semvec/pypi-packages huggingface dataset:
dsci-prediction 6
a package for predicting if cancer is treatable or not
License: MIT
Requires Python: >=3.9,<4.0

# prediction

a package for predicting if cancer is treatable or not

## Installation

```bash
$ pip install dsci-prediction
```

## Usage

`prediction` can be used to load, clean, build a test model and plot results
as follows:



```python
from dsci_prediction.dsci_prediction import *

file_path = "data.txt"  # path to your file
load = load_data(input_path, output_path)
clean = clean_data(input_path, output_path_train, output_path_test)
model = build_test_model(train_df, test_df, cross_val_output, tuned_para_output, classification_output, confusion_matrix_output)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. 
Please note that this project is released with a Code of Conduct. 
By contributing to this project, you agree to abide by its terms.

## License

`prediction` was created by Clichy Bazenga. It is licensed under the terms
of the MIT license.

## Credits

`prediction` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
grouponefunctions 0.1.8
A package containing the necessary functions to smoothly run the analysis in DSCI-310-Group-1
License: MIT
Requires Python: >=3.9,<3.11

# grouponefunctions

A package containing the necessary functions to smoothly run the analysis in DSCI-310-Group-1.

## Installation

Note: The installation is not likely to proceed for Python version lower than 3.9.10. Please check your Python version the code below.

```bash
python --version
```

Then you can install the package by the command below.

```bash
pip install grouponefunctions
```

## Usage

`grouponefunctions` is primarily used for analysis of [Predicting students’ grades using multi-variable regression](https://github.com/DSCI-310/DSCI-310-Group-1), but contains three general use functions. To implement the functions, `import` our package `grouponefunctions` as a module. The package has three functions: `splitxy`, `listfun`, and `plotsquaredata`. 

### `split_xy`

 `split_xy` can be used to split data into predictor feature and target variables as follows:

```python
from grouponefunctions.grouponefunctions import split_xy

X_train, y_train = split_xy(train_df, ["feature1", "feature2", "feature3"], target)
X_test, y_test = split_xy(test_df, ["feature1", "feature2", "feature3"], target) 
```

### `plot_square_data`

This function is capable at creating plots to display in a grid configuration.

```python
from grouponefunctions.grouponefunction import plot_square_data

plot_square_data(X_train, y_train, ["feature1", "feature2", "feature3"], ["title1", "title2", "title3], "This is Plot 1")
plot_square_data(dependent, independednts, ["income"], ["retirement_age"], "Income and Retirement Age Relation")
```

### `list_abs`

This function is the abstract function for list, used in Results part in the analysis. 

```python
from grouponefunctions.grouponefunction import list_abs

list_abs(preprocessor, "pipeline-2", "onehotencoder", categorical_features)
```



## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`grouponefunctions` was created by Andres Zepeda Perez, Daniel Hou, Zizhen Guo, Timothy Zhou. It is licensed under the terms of the MIT license.

## Credits

`grouponefunctions` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
tbnu 0.1.4
TBNU is a super simple and easy to use CLI notes application
License: MIT
Requires Python: >=3.6

TBNU 
==
Terminal Based Notes Utility!
-----

TBNU is a super easy to use notes program. Takes no time to set up and has simple use.

## Features
- Write down whatever very quickly
- Thats literally it

## How to Use
The normal interface can be used as such:
```bash
$ tbnu    
-- TBNU notes --

TBNU -- Help Message
Commands:
    (All commands can be shortened to their first letter)
    help                 show this message
    new [note content]   create a new note
    view NOTE NUMBER     view note at the provided number
    exit / q             quit the program
            
>> new note
Note created
>> view 1 
Note #1
------
note
>> exit
Thank you for using TBNU
$ 
```

You can also use it directly from the command line:
```bash
$ tbnu -n "first note"
-- TBNU notes --
Note created
$ tbnu -v
-- TBNU notes --
Note #1
------
first note
$ tbnu -n "2nd note"
-- TBNU notes --
Note created
$ tbnu -v 2
-- TBNU notes --
Note #2
------
2nd note
$ 
```

To remove all note use:
```bash
$ tbnu --delete-notes
-- TBNU notes --
Are you sure you want to delete all notes from this system (y/n)
>> y
Notes have been deleted. Use 'pip uninstall tbnu' to remove the rest of the program
```
dgx 0.0.24
Dealergeek API
License: MIT
Keywords: DEALERGEEK,API

# dgx

Generate Dealergeek API

## Installation

Use pip to install the package:

```bash
pip install dgx
```

## Usage

This package allows to generate an API structure, with some basic libraries.

### New project

Create a new project

```bash
dgx -i my-app
```

### Add module

This package has some default modules that you can add.

- brand
- personnel

```bash
dgx -a brand
```


### New module

If you want create a module, can you write this command.

```bash
dgx -c my-module
```

### Delete project

You must be in the root folder of the project, my-app-project


```bash
dgx -d my-app
```
group10pack 0.1.9
This package contains the functions for the Statistics Canada investment income analysis,by group 10
License: MIT
Requires Python: >=3.9,<3.10

# DSCI-310-Group-10-Package

DSCI-310-Group-10-Package is a Python package for the investment income outcome analysis done for DSCI310. This package is largely dependent on the scikit-learn and matplotlib packages, and is distributed under the MIT License.

The functions in this package have roles related to hyperparamater optimizations for specific machine learning models, and plot the results obtained.

The main project where this package is used can be found in the following GitHub Repository: https://github.com/DSCI-310/DSCI-310-Group-10

## Installation

```bash
$ pip install group10pack
```

## Usage

Click on the following link for a short tutorial on how to use the functions in this package. 
- [example](docs/example.ipynb)


## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`DSCI-310-Group-10-Package` was created by Harry Zhang Ahmed Rizk Mahdi Heydar Nikko Dumrique. It is licensed under the terms of the MIT license.

## Credits

`DSCI-310-Group-10-Package` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).

### License
This project is licensed under the MIT License and [Creative Commons Attribution-NonCommerical-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/)
<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:1" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" /></a><br />