⚫ Create histograms and box plots for the total bill and tip columns (Four visuals in total). What can we say about the distribution of each variable? (i.e normalized? Skewed?) [] plt.figure(figsize=(12, 6)) plt.subplot(2, 2, 1) #Code goes here ˇ Count 10 Count ЯЯЯЯ Histogram of Total Bill Box Plot of Total Bill total bill Histogram of Tip Box Plot of Tip Question 2: Count Plots Create count plots for the sex, smoker, and day columns. Interpret the results in few words: Which group is larger? [] plt.figure(figsize=(15, 5)) plt.subplot(1, 4, 1) Code goes here + Count Plot of Sex 160 140 120 count 100 9 8 Male 140 120 count • Count Plot of Smoker Count Plot of Day Count Plot of Meal 175 8 count Female Thur fr Sun 125 75 8 K я 10 Lunch time Dinner Load the tips dataset from Seaborn ⚫ The Seaborn "Tips" dataset contains information about restaurant bills, tips, and customer demographics. ⚫ Here are the column descriptions of the Tips dataset. total bill: Meal cost. tip: Tip amount. ⚫ sex: Payer gender. smoker: Smoker (yes/no). ⚫day: Day of week. ⚫time: Lunch/dinner. • size: Party size. [1] import seaborn as sns import pandas as pd import matplotlib.pyplot as plt import numpy as np tips sns. load dataset ("tips") = display(tips.head(3)) print(tips.info()) print("\nDataset Description:") print(tips.describe()) ན་ total bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 RangeIndex: 244 entries, 8 to 243 Data columns (total 7 columns): Non-Null Count Dtype " Column total bill 244 non-null float64 1 tip 244 non-null float64 2 sex 244 non-null category 3 smoker 244 non-null category 4 day 244 non-null category 5 time 244 non-null category 6 size 244 non-null int64 dtypes: category(4), float64(2), int64(1) nemory usage: 7.4 KB None Dataset Description: total_bill tip size nean std count 244.000000 244.000000 244.000000 19.785943 2.998279 2.569672 8.902412 1.383638 0.951100 min 3.070000 1.000000 1.000000 25% 13.347500 2.000000 2.000000 sex 17.795000 2.900000 2.000000 75% 24.127500 3.562500 3.000000 nax 50.810000 10.000000 6.000000
⚫ Create histograms and box plots for the total bill and tip columns (Four visuals in total). What can we say about the distribution of each variable? (i.e normalized? Skewed?) [] plt.figure(figsize=(12, 6)) plt.subplot(2, 2, 1) #Code goes here ˇ Count 10 Count ЯЯЯЯ Histogram of Total Bill Box Plot of Total Bill total bill Histogram of Tip Box Plot of Tip Question 2: Count Plots Create count plots for the sex, smoker, and day columns. Interpret the results in few words: Which group is larger? [] plt.figure(figsize=(15, 5)) plt.subplot(1, 4, 1) Code goes here + Count Plot of Sex 160 140 120 count 100 9 8 Male 140 120 count • Count Plot of Smoker Count Plot of Day Count Plot of Meal 175 8 count Female Thur fr Sun 125 75 8 K я 10 Lunch time Dinner Load the tips dataset from Seaborn ⚫ The Seaborn "Tips" dataset contains information about restaurant bills, tips, and customer demographics. ⚫ Here are the column descriptions of the Tips dataset. total bill: Meal cost. tip: Tip amount. ⚫ sex: Payer gender. smoker: Smoker (yes/no). ⚫day: Day of week. ⚫time: Lunch/dinner. • size: Party size. [1] import seaborn as sns import pandas as pd import matplotlib.pyplot as plt import numpy as np tips sns. load dataset ("tips") = display(tips.head(3)) print(tips.info()) print("\nDataset Description:") print(tips.describe()) ན་ total bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 RangeIndex: 244 entries, 8 to 243 Data columns (total 7 columns): Non-Null Count Dtype " Column total bill 244 non-null float64 1 tip 244 non-null float64 2 sex 244 non-null category 3 smoker 244 non-null category 4 day 244 non-null category 5 time 244 non-null category 6 size 244 non-null int64 dtypes: category(4), float64(2), int64(1) nemory usage: 7.4 KB None Dataset Description: total_bill tip size nean std count 244.000000 244.000000 244.000000 19.785943 2.998279 2.569672 8.902412 1.383638 0.951100 min 3.070000 1.000000 1.000000 25% 13.347500 2.000000 2.000000 sex 17.795000 2.900000 2.000000 75% 24.127500 3.562500 3.000000 nax 50.810000 10.000000 6.000000
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Question
Here's what I need help with machine learning.
1) Create histograms and box plots for the total_bill and tip columns (Four visuals in total).
What can we say about the distribution of each variable? (i.e normalized? Skewed?)
2)
Create count plots for the sex, smoker, and day columns.
Interpret the results in few words: Which group is larger?
![⚫ Create histograms and box plots for the total bill and tip columns (Four visuals in total).
What can we say about the distribution of each variable? (i.e normalized? Skewed?)
[] plt.figure(figsize=(12, 6))
plt.subplot(2, 2, 1)
#Code goes here
ˇ
Count
10
Count
ЯЯЯЯ
Histogram of Total Bill
Box Plot of Total Bill
total bill
Histogram of Tip
Box Plot of Tip
Question 2: Count Plots
Create count plots for the sex, smoker, and day columns.
Interpret the results in few words: Which group is larger?
[] plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
Code goes here
+
Count Plot of Sex
160
140
120
count
100
9
8
Male
140
120
count
•
Count Plot of Smoker
Count Plot of Day
Count Plot of Meal
175
8
count
Female
Thur
fr
Sun
125
75
8 K
я
10
Lunch
time
Dinner](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5038e9fe-a67b-4654-bcd7-3337a3eb6f7a%2F2f45f16c-a926-4844-abeb-17ebc76f2335%2Fjgwgyv_processed.jpeg&w=3840&q=75)
Transcribed Image Text:⚫ Create histograms and box plots for the total bill and tip columns (Four visuals in total).
What can we say about the distribution of each variable? (i.e normalized? Skewed?)
[] plt.figure(figsize=(12, 6))
plt.subplot(2, 2, 1)
#Code goes here
ˇ
Count
10
Count
ЯЯЯЯ
Histogram of Total Bill
Box Plot of Total Bill
total bill
Histogram of Tip
Box Plot of Tip
Question 2: Count Plots
Create count plots for the sex, smoker, and day columns.
Interpret the results in few words: Which group is larger?
[] plt.figure(figsize=(15, 5))
plt.subplot(1, 4, 1)
Code goes here
+
Count Plot of Sex
160
140
120
count
100
9
8
Male
140
120
count
•
Count Plot of Smoker
Count Plot of Day
Count Plot of Meal
175
8
count
Female
Thur
fr
Sun
125
75
8 K
я
10
Lunch
time
Dinner
![Load the tips dataset from Seaborn
⚫ The Seaborn "Tips" dataset contains information about restaurant bills, tips, and customer demographics.
⚫ Here are the column descriptions of the Tips dataset.
total bill: Meal cost.
tip: Tip amount.
⚫ sex: Payer gender.
smoker: Smoker (yes/no).
⚫day: Day of week.
⚫time: Lunch/dinner.
• size: Party size.
[1] import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
tips sns. load dataset ("tips")
=
display(tips.head(3))
print(tips.info())
print("\nDataset Description:")
print(tips.describe())
ན་
total bill tip sex smoker day time size
0
16.99 1.01 Female
No Sun Dinner
2
1
10.34 1.66 Male
No Sun Dinner
3
2
21.01 3.50 Male
No Sun Dinner
3
<class 'pandas.core.frame.DataFrame">
RangeIndex: 244 entries, 8 to 243
Data columns (total 7 columns):
Non-Null Count Dtype
"
Column
total bill 244 non-null
float64
1
tip
244 non-null
float64
2
sex
244 non-null
category
3
smoker
244 non-null
category
4
day
244 non-null
category
5
time
244 non-null
category
6 size
244 non-null
int64
dtypes: category(4), float64(2), int64(1)
nemory usage: 7.4 KB
None
Dataset Description:
total_bill
tip
size
nean
std
count 244.000000 244.000000 244.000000
19.785943 2.998279
2.569672
8.902412 1.383638 0.951100
min
3.070000
1.000000 1.000000
25%
13.347500
2.000000 2.000000
sex
17.795000
2.900000
2.000000
75%
24.127500
3.562500
3.000000
nax
50.810000 10.000000
6.000000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F5038e9fe-a67b-4654-bcd7-3337a3eb6f7a%2F2f45f16c-a926-4844-abeb-17ebc76f2335%2Fkdyfu3r_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Load the tips dataset from Seaborn
⚫ The Seaborn "Tips" dataset contains information about restaurant bills, tips, and customer demographics.
⚫ Here are the column descriptions of the Tips dataset.
total bill: Meal cost.
tip: Tip amount.
⚫ sex: Payer gender.
smoker: Smoker (yes/no).
⚫day: Day of week.
⚫time: Lunch/dinner.
• size: Party size.
[1] import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
tips sns. load dataset ("tips")
=
display(tips.head(3))
print(tips.info())
print("\nDataset Description:")
print(tips.describe())
ན་
total bill tip sex smoker day time size
0
16.99 1.01 Female
No Sun Dinner
2
1
10.34 1.66 Male
No Sun Dinner
3
2
21.01 3.50 Male
No Sun Dinner
3
<class 'pandas.core.frame.DataFrame">
RangeIndex: 244 entries, 8 to 243
Data columns (total 7 columns):
Non-Null Count Dtype
"
Column
total bill 244 non-null
float64
1
tip
244 non-null
float64
2
sex
244 non-null
category
3
smoker
244 non-null
category
4
day
244 non-null
category
5
time
244 non-null
category
6 size
244 non-null
int64
dtypes: category(4), float64(2), int64(1)
nemory usage: 7.4 KB
None
Dataset Description:
total_bill
tip
size
nean
std
count 244.000000 244.000000 244.000000
19.785943 2.998279
2.569672
8.902412 1.383638 0.951100
min
3.070000
1.000000 1.000000
25%
13.347500
2.000000 2.000000
sex
17.795000
2.900000
2.000000
75%
24.127500
3.562500
3.000000
nax
50.810000 10.000000
6.000000
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