Using Machine Learning to Predict the Weather: Part 1

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. The series will be comprised of three different articles describing the major aspects of a Machine Learning project. The data used in this series will be collected from Weather Underground's free tier API web service. 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. Once collected, the data will need to be process and aggregated into a format that is suitable for data analysis, and then cleaned. 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. 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.