Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Datacamp

Case Study: School Budgeting with Machine Learning in Python

via Datacamp

Overview

Learn how to build a model to automatically classify items in a school budget.

Data science isn't just for predicting ad-clicks-it's also useful for social impact! This course is a case study from a machine learning competition on DrivenData. You'll explore a problem related to school district budgeting. By building a model to automatically classify items in a school's budget, it makes it easier and faster for schools to compare their spending with other schools. In this course, you'll begin by building a baseline model that is a simple, first-pass approach. In particular, you'll do some natural language processing to prepare the budgets for modeling. Next, you'll have the opportunity to try your own techniques and see how they compare to participants from the competition. Finally, you'll see how the winner was able to combine a number of expert techniques to build the most accurate model.

Syllabus

Exploring the raw data
-In this chapter, you'll be introduced to the problem you'll be solving in this course. How do you accurately classify line-items in a school budget based on what that money is being used for? You will explore the raw text and numeric values in the dataset, both quantitatively and visually. And you'll learn how to measure success when trying to predict class labels for each row of the dataset.

Creating a simple first model
-In this chapter, you'll build a first-pass model. You'll use numeric data only to train the model. Spoiler alert - throwing out all of the text data is bad for performance! But you'll learn how to format your predictions. Then, you'll be introduced to natural language processing (NLP) in order to start working with the large amounts of text in the data.

Improving your model
-Here, you'll improve on your benchmark model using pipelines. Because the budget consists of both text and numeric data, you'll learn to how build pipielines that process multiple types of data. You'll also explore how the flexibility of the pipeline workflow makes testing different approaches efficient, even in complicated problems like this one!

Learning from the experts
-In this chapter, you will learn the tricks used by the competition winner, and implement them yourself using scikit-learn. Enjoy!

Taught by

Peter Bull

Related Courses

Reviews

Start your review of Case Study: School Budgeting with Machine Learning in Python

Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free