Update README.md

Author: rab657

README.md

@@ -1,54 +1,56 @@
-<h1 align="center">
-	<img width="700" src="main_page_banner.png" alt="explainX.ai"> 
-	<br>
-</h1>
+# explainX: Explain & Debug Any Machine Learning Model With A Single Line of Code 
+<img src="explainx_logo.png" align="right" width="150"/>
 
-<p align="center">
-  <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.6%20|%203.7|%203.8-brightgreen.svg" alt="Python supported"></a>
-  <!-- <a href="https://pypi.org/project/explainx/"><img src="https://badge.fury.io/py/explainx.svg" alt="PyPi Version"></a> -->
-  <!-- <a href="https://pypi.org/project/explainx/"><img src="https://img.shields.io/pypi/dm/explainx" alt="PyPi Downloads"></a> -->
-  <a href="https://www.explainx.ai/"> <img src="https://img.shields.io/website?url=https%3A%2F%2Fwww.explainx.ai%2F" alt="explainx.ai website"></a>
-</p>
+#### ExplainX.ai is a model explainability/interpretability framework for data scientists and business users.
 
-ExplainX.ai is a fast, scalable and end-to-end Explainable AI framework for data scientists & machine learning engineers. 
+[![Supported Python versions](https://img.shields.io/badge/python-3.6%20|%203.7|%203.8-brightgreen.svg)](https://pypi.org/project/explainx/)
+[![Downloads](https://pepy.tech/badge/dalex)](https://pepy.tech/project/dalex)
+![Maintenance](https://img.shields.io/maintenance/yes/2020?style=flat-square)
+[![Website](https://img.shields.io/website?url=https%3A%2F%2Fwww.explainx.ai%2F)](https://www.explainx.ai/)
 
-Understand overall model behavior, get the reasoning behind model predictions, remove biases and create convincing explanations for your business stakeholders with explainX. [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Explain%20any%20black-box%20Machine%20Learning%20model%20in%20just%20one%20line%20of%20code%21&url=https://www.explainx.ai&hashtags=xai,explainable_ai,explainable_machine_learning,trust_in_ai,transparent_ai)
+
+Use explainX to understand overall model behavior, explain the "why" behind model predictions, remove biases and create convincing explanations for your business stakeholders. [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Explain%20any%20black-box%20Machine%20Learning%20model%20in%20just%20one%20line%20of%20code%21&url=https://www.explainx.ai&hashtags=xai,explainable_ai,explainable_machine_learning,trust_in_ai,transparent_ai)
 
 <img width="1000" src="rf_starter_example.png" alt="explainX.ai">
 
-#### Why we need explainability & interpretibility?
+ExplainX.ai is a model explainability/interpretability framework for data scientists and business users.
+
+#### Why we need model explainability & interpretibility?
 
 Essential for:
-1. Model debugging - Why did my model make a mistake? 
-2. Detecting fairness issues - Is my model biased? If yes, where?
-3. Human-AI cooperation - How can I understand and trust the model's decisions?
-4. Regulatory compliance - Does my model satisfy legal & regulatory requirements?
-5. High-risk applications - Healthcare, Financial Services, FinTech, Judicial, Security etc,.
+1. Explaining model predictions
+2. Debugging models
+3. Detecting biases in data
+4. Gaining trust of business users
+5. Successfully deploying AI solution
 
-Visit our website to learn more: https://www.explainx.ai     
+#### What questions can we answer with explainX?
 
-## Try it out
+1. Why did my model make a mistake? 
+2. Is my model biased? If yes, where?
+3. How can I understand and trust the model's decisions?
+4. Does my model satisfy legal & regulatory requirements?
 
-* [Installing explainX](https://docs.explainx.ai/getting-started/installation)
-* [Working Examples](https://docs.explainx.ai/getting-started/starter-example)
-* [explainX Dashboard Features](https://docs.explainx.ai/tutorials/analyzing-dashboard)
-* [Documentation](https://docs.explainx.ai/)
-* [Help Us Improve explainX.ai](https://forms.gle/5Q1xaHd7s6UQkRzf8)
+Visit our website to learn more: https://www.explainx.ai     
 
+# Get Started
 
-# Installation
+## Installation
 
-Python 3.5+ | Linux, Mac, Windows (Install [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) first to run on Windows.)
+Python 3.5+ | Linux, Mac, Windows
 
 ```sh
 pip install explainx
 ```
 
+To download on Windows, please install [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) first and then install the explainX package via ``` pip ```
+
 ## Installation on the cloud
-If you are using AWS SageMaker of Jupyter Notebook deployed on the cloud, visit our documentation for step-by-step guide installing and running explainX. [Cloud Installation Instructions](https://docs.explainx.ai/getting-started/installation)
+If you are using a notebook instance on the cloud (AWS SageMaker, Colab, Azure), please follow our step-by-step guide to install & run explainX cloud. 
+[Cloud Installation Instructions](https://docs.explainx.ai/getting-started/installation)
 
 
-## Example Usage
+## Usage (Example)
 After successfully installing explainX, open up your Python IDE of Jupyter Notebook and simply follow the code below to use it:
 
 1. Import required module.
@@ -57,7 +59,6 @@ After successfully installing explainX, open up your Python IDE of Jupyter Noteb
 from explainx import * 
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
-
 ```
 
 2. Load and split your dataset into x_data and y_data
@@ -109,22 +110,6 @@ lt -h "https://serverless.social" -p 8080
 
 <img width="1000" src="demo-explainx-with-sound.gif" alt="explainX.ai">
 
-Learn to analyze the dashboard by following this link: [explainX Dashboard Features](https://explainx-documentation.netlify.app/analyze-dashboard/)
-
-Visit the documentation to [learn more](https://docs.explainx.ai/)
-
-## Models Supported
-1. Catboost
-2. xgboost==1.0.2
-3. Gradient Boosting Regressor
-4. RandomForest Model
-5. SVM
-6. KNeighboursClassifier
-7. Logistic Regression
-8. DecisionTreeClassifier
-9. Scikit-learn Models
-10. Neural Networks
-
 ## Walkthough Video Tutorial
 
 Please click on the image below to load the tutorial:
@@ -133,6 +118,38 @@ Please click on the image below to load the tutorial:
 
 (Note: Please manually set it to 720p or greater to have the text appear clearly)
 
+## Supported Techniques
+
+|Interpretability Technique | Status |
+|--|--|
+|SHAP Kernel Explainer| Live |
+|SHAP Tree Explainer| Live |
+|What-if Analysis| Live |
+|Model Performance Comparison | Live |
+|Partial Dependence Plot| Live |
+|Surrogate Decision Tree | Coming Soon |
+|Anchors | Coming Soon |
+|Integrated Gradients (IG)| Coming Soon |
+
+## Main Models Supported
+
+| No. | Model Name | Status |
+|--|--|--|
+|1. | Catboost | Live|
+|2. | XGboost==1.0.2 | Live|
+|3. | Gradient Boosting Regressor| Live|
+|4. | RandomForest Model| Live|
+|5. | SVM|Live|
+|6. | KNeighboursClassifier| Live
+|7. | Logistic Regression| Live |
+|8. | DecisionTreeClassifier|Live |
+|9. | All Scikit-learn Models|Live |
+|10.| Neural Networks|Live |
+|11.| H2O.ai AutoML | Next in Line |
+|12.| TensorFlow Models | Coming Soon |
+|13.| PyTorch Models | Coming Soon |
+
+
 ## Contributing
 Pull requests are welcome. In order to make changes to explainx, the ideal approach is to fork the repository then clone the fork locally.