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Case Study: School Budgeting with Machine Learning in Python

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

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4 Hours15 Videos51 Exercises55,544 Learners3800 XPData Scientist TrackMachine Learning Fundamentals Track

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Course Description

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.

  1. 1

    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.

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    Introducing the challenge
    50 xp
    What category of problem is this?
    50 xp
    What is the goal of the algorithm?
    50 xp
    Exploring the data
    50 xp
    Loading the data
    50 xp
    Summarizing the data
    100 xp
    Looking at the datatypes
    50 xp
    Exploring datatypes in pandas
    50 xp
    Encode the labels as categorical variables
    100 xp
    Counting unique labels
    100 xp
    How do we measure success?
    50 xp
    Penalizing highly confident wrong answers
    50 xp
    Computing log loss with NumPy
    100 xp
  2. 2

    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.

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  3. 3

    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!

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In the following tracks

Data Scientist Machine Learning Fundamentals


hugobowneHugo Bowne-AndersoncalanfitzpatrickCasey FitzpatrickyashasYashas Roy
Peter Bull Headshot

Peter Bull

Co-founder of DrivenData

Peter is a co-founder of DrivenData. He earned his master's in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences. His work lies at the intersection of statistics and computer science, and he wants to help bring powerful new modeling techniques to the organizations that need them most. He previously worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University.
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