semvec/pypi-packages huggingface dataset:
Arambh-ML 0.0.4
An Auto ML framework that solves Classification Tasks
Requires Python: >=3.6
Keywords: auto ml python,classification problem,machine learning

# Auto ML for Solving Classification Problems for Tabular Data

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)   


## Functionality of the Auto ML Framework

- Detects the type of problem from the given target feature
- Does EDA on it's own
- Finds out if there are null values present or not
- If null values are present they are treated specifically with the class they are present in
- Forms train-test split on it's own
- Applies 5 ML models to show the use which model performs the best

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.
 ```
  pip install Arambh_ML
  ```
## Example

 
# test.py
from ArambhML import get_model_info

## Make sure all the folders are there 
dataframe_dir = 'path/of/dataframe_dir'
Run get_model_info(dataframe dir)





## Screenshots
<img src="https://drive.google.com/file/d/1p6S11R81ROiWduWs4dqBZZNtVZuft7y7/view?usp=sharing">

## Note 
- I have tried to implement all the functionality, it might have some bugs also. Ignore that or please try to solve that bug.
pdffit 0.1
Fit a Lognormal + Power law distribution to data

# PDF_Fit
A code to fit the LN+PL or LN+2PL form to the density PDF of star forming regions.
To use this:

```python
pip install pdffit
````

Once you have installed, you can use the following sample script - 
```python
from fitter import *
import numpy as np


sample_data = np.load('./sample_data.npz')

#xdata and ydata are just two arrays. 
xdata = sample_data['arr_0']
ydata = sample_data['arr_1']
sink = float(sample_data['arr_2'])

params = Params(s_cut_off = sink)

p0 = [1.7, 1.6, 0.8, 7.1]
PLPLresult = PLPLFit(xdata, ydata, p0, params, use_K21=True)

p0 = [1.85, 1.57]
PLresult = PLFit(xdata, ydata, p0, params)

print (PLresult.sigma_err, PLresult.alpha_err, PLPLresult.sigma_err, PLPLresult.sb)
#To know more about how to access the result: help(Result)

```

In case you want to dig deeper or just use the function for plotting:
```python
from LNPLPL_functions import *
from LNPL_functions import *
```
or whichever way you wish to import the modules and the functions within them. 


To get help on any function:
```python
help(function_name)
```

If you're on the pypi page, please checkout the github version to get the sample data if you need.