Project: Customer Analytics: Preparing Data for Modeling

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 work 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 work experience (in years).
`company_size`	The number of employees at the student's current employer.
`company_type`	The type of company employing the student.
`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`).

Tasks

The Head Data Scientist at Training Data Ltd. has asked you to create a DataFrame called ds_jobs_clean that stores the data in customer_train.csv much more efficiently. Specifically, they have set the following requirements:

Columns containing integers must be stored as 32-bit integers (int32).
Columns containing floats must be stored as 16-bit floats (float16).
Columns containing nominal categorical data must be stored as the category data type.
Columns containing ordinal categorical data must be stored as ordered categories, and not mapped to numerical values, with an order that reflects the natural order of the column.
The columns of ds_jobs_clean must be in the same order as the original dataset.
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.
If you call .info() or .memory_usage() methods on ds_jobs and ds_jobs_clean after you've preprocessed it, you should notice a substantial decrease in memory usage.

# Import necessary libraries
import pandas as pd

# 1 - Exploratory data analysis

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

# View the dataset
ds_jobs.head()

# Create a copy of ds_jobs for transforming
ds_jobs_transformed = ds_jobs.copy()

# 2 - Converting integers, floats, and unordered categories

# 2.1 - Identify the data type of each column in the dataset:
ds_jobs_transformed.dtypes

# 2.2 - Converting integers to int32,  columns student_id and training_hours:
ds_jobs_transformed["student_id"] = ds_jobs_transformed["student_id"].astype('int32')

ds_jobs_transformed["training_hours"] = ds_jobs_transformed["training_hours"].astype('int32')

# 2.3 - Converting floats to float16, only the column city_development_index:
ds_jobs_transformed["city_development_index"] = ds_jobs_transformed["city_development_index"].astype('float16')

# 2.4 - Converting nominal categories using a list to iterate over, columns city, gender, major_discipline, company_type: 
nominal_categories = ["city", "gender","major_discipline","company_type"]

ds_jobs_transformed[nominal_categories] = ds_jobs_transformed[nominal_categories].astype("category")

# 2.5 - Converting two-factor categories to bool, columns relevant_experience and job_change:
ds_jobs_transformed["relevant_experience"] = ds_jobs_transformed["relevant_experience"].map({
    'Has relevant experience': True,
    'No relevant experience': False})

ds_jobs_transformed["job_change"] = ds_jobs_transformed["job_change"].astype("bool")

# 3 - Converting ordered categories, columns education_level, experience, enrolled_university company_size and last_new_job

# 3.1 - Converting the education_level column using a list:
education_level_order = ['Primary School', 'High School', 'Graduate', 'Masters', 'Phd']

ds_jobs_transformed["education_level"] = pd.Categorical(ds_jobs_transformed["education_level"], categories=education_level_order, ordered=True)

# 3.2 - Converting the experience column using a list:
experience_order = ['<1'] + list(map(str, range(1, 21))) + ['>20']

ds_jobs_transformed["experience"] = pd.Categorical(ds_jobs_transformed["experience"], categories=experience_order, ordered=True)

# 3.3 - Converting the company_size column using a list:
company_size_order = ['<10', '10-49', '50-99', '100-499', '500-999', '1000-4999', '5000-9999', '10000+']

ds_jobs_transformed["company_size"] = pd.Categorical(ds_jobs_transformed["company_size"], categories=company_size_order, ordered=True)

# 3.4 - Converting the last_new_job column using a list:
last_new_job_order = ['never', '1', '2', '3', '4', '>4']

ds_jobs_transformed["last_new_job"] = pd.Categorical(ds_jobs_transformed["last_new_job"], categories=last_new_job_order, ordered=True)

# 3.5 - Converting the enrolled_university column using a list:
enrolled_university_order = ['no_enrollment', 'Part time course', 'Full time course']

ds_jobs_transformed["enrolled_university"] = pd.Categorical(ds_jobs_transformed["enrolled_university"], categories=enrolled_university_order, ordered=True)

# 4 - Filtering on ordered categorical columns
ds_jobs_transformed = ds_jobs_transformed[(ds_jobs_transformed['experience'] >= '10') & (ds_jobs_transformed['company_size'] >= '1000-4999')]

print(ds_jobs_transformed.memory_usage())

Final conclusion

In conclusion, our project has successfully demonstrated a substantial decrease in memory usage. Through careful data transformation and optimization techniques, we were able to streamline our dataset and enhance its efficiency, leading to a significant reduction in memory consumption. This improvement not only enhances the performance and scalability of our data processing tasks but also positions our solution as a more effective and resource-efficient approach to managing large datasets.