/IEEE-VIT/termiboard/blob/master/.github/workflows/go-lint.yaml

TODO:
Use this website to create a basic Python package's directory structure. Leave the pyproject.toml file empty. Don't create the README.md or LICENSE as they have already been created.

Expected folder structure:

Insert the code in the mean function in pykitzoid/algorithms/polynomial_regression.go. This algorithm should aim at calculating the mean (average) of a slice of float64 values.