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  3. Case Study: School Budgeting with Machine Learning in Python
  • 1

    Exploring the raw data

    Free

    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.

  • 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.

  • 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!

  • 4

    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!

Initializing

Exercise

Use your model to predict values on holdout data

You're ready to make some predictions! Remember, the train-test-split you've carried out so far is for model development. The original competition provides an additional test set, for which you'll never actually see the correct labels. This is called the "holdout data."

The point of the holdout data is to provide a fair test for machine learning competitions. If the labels aren't known by anyone but DataCamp, DrivenData, or whoever is hosting the competition, you can be sure that no one submits a mere copy of labels to artificially pump up the performance on their model.

Remember that the original goal is to predict the probability of each label. In this exercise you'll do just that by using the .predict_proba() method on your trained model.

First, however, you'll need to load the holdout data, which is available in the workspace as the file HoldoutData.csv.

Instructions

100 XP
  • Read HoldoutData.csv into a DataFrame called holdout. Specify the keyword argument index_col=0 in your call to read_csv().
  • Generate predictions using .predict_proba() on the numeric columns (available in the NUMERIC_COLUMNS list) of holdout. Make sure to fill in missing values with -1000!