Metadata-Version: 2.4
Name: mlaunch
Version: 0.2.0
Summary: Automatic ML pipeline builder
Author-email: Abdulrahman Alowidi <abode.bader@outlook.sa>
License: MIT License
        
        Copyright (c) 2026 Abdulrahman Alowidi
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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Project-URL: Homepage, https://github.com/Abode-18
Keywords: ML,AutoML,Pipeline
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: joblib
Requires-Dist: category_encoders
Dynamic: license-file

# mlaunch

## About the Project
mlaunch is a Python package that automatically generates a complete machine learning model from a CSV file. It handles data preprocessing, encoding, model selection, and evaluation.

## Functions
### AutoML
this function will preprocess the data and create the model for you
```python
from mlaunch import AutoML
model = AutoML(path,y_column,model_name,type = "pipeline")
```
#### parameteres
- path: the path for your csv file
- y_column: the target column in your dataset
- model_name: the name of your model out of these current model:
    - Linear Regression
    - Logistic Regression
    - Random Forest Regression
    - Hist Gradient Boosting Regression
    - Random Forest classifier
    - Hist Gradient Boosting classifier
    - Auto Select : it will select the best model for your data
    more will be added in the future
- type: how will the output be:
    - pipeline: it will output the model as a pipeline
    - python file: it will export the model to `model.pkl` file and run a python file to input the data

### preprocessing
this function will handle the outliers and encode your data and output it as a ColumnTransformer
```python 
from mlaunch import preprocessing
preprocessor = preprocessing(model,df,y_column)
model = Pipeline([
    ("preprocessor",preprocessor)
])
```
#### parameteres
- model: put your model here
- df: put the dataframe here
- y_column: the target column in your dataset

### dataset_info
this function returns a dictionary of the size, cat_columns and num_columns
```python
from mlaunch import dataset_info
info = dataset_info(df,y_column)
```
#### parameteres
- df: put the dataframe here
- y_column: the target column in your dataset

### column_statistics
this function returns a bunch of stats for every column in the dataframe
```python
from automl import column_statistics
stats = column_statistics(df,y_column)
```
#### parameteres
- df: put the dataframe here
- y_column: the target column in your dataset
