# iris_tableau_python

**Repository Path**: denhill/iris_tableau_python

## Basic Information

- **Project Name**: iris_tableau_python
- **Description**: leverage the power of python and tableau to build real-time ML data analysis based on Iris dataset.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-30
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# iris_tableau_python
### leverage the power of python and tableau to build up real-time ML data analysis solution based on Iris dataset.

_Python version_: 2.7  
_Tableau Desktop_: 10.3

Before you run the twbx file, you need to prepare your enviroment as below:
1. download iris_classiffier2.ipynb and dataset files;
2. config jupyter notebook enviroment on your machine;
3. install tabpy_server and tabpy_client then startup the server;
4. run ipynb file and deploy the function to tabpy server;
5. open twbx file, config tabpy server address, then enjoy it!

![image could be find in github page](/Iris_analysis_and_prediction.gif "Iris_analysis_and_prediction")

#### References： 
[Tableau Tabpy Github page](https://github.com/tableau/TabPy)   
[Building advanced analytics applications with TabPy](https://www.tableau.com/about/blog/2017/1/building-advanced-analytics-applications-tabpy-64916)