%global _empty_manifest_terminate_build 0 Name: python-ARAMBHML Version: 0.2.4 Release: 1 Summary: An Auto ML framework that solves Classification Tasks License: MIT License URL: https://pypi.org/project/ARAMBHML/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b7/66/6f645cb49554775688745ae35782f37279b79d2e157f22d94bf248a4e1c9/ARAMBHML-0.2.4.tar.gz BuildArch: noarch Requires: python3-pandas Requires: python3-numpy Requires: python3-matplotlib Requires: python3-seaborn Requires: python3-scikit-learn Requires: python3-sklearn-pandas Requires: python3-xgboost Requires: python3-plotly Requires: python3-plotly-express %description # 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 6 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 ARAMBHML ``` ## Example # test.py ``` from ARAMBHML import arambhNet ``` ``` new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file ``` ``` new.get_model_details(path,target) ``` ## Run the Above Commands to get the results. **NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options ## Here you can perform EDA and also see how do models perform on the dataset. ## Note - I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later. %package -n python3-ARAMBHML Summary: An Auto ML framework that solves Classification Tasks Provides: python-ARAMBHML BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-ARAMBHML # 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 6 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 ARAMBHML ``` ## Example # test.py ``` from ARAMBHML import arambhNet ``` ``` new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file ``` ``` new.get_model_details(path,target) ``` ## Run the Above Commands to get the results. **NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options ## Here you can perform EDA and also see how do models perform on the dataset. ## Note - I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later. %package help Summary: Development documents and examples for ARAMBHML Provides: python3-ARAMBHML-doc %description help # 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 6 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 ARAMBHML ``` ## Example # test.py ``` from ARAMBHML import arambhNet ``` ``` new = arambhNet(path,target) #target is the dependent feature and path is the path of csv file ``` ``` new.get_model_details(path,target) ``` ## Run the Above Commands to get the results. **NOTE** There are more than functionalities available, you can press "tab" after "new." to get the options ## Here you can perform EDA and also see how do models perform on the dataset. ## Note - I have tried to implement all the functionality, it might have some bugs also. Moreover a number of more functionalities would be added later. %prep %autosetup -n ARAMBHML-0.2.4 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-ARAMBHML -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.2.4-1 - Package Spec generated