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`).

# Import necessary libraries
import pandas as pd

# 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()

# Start coding here. Use as many cells as you like!

# Identify variables with only two categories in ds_jobs_transformed
binary_columns = [col for col in ds_jobs_transformed.columns if ds_jobs_transformed[col].nunique() == 2]

# Display the binary columns
binary_columns
print(ds_jobs_transformed['job_change'].value_counts())

# Convert relevant_experience to boolean
dict_relv_exp = {"Has relevant experience": True, "No relevant experience": False}
ds_jobs_transformed['relevant_experience'] = ds_jobs_transformed['relevant_experience'].map(dict_relv_exp)

# Convert job_change to boolean
ds_jobs_transformed['job_change'] = ds_jobs_transformed['job_change'].astype(bool)

# Convert student_id a int32
ds_jobs_transformed['student_id'] = ds_jobs_transformed['student_id'].astype('int32')
ds_jobs_transformed['training_hours'] = ds_jobs_transformed['training_hours'].astype('int32')

# Identificar todas las variables de tipo character con más de dos categorías en ds_jobs_transformed
char_columns = [col for col in ds_jobs_transformed.select_dtypes(include=['object']).columns if ds_jobs_transformed[col].nunique() > 2]
char_columns

# Identificar variables nominales y ordinales en char_columns
nominal_columns = []
ordinal_columns = []

# List of known ordinal columns for this dataset
known_ordinal_columns = ['education_level', 'experience', 'last_new_job', 'company_size', 'enrolled_university']

for col in char_columns:
    if col in known_ordinal_columns:
        ordinal_columns.append(col)
    else:
        nominal_columns.append(col)

nominal_columns, ordinal_columns

print(ds_jobs_transformed["enrolled_university"].value_counts())

# Transformar las variables cadena a categóricas nominales
ds_jobs_transformed[nominal_columns] = ds_jobs_transformed[nominal_columns].astype('category')

# Transformar las variables cadena a categorías ordinales
import pandas as pd

# Education Level:
education_level_order = ["Phd", "Masters", "Graduate", "High School", "Primary School"]
education_level_dtype = pd.CategoricalDtype(categories=education_level_order, ordered=True)
ds_jobs_transformed["education_level"] = ds_jobs_transformed["education_level"].astype(education_level_dtype)

# Experience:
experience_order = ["<1"] + [str(i) for i in range(1, 21)] + [">20"]
experience_dtype = pd.CategoricalDtype(categories=experience_order, ordered=True)
ds_jobs_transformed["experience"] = ds_jobs_transformed["experience"].astype(experience_dtype)

# Company Size:
company_size_order = ["<10", "10-49", "50-99", "100-500", "500-999", "1000-4999", "5000-9999", "10000+"]
company_size_dtype = pd.CategoricalDtype(categories=company_size_order, ordered=True)
ds_jobs_transformed["company_size"] = ds_jobs_transformed["company_size"].astype(company_size_dtype)

# Last New Job:
last_new_job_order = ["never"] + [str(i) for i in range(1, 5)]+[">4"]
last_new_job_order_dtype = pd.CategoricalDtype(categories=last_new_job_order, ordered=True)
ds_jobs_transformed["last_new_job"]=ds_jobs_transformed["last_new_job"].astype(last_new_job_order_dtype)

# Enrolled University
enrolled_university_order = ["no_enrollment", "Part time course", "Full time course"]
enrolled_university_dtype = pd.CategoricalDtype(categories=enrolled_university_order, ordered=True)
ds_jobs_transformed["enrolled_university"]=ds_jobs_transformed["enrolled_university"].astype(enrolled_university_dtype)

ds_jobs_transformed["city_development_index"] = ds_jobs_transformed["city_development_index"].astype('float16')

# Filtrar por estudiantes con 10 o más años de experiencia en empresas con al menos 1000 empleados
ds_jobs_transformed = ds_jobs_transformed[(ds_jobs_transformed["experience"]>="10")&(ds_jobs_transformed["company_size"]>="1000-4999")]
ds_jobs_transformed