Sleep Health and Lifestyle: TPOT (AutoML)

Sleep Health and Lifestyle

This synthetic dataset contains sleep and cardiovascular metrics as well as lifestyle factors of close to 400 fictive persons.

The workspace is set up with one CSV file, data.csv, with the following columns:

Person ID
Gender
Age
Occupation
Sleep Duration: Average number of hours of sleep per day
Quality of Sleep: A subjective rating on a 1-10 scale
Physical Activity Level: Average number of minutes the person engages in physical activity daily
Stress Level: A subjective rating on a 1-10 scale
BMI Category
Blood Pressure: Indicated as systolic pressure over diastolic pressure
Heart Rate: In beats per minute
Daily Steps
Sleep Disorder: One of None, Insomnia or Sleep Apnea

Check out the guiding questions or the scenario described below to get started with this dataset! Feel free to make this workspace yours by adding and removing cells, or editing any of the existing cells.

Source: Kaggle

🌎 Some guiding questions to help you explore this data:

Which factors could contribute to a sleep disorder?
Does an increased physical activity level result in a better quality of sleep?
Does the presence of a sleep disorder affect the subjective sleep quality metric?

📊 Visualization ideas

Boxplot: show the distribution of sleep duration or quality of sleep for each occupation.
Show the link between age and sleep duration with a scatterplot. Consider including information on the sleep disorder.

🔍 Scenario: Automatically identify potential sleep disorders

This scenario helps you develop an end-to-end project for your portfolio.

Background: You work for a health insurance company and are tasked to identify whether or not a potential client is likely to have a sleep disorder. The company wants to use this information to determine the premium they want the client to pay.

Objective: Construct a classifier to predict the presence of a sleep disorder based on the other columns in the dataset.

Check out our Linear Classifiers course (Python) or Supervised Learning course (R) for a quick introduction to building classifiers.

Project Scope:

Explore the data; looking for features that impact the likelihood of having a sleep disorder
Per the scenario above, create a classification model to predict if a client might have a sleep disorder.
Use pipelines and the TPOT autoML library

DataFrameas

df

variable

SELECT *
FROM 'data.csv'
LIMIT 10

import pandas as pd
import numpy as np

sleep_data = pd.read_csv('data.csv')
sleep_data.head()

sleep_data.info()

Notes: No nulls! Dtypes make sense too. Next some descriptive statistics.

sleep_data.describe(include='all')

Nothing jumping out here. Looks like pretty well behaved data. Now I will split my analysis into numeric and categorical features

person_id = sleep_data.pop('Person ID')
cat_cols = sleep_data.drop('Sleep Disorder', axis=1).select_dtypes(include='object').columns
num_cols = sleep_data.select_dtypes(include=np.number).columns

Numerical Features

import matplotlib.pyplot as plt
import seaborn as sns

for  col in num_cols:
    fig, axs = plt.subplots(2, 1, figsize=(8, 4))
    sns.boxplot(data=sleep_data[[col, 'Sleep Disorder']], x=col, ax=axs[1])
    sns.histplot(data=sleep_data[[col, 'Sleep Disorder']], x=col, hue='Sleep Disorder', kde=True, alpha=0.5, ax=axs[0])
    axs[0].set_title(col,loc='center')
    plt.show()

Notes: Several of the features split out based on Sleep Disorder. This indicates these features will be useful in predicting the sleep disorder outcome. Heart Rate appears to have some outliers.

Next let's look at the correlation of the numeric features with eachother using a heatmap.

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