semvec/pypi-packages huggingface dataset:
algo-lib 0.0.5
A small sample package with two search algorithms
Requires Python: >=3.6

# Algo-lib (the Algorithm Library)

This is a simple example package. It contains two searching algorithms. The purpose of this repo is to practice packaging a library for distribution.

For package distribution information and release history, see: https://pypi.org/project/algo-lib/ 

### Quick Start

1. Install algo-lib from the command line using pip.
```
python3 -m pip install --upgrade algo-lib
```

#### Example Usage

1. Binary Search
```python
from algo_lib import search

lst = [1, 50, 99, 150, 40000]
targetValue = 99

#binary search returns the index of a target value if present in a sorted list
targetIndex = search.binary(lst, 0, len(lst) - 1, targetValue) #targetIndex is 2
```

2. Linear Search
```python
from algo_lib import search

lst = [5, 1, 2, 100, 41, -1]
targetValue = -1

#linear search returns the index of a target value if present in list
targetIndex = search.binary(lst, targetValue) #targetIndex is 5
```

3. Merge Sort
```python
from algo_lib import sort

lst = [5, 1, 2, 100, 41, -1]

#merge sort takes a list argument, and sorts it in either increasing or decreasing order
targetIndex = sort.merge(lst) #lst is [-1, 1, 2, 5, 41, 100]
```
theGradient 0.0.1
Linear Regression

Automated Regression
 ====================
 ## A simple [linear regression](https://en.wikipedia.org/wiki/Linear_regression#:~:text=In%20statistics%2C%20linear%20regression%20is,is%20called%20simple%20linear%20regression. "regression") model that lets you select a `Google Sheet` from the drive and calculates all the optimised parameters of the model using `Gradient_Descent_Algorithm` and lets you save all the parameters and evaluation metrics of the model to your `Google Drive.`

 1. ### It not just calculates the optimised parameters of your model but lets you graphically visualise the relationship between various other parameters.

 2. ### It lets you save files to your drive after your proper authentication.
```
You Come up  with a Dataset and walk out with the most optimised parameters of the model...Everything else(Data Preprocessing and Data Modelling) is taken care of.
```

### It's kind of a drag and drop model.

## How to install?
--------------------
```python
pip install theGradient
```

## How to use:
----------------
1. ### import the module
2. ### call the main function using the module

```python
import theGradient
theGradient.main()
```

## Prerequisties required for the module to run successfully:
------------------------------------------------------------
#### Before you call the module make sure you have installed all the following modules
```python
pip install pydrive
pip install gspread
pip install pandas
pip install sklearn
pip install matplotlib
```
## Usage and functionality:
-------------------------------
### This module can be used with any of the dataset but make sure to have the target variable at the last column of the spreadsheet.



## References:
------------------
1. [Creating a python library](https://python-packaging.readthedocs.io/en/latest/index.html "creating a library")
2. [Hosting the library on PyPI](https://medium.com/little-big-engineering/lets-talk-about-python-packaging-6d84b81f1bb5 "hosting the library")
3. [working with google sheets](https://www.youtube.com/watch?v=cnPlKLEGR7E&t=10s&ab_channel=TechWithTim "googleSheets")
4. [working with markdown Language](https://www.youtube.com/watch?v=bpdvNwvEeSE&ab_channel=HiteshChoudhary "markdownLanguage")

## License:
---------------------
This Package is distributed under the [MIT license](https://opensource.org/licenses/MIT)