semvec/pypi-packages huggingface dataset:
easy-predictor 0.6.7
Perform regression/classification prediction on Excel dataset
Requires Python: >=3.6

![Screenshot_10](https://user-images.githubusercontent.com/64541739/174754962-952e3e72-0b2c-4ae6-987d-9f46c965e5c4.png)

# Easy Predictor

Perform Regression/Classifcation prediction on Excel dataset with ease.   

# Github Link

https://github.com/Tho100/easy_predictor

## Features

- Perform linear regression prediction by utilizing **linear** function
- Perform classification prediction by utilizing **classification** function
- Perform statistical calculation by utilizing **stats** function
- Visualize excel dataset by utilizing **tabulator** 

## Instruction

1. Linear Regression Prediction

```python
from easy_predictor import linear 

dataset = "path_to_excel_file"
model = linear.linear_regression()
model.predict(dataset,data_type: str,column_x: list[str],value: list[int],column_y: str)
```
2. Text Classification Prediction

```python
from easy_predictor import classification

dataset = "path_to_excel_file"
model = classification.classification()
model.predict(dataset,data_type: str,column_x: list[str],value: list[int],column_y: str)
```

3. Visualize Data

```python
from easy_predictor import table

dataset = "path_to_excel_file"
table.tabulator(dataset,dataset_type: 'xlsx','csv',columns=list[str])
```

# Statistical Calculation 

## Available Statistical Function

- Mean
- Median
- Mode
- Half

**Mean**

```python
from easy_predictor import stats

dataset = "path_to_excel"
model = stats.statistical()
model.mean(dataset,dtype: str,column=['Column1']) 
```

**Median**

```python
from easy_predictor import stats

dataset = "path_to_excel"
model = stats.statistical()
model.median(dataset,dtype: str,column=['Column1']) 
```

**Mode**

```python
from easy_predictor import stats

dataset = "path_to_excel"
model = stats.statistical()
model.mode(dataset,dtype: str,column=['Column1']) 
```

**Half**

```python
from easy_predictor import stats

dataset = "path_to_excel"
model = stats.statistical()
model.half(dataset,dtype: str,column=['Column1']) # Find half values of each rows from Column1
```

## Latest Update 0.1.0

```
CSV (Comma Seperated Values) File supported
```
## Latest Update 0.2.0

```
New 'table' Function To Visualize Data In Tabular Form
```

## Latest Update 0.3.0

```
ImportError: cannot import name 'table' from 'easy_predictor'. Bug
Fixed
```

## Latest Update 0.4.0

```
TypeError: object of type 'int' has no len(). Bug Fixed
```

## Latest Update 0.5.0

```
Bugs Fixed
```

## Latest Update 0.6.1

```
Multiple Column Input Supported
```

## Latest Update 0.6.2

```
Statistical Function
```

## Latest Update 0.6.3

```
Bugs Fixed
```

## Latest Update (0.6.6)

- **User Now Can Assign A Variable To Predicted Value**
- **Bug Fixed**

## Latest Update (0.6.7)

- **`got multiple values for argument 'data_type'` Bug fixed**
- **More Clean Looking Output**
sudokuku 0.4.0
A package for solving sudokus
Requires Python: >=3.9

# Sudokuku

[![Python Version](https://img.shields.io/pypi/pyversions/sudokuku?style=flat-square)](https://pypi.org/project/sudokuku)
[![License](https://img.shields.io/pypi/l/sudokuku?style=flat-square)](https://github.com/jonasstrube/sudokuku/blob/main/LICENSE)

[![PyPI Package Version](https://shields.io/pypi/v/sudokuku?style=flat-square)](https://pypi.org/project/sudokuku)
[![PyPI Downloads](https://img.shields.io/pypi/dm/sudokuku?style=flat-square)](https://pypi.org/project/sudokuku)
![GitHub Repository Size](https://shields.io/github/repo-size/jonasstrube/sudokuku?style=flat-square)
![GitHub Last Commit](https://img.shields.io/github/last-commit/jonasstrube/sudokuku?style=flat-square)
[![codecov](https://img.shields.io/codecov/c/github/jonasstrube/sudokuku?style=flat-square)](https://codecov.io/gh/jonasstrube/sudokuku)

A package for solving sudokus

## Contributing

Contributions are always welcome.

### Testing

You can run all tests via

    py -m pytest

You can also look up code coverage, for example using [pytest-cov](https://pypi.org/project/pytest-cov/).

    py -m pytest --cov sudokuku/

For testing your own changes, install sudokuku in development mode. The package installation will link directly to your local development files, not to the official files you downloaded from pypi.

    pip install -e ./
dftp 0.3
Command-line interface for Remember the Milk.

# Don't Forget the Python

Don't Forget the Python is a command-line interface for [Remember the Milk](https://www.rememberthemilk.com), an online task-management system.

Remember the Milk is very good at what it does - and is very easy to use in the browser and various apps - so Don't Forget the Python does not focus on duplicating its abilities, but instead adds a couple features that RTM doesn't currently provide, like exporting your tasks to pdf and printing your lists and tasks to the terminal. Currently, only a simplified version of tasks is supported (name and due or completed date).

## Installation

Use [pipx](https://pypi.org/project/pipx/): `pipx install dftp`

## Usage

From a terminal, run `dftp` and the list of available commands will be shown, including full help documentation.

## New in Version 0.2.0

You can now filter tasks by various due and/or completed dates - on, before, or after a date (and between two dates). Run `dftp tasks --help` in a terminal to see the options.

## Disclaimer

This CLI uses the Remember The Milk API but is not endorsed or certified by Remember The Milk.