Metadata-Version: 2.4
Name: ml-express
Version: 0.1.5
Summary: A Python library for day to day data analysis and machine learning.
Home-page: https://github.com/ved93/ml-express
Author: Ved
Author-email: vpved93@gmail.com
License: License :: OSI Approved :: MIT License
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: scipy>=1.9.3
Requires-Dist: numpy>=1.23.2
Requires-Dist: pandas>=1.5.0
Requires-Dist: scikit-learn>=1.1.3
Requires-Dist: fg-data-profiling>=4.19.0
Requires-Dist: matplotlib>=3.6.0
Requires-Dist: joblib>=1.2.0
Requires-Dist: Pillow>=9.3.0
Requires-Dist: seaborn>=0.12.0
Requires-Dist: graphviz>=0.20
Dynamic: author
Dynamic: author-email
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: requires-dist
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# ml_express

A Python library for day to day data analysis and machine learning.
This aims to make data building, cleaning and machine learning much much faster.



## Installation

pip install ml-express

## Usage

```python
import ml_express as mlx
from ml_express import eda

# this will create an html report and report will be saved in report folder in working directory
eda.create_summary_report(df)

# generate summary statistics of data type and categorical vars
eda.gen_eda(df)

# using correlation modules
# correlation for categorical vars
dfcramers = correlation.get_corr_df_cat(cat_cols,train_df_new)

# Draw the heatmap using seaborn
f, ax = plt.subplots(figsize=(12, 6))

sns.heatmap(dfcramers, annot=True, fmt='.2f',cmap='coolwarm',vmin=0 )
plt.title("Important Catg variables correlation map", fontsize=15)
plt.show()


# correlation for categorical and continuous vars
dfcramers = correlation.get_corr_df_cat_cont(cat_cols ,num_cols ,train_df_new)

# Draw the heatmap using seaborn
f, ax = plt.subplots(figsize=(12, 6))

sns.heatmap(dfcramers, annot=True, fmt='.2f',cmap='coolwarm',vmin=0,vmax = 1 )
plt.title("Important Catg vs Cont variables correlation map", fontsize=15)
plt.show()

```

### Preprocessing



## Contributing
Contributions are very welcome. Please feel free to submit PR. 


## License
Distributed under the terms of the MIT license, "ml-express" is free and open source software

## Issues
If you encounter any problems, please file an issue along with a detailed description.
