Python Auto-ML Package for Tabular Datasets

Overview
Tabular-AutoML

Tabular-AutoML

AutoML Package for tabular datasets

Tabular dataset tuning is now hassle free!

Run one liner command and get best tuning and processed dataset in a go.

Python Git

Used Python Libraries :
lightgbm numpy numpy numpy

Installation & Usage


  1. Create a Virtual Environment : Tutorial
  2. Clone the repository.
  3. Open the directory with cmd.
  4. Copy this command in terminal to install dependencies.
pip install -r requirements.txt
  1. Installing the requirements.txt may generate some error due to outdated MS Visual C++ Build. You can fix this problem using this.
  2. First check the parser variable that has to be passed with all customizations.
>>> python -m tab_automl.main --help
usage: main.py [-h] -d  -t  -tf  [-p] [-f] [-spd] [-sfd] [-sm]

automl hyper parameters

optional arguments:
  -h, --help            show this help message and exit
  -d , --data-source    File path
  -t , --problem-type   Problem Type , currently supporting *regression* or *classification*
  -tf , --target-feature
                        Target feature inside the data
  -p , --pre-proc       If data processing is required
  -f , --fet-eng        If feature engineering is required
  -spd , --save-proc-data
                        Save the processed data
  -sfd , --save-fet-data
                        Save the feature engineered data
  -sm , --save-model    Save the best trained model
  1. Now run the command with your custom data, problem type and target feature
>> # For Classification Problem >>> python -m tab_automl.main -d "your custom data scource\custom_data.csv" -t "classification" -tf "your_custom_target_feature" -spd "true" -sfd "true" -sm "true"">
>>> # For Regression Problem
>>> python -m tab_automl.main -d "your custom data scource\custom_data.csv" -t "regression" -tf "your_custom_target_feature" -spd "true" -sfd "true" -sm "true"

>>> # For Classification Problem
>>> python -m tab_automl.main -d "your custom data scource\custom_data.csv" -t "classification" -tf "your_custom_target_feature" -spd "true" -sfd "true" -sm "true"

Contributing Guidelines


  1. Coment on the issue on which you want to work.
  2. If you get assigned, fork the repository.
  3. Create a new branch which should be named on your github user_id , e.g. sagnik1511.
  4. Update the changes on that branch.
  5. Create a PR (Pull request) to the main branch of the parent repository.
  6. The PR title should named like this [Issue Number] Heading of the issue.
  7. Describe the changes you have done with proper reasons.

Contributors


  1. Sagnik Roy : sagnik1511

If you like the project, do

Also follow me on GitHub , Kaggle , LinkedIn

Thank You for Visiting :)

Owner
Sagnik Roy
Data Science Intern @ Argoid • Video Games & Machine Vision attracts me!
Sagnik Roy
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