![rain city time machine rain city time machine](https://i.pinimg.com/originals/33/64/f5/3364f53a8b8b0c5c503b5e171d4d7c99.jpg)
#RAIN CITY TIME MACHINE HOW TO#
In this article, I will describe how to programmatically pull daily weather data from Weather Underground using their free tier of service available for non-commercial purposes. The company provides a swath of API's that are available for both commercial and non-commercial uses. Weather Underground is a company that collects and distributes data on various weather measurements around the globe. Getting Familiar with Weather Underground I will compare the process of building a Neural Network model, interpreting the results and, overall accuracy between the Linear Regression model built in the prior article and the Neural Network model. The final article will focus on using Neural Networks. This article will conclude with a discussion of Linear Regression model testing and validation. I will discuss the importance of understanding the assumptions necessary for using a Linear Regression model and demonstrate how to evaluate the features to build a robust model. The second article will focus on analyzing the trends in the data with the goal of selecting appropriate features for building a Linear Regression model using the statsmodels and scikit-learn Python libraries. Once collected, the data will need to be process and aggregated into a format that is suitable for data analysis, and then cleaned. I will be using the requests library to interact with the API to pull in weather data since 2015 for the city of Lincoln, Nebraska.
#RAIN CITY TIME MACHINE SERIES#
The data used in this series will be collected from Weather Underground's free tier API web service. Data collection and processing (this article).The series will be comprised of three different articles describing the major aspects of a Machine Learning project. This is the first article of a multi-part series on using Python and Machine Learning to build models to predict weather temperatures based off data collected from Weather Underground. Part 1: Collecting Data From Weather Underground