diff --git a/README.md b/README.md
index a4acea6f1abc979090b5e9074c03666bdbeefe2e..6b5f638eed4c45ca9ccbc9427c3194cb8f816d93 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,21 @@
## pycaret
pycaret is the free software and open source machine learning library for python programming language. It is built around several popular machine learning libraries in python. Its primary objective is to reduce the cycle time of hypothesis to insights by providing an easy to use high level unified API. pycaret's vision is to become defacto standard for teaching machine learning and data science. Our strength is in our easy to use unified interface for both supervised and unsupervised machine learning problems. It saves time and effort that citizen data scientists, students and researchers spent on coding or learning to code using different interfaces, so that now they can focus on business problem and value creation.
-## Key Features
-* Ease of Use
-* Focus on Business Problem
-* 10x efficient
-* Collaboration
-* Business Ready
-* Cloud Ready
-
## Current Release
-The current release is beta 0.0.5 (as of 23/12/2019). A full release for public is targetted to be available by 31/12/2020.
+The current release is beta 0.0.6 (as of 24/12/2019). A full release is targetted to be available by 31/01/2020.
+
+## Features Currently Available
+As per beta 0.0.6 following modules are generally available:
+*pycaret.classification
+*pycaret.regression
+*pycaret.nlp
+*pycaret.arules
+
+## Future Release
+Following features are targetted for future release (beta 0.0.7 & beta 0.0.8):
+*pycaret.anamoly
+*pycaret.clustering
+*pycaret.preprocess
## Installation
@@ -25,20 +30,22 @@ pip install pycaret
```
## Quick Start
-As of beta 0.0.5 classification, regression and nlp modules are available. Future release will be include Anamoly Detection, Association Rules, Clustering, Recommender System and Time Series.
+As of beta 0.0.6 classification, regression and nlp modules are available. Future release will be include Anamoly Detection, Association Rules, Clustering, Recommender System and Time Series.
-### Classification
+### Classification / Regression
Getting data from pycaret repository
```python
from pycaret.datasets import get_data
-juice = get_data('juice')
+juice = get_data('juice') #classification dataset
```
1. Initializing the pycaret environment setup
```python
+from pycaret.classification import * #for classification
+from pycaret.regression import * #for regression
exp1 = setup(juice, 'Purchase')
```
@@ -67,7 +74,12 @@ Extra Trees Classifier (et)
Extreme Gradient Boosting - xgboost (xgboost)
Light Gradient Boosting - Microsoft LightGBM (lightgbm)
-3. Tuning a model using inbuilt grids.
+3. Compare all models at once
+```python
+compare_models()
+```
+
+4. Tuning a model using pre-built search grids.
```python
tuned_xgb = tune_model('xgboost')
```
@@ -171,18 +183,35 @@ m = load_model('lr_23122019')
e = load_experiment('expname1')
```
-14. Compare all Models
-```python
-compare_models()
-```
+## AutoML
+All modules also have AutoML module built-in. It is very easy to run AutoML.
-15. AutoML
```python
-aml1 = automl()
+from pycaret.datasets import get_data
+juice = get_data('juice')
+
+from pycaret.classification import *
+exp1 = setup(data, 'Purchase')
+
+aml1 = automl() #same for regression
```
+## Getting Started Tutorials
+Tutorials are work in progress. Will be uploaded on our git page by 07/01/2020.
+
## Documentation
Documentation work is in progress. They will be uploaded on our website http://www.pycaret.org as soon as they are available. (Target Availability : 21/01/2020)
## Contributions
Contributions are most welcome. To make contribution please reach out moez.ali@queensu.ca
+
+## License
+
+Copyright 2019 pycaret
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is 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, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+© 2019 GitHub, Inc.
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diff --git a/requirements.txt b/requirements.txt
index 2febf0a3ddf304e63bed89eb1add29bac7444f4e..1118a1bed4ec23593ddf470f0b63bbbaa81f9777 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ seaborn
matplotlib
IPython
joblib
-sklearn
+scikit-learn
shap
ipywidgets
yellowbrick
@@ -18,4 +18,5 @@ umap-learn
pyLDAvis
gensim
spacy
-nltk
\ No newline at end of file
+nltk
+mlxtend
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diff --git a/setup.py b/setup.py
index 1cf5ac1d8c4e8bdd051d20f14aab9284561fb6d6..c6916c8fa786c5ac5a3ff83916b53617c325f9fd 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@ def readme():
setup(
name="pycaret",
- version="0.0.5",
+ version="0.0.6",
description="A Python package for supervised and unsupervised machine learning.",
long_description=readme(),
long_description_content_type="text/markdown",
@@ -24,7 +24,7 @@ setup(
packages=["pycaret"],
include_package_data=True,
install_requires=["pandas", "numpy", "seaborn", "matplotlib", "IPython", "joblib",
- "sklearn", "shap", "ipywidgets", "yellowbrick", "xgboost==0.90",
+ "scikit-learn", "shap", "ipywidgets", "yellowbrick", "xgboost==0.90",
"wordcloud", "textblob", "plotly==4.4.1", "cufflinks", "umap-learn",
- "lightgbm==2.3.1", "pyLDAvis", "gensim", "spacy", "nltk"]
+ "lightgbm==2.3.1", "pyLDAvis", "gensim", "spacy", "nltk", "mlxtend"]
)
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