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 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

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.

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

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

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

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 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 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 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.

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 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 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 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

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)

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.

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 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.

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

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