Skip to content
Project: Customer Analytics: Preparing Data for Modeling
  • AI Chat
  • Code
  • Report
  • 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:

    student_idA unique ID for each student.
    cityA code for the city the student lives in.
    city_development_indexA scaled development index for the city.
    genderThe student's gender.
    relevant_experienceAn indicator of the student's work relevant experience.
    enrolled_universityThe type of university course enrolled in (if any).
    education_levelThe student's education level.
    major_disciplineThe educational discipline of the student.
    experienceThe student's total work experience (in years).
    company_sizeThe number of employees at the student's current employer.
    last_new_jobThe number of years between the student's current and previous jobs.
    training_hoursThe number of hours of training completed.
    job_changeAn indicator of whether the student is looking for a new job (1) or not (0).
    # Import required modules
    import pandas as pd
    ds_jobs = pd.read_csv("customer_train.csv")
    ds_jobs_clean = ds_jobs.copy()
    # Ordering categories
    ordered_cats = {
        'relevant_experience': ['No relevant experience', 'Has relevant experience'],
        'enrolled_university': ['no_enrollment', 'Part time course', 'Full time course'],
        'education_level': ['Primary School', 'High School', 'Graduate', 'Masters', 'Phd'],
        'experience': ['<1'] + list(map(str, range(1, 21))) + ['>20'],
        'company_size': ['<10', '10-49', '50-99', '100-499', '500-999', '1000-4999', '5000-9999', '10000+'],
        'last_new_job': ['never', '1', '2', '3', '4', '>4']
    # Arranging columns' type
    for column in ds_jobs_clean.columns:
        if ds_jobs_clean[column].dtype == "int64":
             ds_jobs_clean[column] = ds_jobs_clean[column].astype("int32")
        elif ds_jobs_clean[column].dtype == "float64":
             ds_jobs_clean[column] = ds_jobs_clean[column].astype("float16")
        elif column in ordered_cats.keys():
            category = pd.CategoricalDtype(ordered_cats[column], ordered=True)
            ds_jobs_clean[column] = ds_jobs_clean[column].astype(category)   
        # Convert remaining columns to standard categories
            ds_jobs_clean[column] = ds_jobs_clean[column].astype('category')
    # The DataFrame should be filtered to only contain students with 10 or more years of experience at companies with at least 1000 employees, as their recruiter base is suited to more experienced professionals at enterprise companies.
    ds_jobs_clean = ds_jobs_clean[(ds_jobs_clean["experience"] >= '10') & (ds_jobs_clean["company_size"] >= '1000-4999')]