Open In Colab

TIPS Dataset

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

tips = pd.read_csv('https://frenzy86.s3.eu-west-2.amazonaws.com/fav/tips.csv')
tips.head()

/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
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

A data frame with 244 observations on the following 8 variables.

total_bill: a numeric vector, the bill amount (dollars)

**tip:**a numeric vector, the tip amount (dollars)

sex: factor with levels Female Male, gender of the payer of the bill

smoker: factor with levels No Yes, whether the party included smokers

day: factor with levels Friday Saturday Sunday Thursday, day of the week

time: factor with levels Day Night, rough time of day

size: numeric vector, number of people in party

tips
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
... ... ... ... ... ... ... ...
239 29.03 5.92 Male No Sat Dinner 3
240 27.18 2.00 Female Yes Sat Dinner 2
241 22.67 2.00 Male Yes Sat Dinner 2
242 17.82 1.75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2

244 rows × 7 columns

# sns.set()
# tips = sns.load_dataset("tips")
# tips.head()

tips.describe()
total_bill tip size
count 244.000000 244.000000 244.000000
mean 19.785943 2.998279 2.569672
std 8.902412 1.383638 0.951100
min 3.070000 1.000000 1.000000
25% 13.347500 2.000000 2.000000
50% 17.795000 2.900000 2.000000
75% 24.127500 3.562500 3.000000
max 50.810000 10.000000 6.000000 
tips.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   total_bill  244 non-null    float64
 1   tip         244 non-null    float64
 2   sex         244 non-null    object 
 3   smoker      244 non-null    object 
 4   day         244 non-null    object 
 5   time        244 non-null    object 
 6   size        244 non-null    int64  
dtypes: float64(2), int64(1), object(4)
memory usage: 13.5+ KB

sns.countplot(x='smoker',data=tips);
../../_images/tips_dataset_8_0.png 
sns.countplot(x='sex',data=tips);
../../_images/tips_dataset_9_0.png 
sns.countplot(x='day',data=tips);
../../_images/tips_dataset_10_0.png 
plt.figure(figsize=(19,8))
sns.countplot(x='time',data=tips);
../../_images/tips_dataset_11_0.png 
sns.countplot(y='time',data=tips);
../../_images/tips_dataset_12_0.png 
sns.pairplot(tips,height=4,corner=True)

<seaborn.axisgrid.PairGrid at 0x7fb5dc8b27f0>
../../_images/tips_dataset_14_1.png 
## Categorical = 'sex', 'smoker' , 'day', 'time' , 'size'

sns.pairplot(tips,height=4,hue='sex');
../../_images/tips_dataset_16_0.png 
sns.pairplot(tips,height=4,hue='smoker');
../../_images/tips_dataset_17_0.png 
sns.pairplot(tips,height=4,hue='day');
../../_images/tips_dataset_18_0.png 
sns.pairplot(tips,height=4,hue='time');
../../_images/tips_dataset_19_0.png 
sns.pairplot(tips,height=4,hue='size');
../../_images/tips_dataset_20_0.png 

sns.relplot(x="total_bill", y="tip", hue="day",
                col="time", row="sex", data=tips);
../../_images/tips_dataset_21_0.png 
sns.relplot(x="total_bill", y="tip", col="time",hue="smoker", size="size",
            data=tips);
../../_images/tips_dataset_22_0.png 
sns.relplot(x="total_bill", y="tip", hue="time",col="day", data=tips)

<seaborn.axisgrid.FacetGrid at 0x7fb5d61d31d0>
../../_images/tips_dataset_23_1.png 
sns.relplot(x="total_bill", y="tip", hue="time",col="day", col_wrap=2, data=tips)

<seaborn.axisgrid.FacetGrid at 0x7fb5d5fec898>
../../_images/tips_dataset_24_1.png 
sns.catplot(x="day", y="total_bill", hue="smoker",kind="swarm", data=tips,
            height=8, aspect=1);
../../_images/tips_dataset_25_0.png 
sns.catplot(x="day", y="total_bill", hue="smoker",kind="violin", split=True, data=tips,  
            height=8,aspect=1);
../../_images/tips_dataset_26_0.png 
plt.figure(figsize=(19,8))
sns.scatterplot(x="total_bill", y="tip", data=tips);
../../_images/tips_dataset_27_0.png 
plt.figure(figsize=(19,8))
sns.distplot(tips['tip']);
../../_images/tips_dataset_28_0.png 
plt.figure(figsize=(19,8))
sns.barplot(x="day", y="tip", data = tips);
../../_images/tips_dataset_29_0.png 
fig,ax =plt.subplots(1, 2, sharey=True, figsize=(16, 10))

sns.boxplot(x="day", y="tip", data=tips, ax=ax[0])
sns.scatterplot(x="total_bill", y="tip", hue="day", data=tips, ax=ax[1]);
../../_images/tips_dataset_30_0.png 
plt.figure(figsize=(19,8))
sns.regplot(x="total_bill", y="tip", data=tips);
../../_images/tips_dataset_31_0.png 
sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg',height=16)

<seaborn.axisgrid.JointGrid at 0x7fb5d6632240>
../../_images/tips_dataset_32_1.png 
tips.corr()
total_bill tip size
total_bill 1.000000 0.675734 0.598315
tip 0.675734 1.000000 0.489299
size 0.598315 0.489299 1.000000 
plt.subplots(figsize=(15,12)) 
sns.heatmap(tips.corr(), annot = True);#, annot_kws={'size': 12});
../../_images/tips_dataset_34_0.png 
sns.pairplot(data=tips,kind="reg", size=4);

/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:2071: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)
../../_images/tips_dataset_35_1.png 
sns.pairplot(data=tips,kind="reg", size=4, hue="sex");

/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:2071: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)
../../_images/tips_dataset_36_1.png 
sns.pairplot(data=tips,kind="reg", size=4, hue="smoker")

/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:2071: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)

<seaborn.axisgrid.PairGrid at 0x7f31f87d08d0>
../../_images/tips_dataset_37_2.png 
from sklearn.linear_model import LinearRegression

df = tips.copy()
df.head()
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 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   total_bill  244 non-null    float64
 1   tip         244 non-null    float64
 2   sex         244 non-null    object 
 3   smoker      244 non-null    object 
 4   day         244 non-null    object 
 5   time        244 non-null    object 
 6   size        244 non-null    int64  
dtypes: float64(2), int64(1), object(4)
memory usage: 13.5+ KB

X = df[['total_bill','size']]
y = df['tip']

X
total_bill size
0 16.99 2
1 10.34 3
2 21.01 3
3 23.68 2
4 24.59 4
... ... ...
239 29.03 3
240 27.18 2
241 22.67 2
242 17.82 2
243 18.78 2

244 rows × 2 columns

model = LinearRegression(fit_intercept=True)
model

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

#X = X.values.reshape(-1,1)

X.ndim

2

X.shape

(244, 2)

y.shape

(244,)

model.fit(X,y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

model.coef_

array([0.09271334, 0.19259779])

model.intercept_

0.6689447408125031

model.predict([[220,3]])

array([21.64367223])
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