A common problem when creating models to generate business value from data is that the datasets can be so large that it can take days for the model to generate predictions. Ensuring that your dataset is stored as efficiently as possible is crucial for allowing these models to run on a more reasonable timescale without having to reduce the size of the dataset.

You've been hired by a major online data science training provider called Training Data Ltd. to clean up one of their largest customer datasets. This dataset will eventually be used to predict whether their students are looking for a new job or not, information that they will then use to direct them to prospective recruiters.

You've been given access to customer_train.csv, which is a subset of their entire customer dataset, so you can create a proof-of-concept of a much more efficient storage solution. The dataset contains anonymized student information, and whether they were looking for a new job or not during training:

Column	Description
student_id	A unique ID for each student.
city	A code for the city the student lives in.
city_development_index	A scaled development index for the city.
gender	The student's gender.
relevant_experience	An indicator of the student's relevant experience.
enrolled_university	The type of university course enrolled in (if any).
education_level	The student's education level.
major_discipline	The educational discipline of the student.
experience	The student's total experience (in years).
company_size	The number of employees at the student's current employer.
last_new_job	The number of years between the student's current and previous jobs.
training_hours	The number of hours of training completed.
job_change	An indicator of whether the student is looking for a new job (1) or not (0).