⚫ 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

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?