import seaborn as sns

# Load the titanic dataset
tips_data = sns.load_dataset('tips')




tips_data.head(15)
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
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
5 25.29 4.71 Male No Sun Dinner 4
6 8.77 2.00 Male No Sun Dinner 2
7 26.88 3.12 Male No Sun Dinner 4
8 15.04 1.96 Male No Sun Dinner 2
9 14.78 3.23 Male No Sun Dinner 2
10 10.27 1.71 Male No Sun Dinner 2
11 35.26 5.00 Female No Sun Dinner 4
12 15.42 1.57 Male No Sun Dinner 2
13 18.43 3.00 Male No Sun Dinner 4
14 14.83 3.02 Female No Sun Dinner 2

Anaylzing the data set

  • This data set analyzes the amount of tip and includes different variables such as the gender, smoker, day and time, and it also includes the size
  • The tip varies with total bill however the other factors may have an impact
df = tips_data

df['sex'] = df['sex'].apply(lambda x: 1 if x == 'Male' else 0)
# 0 is female and 1 is male
df['smoker'] = df['smoker'].apply(lambda x: 1 if x == 'Yes' else 0)
# smoker is 1 non smoker is 0
df.head()
total_bill tip sex smoker day time size
0 16.99 1.01 0 0 Sun Dinner 2
1 10.34 1.66 1 0 Sun Dinner 3
2 21.01 3.50 1 0 Sun Dinner 3
3 23.68 3.31 1 0 Sun Dinner 2
4 24.59 3.61 0 0 Sun Dinner 4 
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
enc = OneHotEncoder(handle_unknown='ignore')
enc.fit(df[['day']])
onehot = enc.transform(df[['day']]).toarray()
cols = ['day_' + val for val in enc.categories_[0]]
df[cols] = pd.DataFrame(onehot)
df.drop(['day'], axis=1, inplace=True)
df.dropna(inplace=True)

df.head()
total_bill tip sex smoker time size day_Fri day_Sat day_Sun day_Thur
0 16.99 1.01 0 0 Dinner 2 0.0 0.0 1.0 0.0
1 10.34 1.66 1 0 Dinner 3 0.0 0.0 1.0 0.0
2 21.01 3.50 1 0 Dinner 3 0.0 0.0 1.0 0.0
3 23.68 3.31 1 0 Dinner 2 0.0 0.0 1.0 0.0
4 24.59 3.61 0 0 Dinner 4 0.0 0.0 1.0 0.0 
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
enc = OneHotEncoder(handle_unknown='ignore')
enc.fit(df[['time']])
onehot = enc.transform(df[['time']]).toarray()
cols = ['time_' + val for val in enc.categories_[0]]
df[cols] = pd.DataFrame(onehot)
df.drop(['time'], axis=1, inplace=True)
df.dropna(inplace=True)

df.head()
total_bill tip sex smoker size day_Fri day_Sat day_Sun day_Thur time_Dinner time_Lunch
0 16.99 1.01 0 0 2 0.0 0.0 1.0 0.0 1.0 0.0
1 10.34 1.66 1 0 3 0.0 0.0 1.0 0.0 1.0 0.0
2 21.01 3.50 1 0 3 0.0 0.0 1.0 0.0 1.0 0.0
3 23.68 3.31 1 0 2 0.0 0.0 1.0 0.0 1.0 0.0
4 24.59 3.61 0 0 4 0.0 0.0 1.0 0.0 1.0 0.0 
print(df.query("tip > 0").mean())

total_bill     19.785943
tip             2.998279
size            2.569672
day_Fri         0.077869
day_Sat         0.356557
day_Sun         0.311475
day_Thur        0.254098
time_Dinner     0.721311
time_Lunch      0.278689
dtype: float64

/tmp/ipykernel_12209/703362537.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  print(df.query("tip > 0").mean())

print("max for male")
print()
print(df.query("sex == 1").max())
print()
print("min for male")
print()
print(df.query("sex == 1").min())

max for male

total_bill     50.81
tip            10.00
size            6.00
day_Fri         1.00
day_Sat         1.00
day_Sun         1.00
day_Thur        1.00
time_Dinner     1.00
time_Lunch      1.00
dtype: float64

min for male

total_bill     7.25
tip            1.00
size           1.00
day_Fri        0.00
day_Sat        0.00
day_Sun        0.00
day_Thur       0.00
time_Dinner    0.00
time_Lunch     0.00
dtype: float64

/tmp/ipykernel_12209/1846147331.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  print(df.query("sex == 1").max())
/tmp/ipykernel_12209/1846147331.py:7: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  print(df.query("sex == 1").min())

df.head()
df['tip_ratio'] = df['tip'] / df['total_bill']
df.head()

df.drop(['total_bill'], axis=1, inplace=True)

df.head()
tip sex smoker size day_Fri day_Sat day_Sun day_Thur time_Dinner time_Lunch tip_ratio
0 1.01 0 0 2 0.0 0.0 1.0 0.0 1.0 0.0 0.059447
1 1.66 1 0 3 0.0 0.0 1.0 0.0 1.0 0.0 0.160542
2 3.50 1 0 3 0.0 0.0 1.0 0.0 1.0 0.0 0.166587
3 3.31 1 0 2 0.0 0.0 1.0 0.0 1.0 0.0 0.139780
4 3.61 0 0 4 0.0 0.0 1.0 0.0 1.0 0.0 0.146808 
df.drop(['tip'], axis=1, inplace=True)

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score


data = df


X = data[['size', 'sex', 'smoker', 'day_Fri', 'day_Sat', 'day_Sun', 'day_Thur', 'time_Dinner', 'time_Lunch']]
y = data['tip_ratio']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)

model = LinearRegression()


model.fit(X_train, y_train)
y_pred = model.predict(X_test)


mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("mse", mse)
print("mae", mae)
print("r2", r2)

mse 0.005770406726776936
mae 0.038372554793093525
r2 -0.00193944292273307

mean_size = df['size'].mean()

# Print the mean size
print(mean_size)

2.569672131147541

mean_gender = df['tip_ratio'].mean()

# Print the mean size
print("mean_gender", mean_gender)

mean_gender 0.16080258172250478