About Dataset¶

The dataset contains information about a group of test subjects and their sleep patterns. Each test subject is identified by a unique "Subject ID" and their age and gender are also recorded. The "Bedtime" and "Wakeup time" features indicate when each subject goes to bed and wakes up each day, and the "Sleep duration" feature records the total amount of time each subject slept in hours. The "Sleep efficiency" feature is a measure of the proportion of time spent in bed that is actually spent asleep. The "REM sleep percentage", "Deep sleep percentage", and "Light sleep percentage" features indicate the amount of time each subject spent in each stage of sleep. The "Awakenings" feature records the number of times each subject wakes up during the night. Additionally, the dataset includes information about each subject's caffeine and alcohol consumption in the 24 hours prior to bedtime, their smoking status, and their exercise frequency.

Dataset Link - Kaggle link

Data Dictionary -¶

ID: Unique identifier for each test subject.
Age: The age of each test subject in years.
Gender: The gender of each test subject, categorized as either male or female.
Bedtime: The time at which each test subject goes to bed each night.
Wakeup time: The time at which each test subject wakes up each morning.
Sleep duration: The total amount of time each test subject sleeps in hours.
Sleep efficiency: A measure of the proportion of time spent in bed that is actually spent asleep.
REM sleep percentage: The percentage of time spent in rapid eye movement (REM) sleep.
Deep sleep percentage: The percentage of time spent in deep sleep.
Light sleep percentage: The percentage of time spent in light sleep.
Awakenings: The number of times each test subject wakes up during the night.
Caffeine consumption: The amount of caffeine consumed by each subject in the 24 hours prior to bedtime.
Alcohol consumption: The amount of alcohol consumed by each subject in the 24 hours prior to bedtime.
Smoking status: Indicates whether each subject is a smoker or non-smoker.
Exercise frequency: The frequency with which each subject engages in exercise in a week.

Installing dependency¶

👉Ignore It if already installed

1. !pip install numpy
2. !pip install pandas
3. !pip install matplotlib
4. !pip install seaborn

step -1 Data Preprocessing and Cleaning¶

Importing Required library¶

In [1]:

# perform linear operations
import numpy as np

# Data manipulation
import pandas as pd

#Data Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Remove warnings
import warnings
warnings.filterwarnings('ignore')

In [2]:

#Load the dataset
data=pd.read_csv(r"C:\Users\Lenovo\Downloads\content\Sleep Efficiency Analysis\Sleep_Efficiency.csv")

# Print top 5 rows
data.head()

Out[2]:

	ID	Age	Gender	Bedtime	Wakeup time	Sleep duration	Sleep efficiency	REM sleep percentage	Deep sleep percentage	Light sleep percentage	Awakenings	Caffeine consumption	Alcohol consumption	Smoking status	Exercise frequency
0	1	65	Female	2021-03-06 01:00:00	2021-03-06 07:00:00	6.0	0.88	18	70	12	0.0	0.0	0.0	Yes	3.0
1	2	69	Male	2021-12-05 02:00:00	2021-12-05 09:00:00	7.0	0.66	19	28	53	3.0	0.0	3.0	Yes	3.0
2	3	40	Female	2021-05-25 21:30:00	2021-05-25 05:30:00	8.0	0.89	20	70	10	1.0	0.0	0.0	No	3.0
3	4	40	Female	2021-11-03 02:30:00	2021-11-03 08:30:00	6.0	0.51	23	25	52	3.0	50.0	5.0	Yes	1.0
4	5	57	Male	2021-03-13 01:00:00	2021-03-13 09:00:00	8.0	0.76	27	55	18	3.0	0.0	3.0	No	3.0

In [3]:

# check for shape
data.shape

Out[3]:

(452, 15)

From above cell we see that the dataset is contains 452 observations and 15 columns

In [4]:

#Check info of each colummn
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 452 entries, 0 to 451
Data columns (total 15 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   ID                      452 non-null    int64  
 1   Age                     452 non-null    int64  
 2   Gender                  452 non-null    object 
 3   Bedtime                 452 non-null    object 
 4   Wakeup time             452 non-null    object 
 5   Sleep duration          452 non-null    float64
 6   Sleep efficiency        452 non-null    float64
 7   REM sleep percentage    452 non-null    int64  
 8   Deep sleep percentage   452 non-null    int64  
 9   Light sleep percentage  452 non-null    int64  
 10  Awakenings              432 non-null    float64
 11  Caffeine consumption    427 non-null    float64
 12  Alcohol consumption     438 non-null    float64
 13  Smoking status          452 non-null    object 
 14  Exercise frequency      446 non-null    float64
dtypes: float64(6), int64(5), object(4)
memory usage: 53.1+ KB

From above cell we see that there are 4 object column and 5 integer and 6 column contain float values

In [5]:

# Checking null values
data.isnull().sum()

Out[5]:

ID                         0
Age                        0
Gender                     0
Bedtime                    0
Wakeup time                0
Sleep duration             0
Sleep efficiency           0
REM sleep percentage       0
Deep sleep percentage      0
Light sleep percentage     0
Awakenings                20
Caffeine consumption      25
Alcohol consumption       14
Smoking status             0
Exercise frequency         6
dtype: int64

From above cell we see that there are some missing values in our data So we can either drop the missing values or we can fill those

Let's fill the missing values in Awakenings column

In [6]:

data.Awakenings.value_counts()

Out[6]:

1.0    154
0.0     95
3.0     63
4.0     63
2.0     57
Name: Awakenings, dtype: int64

In [7]:

awakenings_frequent_category=data.Awakenings.mode()
awakenings_frequent_category

Out[7]:

0    1.0
Name: Awakenings, dtype: float64

In [8]:

data.Awakenings.fillna(1,inplace=True)

In [9]:

data.Awakenings.isna().sum()

Out[9]:

Fill the missing value in caffeine consumption

In [10]:

data['Caffeine consumption'].value_counts()

Out[10]:

0.0      211
50.0     107
25.0      79
75.0      25
200.0      4
100.0      1
Name: Caffeine consumption, dtype: int64

In [11]:

caffeine_consumption_frequent_category=data['Caffeine consumption'].mode()
caffeine_consumption_frequent_category

Out[11]:

0    0.0
Name: Caffeine consumption, dtype: float64

In [12]:

data['Caffeine consumption'].fillna(0,inplace=True)

In [13]:

data['Caffeine consumption'].isnull().sum()

Out[13]:

Fill the missing values in alcohol consumption columns

In [14]:

data['Alcohol consumption'].isnull().sum()

Out[14]:

In [15]:

data['Alcohol consumption'].value_counts()

Out[15]:

0.0    246
1.0     54
3.0     48
2.0     37
5.0     30
4.0     23
Name: Alcohol consumption, dtype: int64

In [16]:

alcohol_consumption_frequent_category=data['Alcohol consumption'].mode()
alcohol_consumption_frequent_category

Out[16]:

0    0.0
Name: Alcohol consumption, dtype: float64

In [17]:

data['Alcohol consumption'].fillna(0,inplace=True)

In [18]:

data['Alcohol consumption'].isnull().sum()

Out[18]:

Fill the missing values in Exercise frequency column

In [19]:

data['Exercise frequency'].isnull().sum()

Out[19]:

In [20]:

data['Exercise frequency'].value_counts()

Out[20]:

3.0    130
0.0    116
1.0     97
2.0     54
4.0     41
5.0      8
Name: Exercise frequency, dtype: int64

In [21]:

exercise_frequency_frequent_category=data['Exercise frequency'].mode()
exercise_frequency_frequent_category

Out[21]:

0    3.0
Name: Exercise frequency, dtype: float64

In [22]:

data['Exercise frequency'].fillna(3,inplace=True)

In [23]:

data['Exercise frequency'].isnull().sum()

Out[23]:

In [24]:

# check for duplicate
data.duplicated().sum()

Out[24]:

From above cell we see that there are no duplicates present in our dataset

In [25]:

data

Out[25]:

	ID	Age	Gender	Bedtime	Wakeup time	Sleep duration	Sleep efficiency	REM sleep percentage	Deep sleep percentage	Light sleep percentage	Awakenings	Caffeine consumption	Alcohol consumption	Smoking status	Exercise frequency
0	1	65	Female	2021-03-06 01:00:00	2021-03-06 07:00:00	6.0	0.88	18	70	12	0.0	0.0	0.0	Yes	3.0
1	2	69	Male	2021-12-05 02:00:00	2021-12-05 09:00:00	7.0	0.66	19	28	53	3.0	0.0	3.0	Yes	3.0
2	3	40	Female	2021-05-25 21:30:00	2021-05-25 05:30:00	8.0	0.89	20	70	10	1.0	0.0	0.0	No	3.0
3	4	40	Female	2021-11-03 02:30:00	2021-11-03 08:30:00	6.0	0.51	23	25	52	3.0	50.0	5.0	Yes	1.0
4	5	57	Male	2021-03-13 01:00:00	2021-03-13 09:00:00	8.0	0.76	27	55	18	3.0	0.0	3.0	No	3.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
447	448	27	Female	2021-11-13 22:00:00	2021-11-13 05:30:00	7.5	0.91	22	57	21	0.0	0.0	0.0	No	5.0
448	449	52	Male	2021-03-31 21:00:00	2021-03-31 03:00:00	6.0	0.74	28	57	15	4.0	25.0	0.0	No	3.0
449	450	40	Female	2021-09-07 23:00:00	2021-09-07 07:30:00	8.5	0.55	20	32	48	1.0	0.0	3.0	Yes	0.0
450	451	45	Male	2021-07-29 21:00:00	2021-07-29 04:00:00	7.0	0.76	18	72	10	3.0	0.0	0.0	No	3.0
451	452	18	Male	2021-03-17 02:30:00	2021-03-17 10:00:00	7.5	0.63	22	23	55	1.0	50.0	0.0	No	1.0

452 rows × 15 columns

Convert the Bedtime and Wakeup time column to datetime format

In [32]:

data['Bedtime']=pd.to_datetime(data['Bedtime'])
data['Wakeup time']=pd.to_datetime(data['Wakeup time'])

In [33]:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 452 entries, 0 to 451
Data columns (total 15 columns):
 #   Column                  Non-Null Count  Dtype         
---  ------                  --------------  -----         
 0   ID                      452 non-null    int64         
 1   Age                     452 non-null    int64         
 2   Gender                  452 non-null    object        
 3   Bedtime                 452 non-null    datetime64[ns]
 4   Wakeup time             452 non-null    datetime64[ns]
 5   Sleep duration          452 non-null    float64       
 6   Sleep efficiency        452 non-null    float64       
 7   REM sleep percentage    452 non-null    int64         
 8   Deep sleep percentage   452 non-null    int64         
 9   Light sleep percentage  452 non-null    int64         
 10  Awakenings              452 non-null    float64       
 11  Caffeine consumption    452 non-null    float64       
 12  Alcohol consumption     452 non-null    float64       
 13  Smoking status          452 non-null    object        
 14  Exercise frequency      452 non-null    float64       
dtypes: datetime64[ns](2), float64(6), int64(5), object(2)
memory usage: 53.1+ KB

Step 2- Data Analysis¶

In [34]:

data

Out[34]:

	ID	Age	Gender	Bedtime	Wakeup time	Sleep duration	Sleep efficiency	REM sleep percentage	Deep sleep percentage	Light sleep percentage	Awakenings	Caffeine consumption	Alcohol consumption	Smoking status	Exercise frequency
0	1	65	Female	2021-03-06 01:00:00	2021-03-06 07:00:00	6.0	0.88	18	70	12	0.0	0.0	0.0	Yes	3.0
1	2	69	Male	2021-12-05 02:00:00	2021-12-05 09:00:00	7.0	0.66	19	28	53	3.0	0.0	3.0	Yes	3.0
2	3	40	Female	2021-05-25 21:30:00	2021-05-25 05:30:00	8.0	0.89	20	70	10	1.0	0.0	0.0	No	3.0
3	4	40	Female	2021-11-03 02:30:00	2021-11-03 08:30:00	6.0	0.51	23	25	52	3.0	50.0	5.0	Yes	1.0
4	5	57	Male	2021-03-13 01:00:00	2021-03-13 09:00:00	8.0	0.76	27	55	18	3.0	0.0	3.0	No	3.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
447	448	27	Female	2021-11-13 22:00:00	2021-11-13 05:30:00	7.5	0.91	22	57	21	0.0	0.0	0.0	No	5.0
448	449	52	Male	2021-03-31 21:00:00	2021-03-31 03:00:00	6.0	0.74	28	57	15	4.0	25.0	0.0	No	3.0
449	450	40	Female	2021-09-07 23:00:00	2021-09-07 07:30:00	8.5	0.55	20	32	48	1.0	0.0	3.0	Yes	0.0
450	451	45	Male	2021-07-29 21:00:00	2021-07-29 04:00:00	7.0	0.76	18	72	10	3.0	0.0	0.0	No	3.0
451	452	18	Male	2021-03-17 02:30:00	2021-03-17 10:00:00	7.5	0.63	22	23	55	1.0	50.0	0.0	No	1.0

452 rows × 15 columns

How does sleep duration vary between different age groups and genders?

In [41]:

def agegroup(x):
    if x > 0 and x <= 12:
        return 'Kid'
    elif x > 12 and x <= 18:
        return 'Teenager'
    elif x > 18 and x <= 30:
        return 'Young Adult'
    elif x > 30 and x <= 40:
        return 'Adult'
    elif x > 40 and x <= 60:
        return 'Middle'
    else:
        return 'Senior'

data['Agegroup'] = data['Age'].apply(agegroup)

In [43]:

plt.figure(figsize=(10, 6))
sns.boxplot(x="Agegroup", y="Sleep duration", hue="Gender", data=data)
plt.title('Sleep Duration Variation by Age Group and Gender')
plt.xlabel('Age Group')
plt.ylabel('Sleep Duration (hours)')
plt.show()

No description has been provided for this image

Sleep Duration Across Different Age Groups and Genders:

The data reveals insightful trends about sleep duration among various age groups and genders.

Senior Citizens: On average, both males and females in this age group sleep for approximately 7.5 hours. Notably, there appears to be one male individual who sleeps for only 5 hours.
Adults: The average sleep duration for adults is also approximately 7.5 hours, with most individuals experiencing a sleep duration between 6 and 9 hours. One female adult also demonstrates a sleep duration of only 5 hours.
Middle-Aged Adults: Similar to other age groups, the average sleep duration for females falls within the range of 6 to 9 hours and males falls within the range of 5.5 to 9 hours .
Young Adults: Sleep duration for this group also shows a range of 6 to 9 hours, with a comparable pattern between male and female subjects.
Kids: The data indicates that children generally sleep for 7 to 9 hours, with the average sleep duration for kids estimated to be 8.7 hours.
Teenagers: Similarly, teenagers exhibit sleep durations in the range of 7 to 9 hours, with an average sleep duration of 8.7 hours as well for female.

Is there a relationship between sleep efficiency and the amount of REM sleep experienced by the test subjects?

In [49]:

plt.figure(figsize=(8, 6))
plt.scatter(data['Sleep efficiency'], data['REM sleep percentage'], alpha=0.5)
plt.title('Relationship between Sleep Efficiency and REM Sleep')
plt.xlabel('Sleep Efficiency')
plt.ylabel('REM Sleep Percentage')


plt.show()

In [50]:

# Calculate the correlation coefficient
correlation_coef = np.corrcoef(data['Sleep efficiency'],data['REM sleep percentage'] )[0, 1]
print("Correlation Coefficient:", correlation_coef)

Correlation Coefficient: 0.062362454433546856

The correlation coefficient of 0.062 suggests a weak positive correlation between sleep efficiency and the percentage of REM sleep experienced by the test subjects. While there is a positive association between the two variables, it is relatively weak, indicating that changes in one variable do not consistently predict proportional changes in the other.

What is the average bedtime and wakeup time for different age groups and genders?

In [66]:

data['bedtime_hours']=data['Bedtime'].dt.hour
data
def change_bedtime(x):
    if x==0:
        return 12
    elif x<12:
        return x
    elif x>12:
        return x-12
data['bedtime_hours']=data['bedtime_hours'].apply(change_bedtime)

In [67]:

data['bedtime_hours'].value_counts()

Out[67]:

12    110
10     83
9      73
1      67
2      64
11     55
Name: bedtime_hours, dtype: int64

In [78]:

plt.figure(figsize=(10, 6))
sns.boxplot(x="Agegroup", y="bedtime_hours", hue="Gender", data=data)
plt.title('Average bed time by Age Group and Gender')
plt.xlabel('Age Group')
plt.ylabel('Bed time hours')


plt.legend(bbox_to_anchor=(1, 1), loc='upper left')

plt.show()

In [80]:

data['wakeuptime_hours']=data['Wakeup time'].dt.hour
data

Out[80]:

	ID	Age	Gender	Bedtime	Wakeup time	Sleep duration	Sleep efficiency	REM sleep percentage	Deep sleep percentage	Light sleep percentage	Awakenings	Caffeine consumption	Alcohol consumption	Smoking status	Exercise frequency	Agegroup	bedtime_hours	wakeuptime_hours
0	1	65	Female	2021-03-06 01:00:00	2021-03-06 07:00:00	6.0	0.88	18	70	12	0.0	0.0	0.0	Yes	3.0	Senior	1	7
1	2	69	Male	2021-12-05 02:00:00	2021-12-05 09:00:00	7.0	0.66	19	28	53	3.0	0.0	3.0	Yes	3.0	Senior	2	9
2	3	40	Female	2021-05-25 21:30:00	2021-05-25 05:30:00	8.0	0.89	20	70	10	1.0	0.0	0.0	No	3.0	Adult	9	5
3	4	40	Female	2021-11-03 02:30:00	2021-11-03 08:30:00	6.0	0.51	23	25	52	3.0	50.0	5.0	Yes	1.0	Adult	2	8
4	5	57	Male	2021-03-13 01:00:00	2021-03-13 09:00:00	8.0	0.76	27	55	18	3.0	0.0	3.0	No	3.0	Middle	1	9
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
447	448	27	Female	2021-11-13 22:00:00	2021-11-13 05:30:00	7.5	0.91	22	57	21	0.0	0.0	0.0	No	5.0	Young Adult	10	5
448	449	52	Male	2021-03-31 21:00:00	2021-03-31 03:00:00	6.0	0.74	28	57	15	4.0	25.0	0.0	No	3.0	Middle	9	3
449	450	40	Female	2021-09-07 23:00:00	2021-09-07 07:30:00	8.5	0.55	20	32	48	1.0	0.0	3.0	Yes	0.0	Adult	11	7
450	451	45	Male	2021-07-29 21:00:00	2021-07-29 04:00:00	7.0	0.76	18	72	10	3.0	0.0	0.0	No	3.0	Middle	9	4
451	452	18	Male	2021-03-17 02:30:00	2021-03-17 10:00:00	7.5	0.63	22	23	55	1.0	50.0	0.0	No	1.0	Teenager	2	10

452 rows × 18 columns

In [82]:

plt.figure(figsize=(10, 6))
sns.boxplot(x="Agegroup", y="wakeuptime_hours", hue="Gender", data=data)
plt.title('Average Wake up time by Age Group and Gender')
plt.xlabel('Age Group')
plt.ylabel('Wake Up time hours')


plt.legend(bbox_to_anchor=(1, 1), loc='upper left')

plt.show()

Waking Times Across Different Age Groups and Genders:

Senior Citizens: Most individuals in this age group tend to wake up between 8 and 9 AM, suggesting a preference for later wake-up times.
Adults: Both males and females in the adult age group typically wake up between 5 AM and 8:30 AM, highlighting a moderate variation in wake-up times between genders.
Middle-Aged Adults: The wake-up times for middle-aged individuals range from 5:30 AM to 8:30 AM, indicating a broader range compared to other groups.
Young Adults: The data suggests that females in this age group wake up between 5:30 AM and 8 AM, whereas males tend to awaken between 6 AM and 8 AM, reflecting a relatively consistent pattern across genders.
Kids: Female children generally wake up between 8 AM and 10 AM, showcasing a tendency for later wake-up times compared to other groups.
Teenagers: Male teenagers typically wake up between 8 AM and 10 AM, suggesting a preference for later mornings in this group.

Do individuals with higher caffeine consumption experience more frequent awakenings during the night?

In [89]:

sns.countplot(x=data['Caffeine consumption'],hue=data['Awakenings'])
plt.title('Effect of Caffeine consumption on Awakenings during night')
plt.show()

Effect of Caffeine Consumption on Nighttime Awakenings:

The count plot reveals a surprising trend regarding the relationship between caffeine consumption and the frequency of awakenings during the night. Regardless of the amount of caffeine consumed, the majority of individuals experience a consistent number of nighttime awakenings, most frequently occurring only once. This suggests that there might not be a substantial link between caffeine intake and the frequency of nighttime disturbances. This finding could prompt further investigation into the potential factors influencing sleep quality and disturbances, outside of caffeine consumption.

How does exercise frequency impact the overall sleep quality and duration of the test subjects?

In [95]:

sns.barplot(x='Exercise frequency', y='Sleep duration', data=data)
plt.title('Exercise Frequency vs. Sleep Duration')
plt.show()

From above bar plot we see that there is no impact of exercise in a week on sleep duration of people

In [97]:

sns.barplot(x='Exercise frequency', y=data['Sleep efficiency'], data=data)
plt.title('Exercise Frequency vs. Sleep Efficiency')
plt.show()

From the above bar plot, it is evident that individuals who engage in exercise 4 or 5 times a week tend to experience more efficient sleep compared to those who engage in less frequent exercise or do not exercise at all.

Is there any significant difference in sleep patterns between smokers and non-smokers?

In [99]:

sns.barplot(x='Smoking status', y=data['Sleep efficiency'], data=data)
plt.title('Smoking status vs. Sleep Efficiency')
plt.show()

The bar plot demonstrates that individuals who do not smoke experience more efficient sleep in comparison to those who smoke.

What is the distribution of sleep stages (REM, deep, and light sleep) among different age groups and genders?

In [106]:

plt.figure(figsize=(10,5))
sns.violinplot(x='Agegroup',y='Deep sleep percentage',hue='Gender',data=data)
plt.title('Deep sleep percentage by age groups and gender')
plt.show()

The analysis of the deep sleep percentage across various age groups underscores interesting trends. Generally, the average deep sleep percentage among the different groups spans from 50 to 60, indicating relatively consistent sleep patterns among these age categories. However, the teenager age group stands out with an average deep sleep percentage ranging from 20 to 40, suggesting a distinctive sleep behavior compared to other age groups. Remarkably, the data distribution for the deep sleep percentage is quite extensive, encompassing a range from 20 to 70 across all the age groups.

In [108]:

plt.figure(figsize=(10,5))
sns.violinplot(x='Agegroup',y='Light sleep percentage',hue='Gender',data=data)
plt.title('Light sleep percentage by age groups and gender')
plt.show()

Upon examining the light sleep percentage across various age groups, it becomes apparent that the average light sleep percentage typically ranges between 10% to 20%, with a few deviations observed for the teenager and kid age groups, where the average light sleep percentage spans from 40% to 50%. Notably, the dataset displays a comprehensive spread of data for the light sleep percentage, extending from 0% to 70% across all age groups, except for the teenager male group, which exhibits a broader range from -20% to 80%

In [111]:

plt.figure(figsize=(10,5))
sns.violinplot(x='Agegroup',y='REM sleep percentage',hue='Gender',data=data)
plt.title('REM sleep percentage by age groups and gender')
plt.show()

The examination of the REM sleep percentage across various age groups reveals that the average REM sleep percentage typically ranges between 20% to 25%. Notably, the dataset displays a comprehensive spread of data for the REM sleep percentage, extending from 15% to 30% across all age groups, except for the young adult male age group, which exhibits a broader range from 0% to 30%.

Can we identify any correlations between alcohol consumption and sleep efficiency?

In [115]:

sns.barplot(x='Alcohol consumption', y='Sleep efficiency', data=data)
plt.title('Sleep Efficiency based on Alcohol Consumption')
plt.xlabel('Alcohol Consumption (in units)')
plt.ylabel('Sleep Efficiency')
plt.show()

Alcohol Consumption and Sleep Efficiency:

The bar plot highlights an interesting relationship between alcohol consumption and sleep efficiency. It appears that individuals who consume higher quantities of alcohol tend to have lower sleep efficiency. This may suggest a potential negative impact of alcohol on the quality of sleep. Further analysis or research may be required to establish a clear causal relationship.

Recommendations and Conclusions -¶

Based on the comprehensive analysis of the dataset, the following recommendations and conclusions can be drawn:

Sleep Patterns and Quality:
- Encourage a consistent sleep routine to maintain healthy sleep patterns.
- Promote awareness of the importance of sufficient sleep duration across all age groups.
- Highlight the potential impact of caffeine consumption and alcohol intake on sleep quality.
Age-Specific Sleep Recommendations:
- Develop age-specific sleep guidelines for different demographics, particularly focusing on senior citizens and teenagers.
- Foster healthy sleep habits in children and emphasize the importance of adequate sleep duration during early developmental stages.
Healthy Lifestyle Habits:
- Encourage regular physical activity and exercise to promote better sleep efficiency.
- Educate individuals about the potential adverse effects of smoking on sleep quality.
- Highlight the benefits of a balanced lifestyle, including proper nutrition and reduced alcohol consumption, for improved sleep patterns.
Further Research Areas:
- Investigate the impact of environmental factors on sleep efficiency and quality.
- Explore the potential influence of genetic and physiological factors on individual sleep patterns.
- Conduct longitudinal studies to monitor the long-term effects of sleep behavior on overall health and well-being.

By implementing these recommendations and considering the insights obtained from the dataset analysis, it is possible to promote better sleep practices and enhance overall health and quality of life for individuals of all age groups.

Practice question¶

How does the proportion of deep sleep change with age and gender?
Is there a difference in the sleep duration and efficiency between different smoking statuses and exercise frequencies?
How does the interaction between caffeine consumption and exercise frequency affect sleep efficiency and duration?
Can we identify any seasonal variations or trends in the sleep patterns of the test subjects based on the data provided?
Are there any correlations between bedtime and wake-up time and other sleep-related metrics such as sleep efficiency and total sleep duration?
How does the combination of alcohol consumption, smoking status, and exercise frequency affect sleep quality and duration among different age groups and genders?

In [ ]:

Exploratory Data Analysis on Sleep Efficiency Data in Python

About Dataset¶

Data Dictionary -¶

Installing dependency¶

step -1 Data Preprocessing and Cleaning¶

Importing Required library¶

Step 2- Data Analysis¶

Recommendations and Conclusions -¶

Practice question¶

Talk to our Industry Experts for Career Counselling

Company

Platform

Resources

Get in touch