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 Question 3: Scatter Plot Create a scatter plot to visualize the relationship between total bill and tip. • Interpret the plot: Do we see any correlation? [] #Code goes here I ㄛˇ Scatter Plot: Total Bill vs. Tip 10 6. 4 total_bill Question 4: Pair Plots • Create pair plots to visualize relationships between: (total bill, tip, size). ⚫ Interpret the plot. [] Code goes here H total bill 50 40 di 10 10 1- . 40 2 total bil bp sze

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 Question 3: Scatter Plot Create a scatter plot to visualize the relationship between total bill and tip. • Interpret the plot: Do we see any correlation? [] #Code goes here I ㄛˇ Scatter Plot: Total Bill vs. Tip 10 6. 4 total_bill Question 4: Pair Plots • Create pair plots to visualize relationships between: (total bill, tip, size). ⚫ Interpret the plot. [] Code goes here H total bill 50 40 di 10 10 1- . 40 2 total bil bp sze