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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:

ColumnDescription
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.
company_typeThe type of company employing the student.
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 necessary libraries
import pandas as pd

# Load the dataset
ds_jobs = pd.read_csv("customer_train.csv")

# View the dataset
ds_jobs.head()
# Perform EDA to identify ordinal, nominal, and binary categorical features
for column in df.select_dtypes("object").columns:
    print(df_transformed[column].value_counts(), '\n')

# Define mappings for ordered categorical columns
ordered_categories = {
    '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']
}

# Define mappings for binary categorical columns
binary_categories = {
    'relevant_experience': {'No relevant experience': False, 'Has relevant experience': True},
    'job_change': {0.1: False, 1.0: True}
}

# Update data types based on column categories
for column in df_transformed:

    # Convert binary categorical columns to boolean
    if column in binary_categories:
        df_transformed[column] = df_transformed[column].map(binary_categories[column])

    # Convert integer columns to int32
    elif column in ['student_id', 'training_hours']:
        df_transformed[column] = df_transformed[column].astype('int32')

    # Convert float columns to float16
    elif column == 'city_development_index':
        df_transformed[column] = df_transformed[column].astype('float16')

    # Apply ordered categories to specified columns
    elif column in ordered_categories:
        cat_type = pd.CategoricalDtype(ordered_categories[column], ordered=True)
        df_transformed[column] = df_transformed[column].astype(cat_type)

    # Convert other columns to standard categories
    else:
        df_transformed[column] = df_transformed[column].astype('category')

# Filter records for individuals with 10+ years of experience at companies with 1000+ employees
df_transformed = df_transformed[(df_transformed['experience'] >= '10') & (df_transformed['company_size'] >= '1000-4999')]