- #Predict house prices with simplexlp install#
- #Predict house prices with simplexlp manual#
Alternatively, you can run the test.py script. Hopefully you will not see any error messages. To test if you have everything, open your Python shell (it should say Python 2.7.x), and run the following commands (this is how you import libraris). Again, there are so many instructions online that are fantastic.
#Predict house prices with simplexlp install#
If you're on a Mac, my recommendation is to install Homebrew, then brew install python2, and then pip2 install instead of pip install.
nnector - pip install mysql-connector=2.1.6. pandas - pip install pandas or conda install pandas (if you previously install conda). You need to make sure that you have the following software/libraries (and their dependencies) installed on your machine: (Sorry, Python is the reason I traded Windows one night for Arch Linux a few years ago.) Please refer to the online documentation for doing this in Windows. The following instructions are for Unix-based systems (including Mac). If you'd rather use a Unix box on Cloud9, you can skip this step and read the Using Cloud9 instructions instead. To do this tutorial, you will need to have some software and libraries installed on your computer. You will have access to both temperature and energy data at a temporal resolutions of 15 minutes. More specifically, we query the energy consumption of a solar hyrbrid mini split air conditioner used for agricultural produce cooling. This project is based on an energy-analytics project, which means that we will query energy related data. It has not been tested with any other version of Python. Note: This tutorial is written in and for Python 2.7. We'll first use the nnector libray to query and retrieve data from a SQL server, then load that data into a pandas dataframe, and ultimately plot the data. In this brief tutorial, we will look at the use of the pandas library in Python for data analytics. Lab 1: Tableau - Part I - Updated 9/20īI Concept of the week (12/15) Tutorial and Intro to Data Analytics with Python. Lab 3a: Power BI - Part I - Updated 9/19. Lab 3b: Power BI - Part II - Updated 9/21. Lab 4: Power BI in Excel - Updated 10/3. Lab 5: Decision Tree for predicting MBA graduates' promotion. Lab 6: Linear Regression with RapidMiner - Updated 11/22. Lab 7: K-Mean Clustering with RapidMiner. Lab 8: Twitter Sentiment Analysis with RapidMiner. Lab 9: Web Analytics with Google Analytics. Lab 10: Linear Programming Optimization - new. 
#Predict house prices with simplexlp manual#
ITM387K: Lab Manual URL: /itm387k Table of Contents
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