Linear Regression을 통한 고객별 연간 지출액 예측

고객별 연간 지출액 예측¶

Linear Regression¶

고객의 연간 지출액, 서비스 사용시간, 멤버쉽 기간 등 데이터로 모델을 학습시킨 후
새로운 고객이 유입되었을 때 해당 고객의 연간 지출액을 예측한다.

모듈 및 데이터 로드¶

In [1]:

# 모듈 로드

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

In [2]:

# 데이터 로드

data = pd.read_csv('ecommerce.csv')

데이터 특성 확인¶

In [3]:

data

Out[3]:

	Email	Address	Avatar	Avg. Session Length	Time on App	Time on Website	Length of Membership	Yearly Amount Spent
0	mstephenson@fernandez.com	835 Frank Tunnel\nWrightmouth, MI 82180-9605	Violet	34.497268	12.655651	39.577668	4.082621	587.951054
1	hduke@hotmail.com	4547 Archer Common\nDiazchester, CA 06566-8576	DarkGreen	31.926272	11.109461	37.268959	2.664034	392.204933
2	pallen@yahoo.com	24645 Valerie Unions Suite 582\nCobbborough, D...	Bisque	33.000915	11.330278	37.110597	4.104543	487.547505
3	riverarebecca@gmail.com	1414 David Throughway\nPort Jason, OH 22070-1220	SaddleBrown	34.305557	13.717514	36.721283	3.120179	581.852344
4	mstephens@davidson-herman.com	14023 Rodriguez Passage\nPort Jacobville, PR 3...	MediumAquaMarine	33.330673	12.795189	37.536653	4.446308	599.406092
...	...	...	...	...	...	...	...	...
495	lewisjessica@craig-evans.com	4483 Jones Motorway Suite 872\nLake Jamiefurt,...	Tan	33.237660	13.566160	36.417985	3.746573	573.847438
496	katrina56@gmail.com	172 Owen Divide Suite 497\nWest Richard, CA 19320	PaleVioletRed	34.702529	11.695736	37.190268	3.576526	529.049004
497	dale88@hotmail.com	0787 Andrews Ranch Apt. 633\nSouth Chadburgh, ...	Cornsilk	32.646777	11.499409	38.332576	4.958264	551.620145
498	cwilson@hotmail.com	680 Jennifer Lodge Apt. 808\nBrendachester, TX...	Teal	33.322501	12.391423	36.840086	2.336485	456.469510
499	hannahwilson@davidson.com	49791 Rachel Heights Apt. 898\nEast Drewboroug...	DarkMagenta	33.715981	12.418808	35.771016	2.735160	497.778642

500 rows × 8 columns

In [4]:

data.head()

Out[4]:

	Email	Address	Avatar	Avg. Session Length	Time on App	Time on Website	Length of Membership	Yearly Amount Spent
0	mstephenson@fernandez.com	835 Frank Tunnel\nWrightmouth, MI 82180-9605	Violet	34.497268	12.655651	39.577668	4.082621	587.951054
1	hduke@hotmail.com	4547 Archer Common\nDiazchester, CA 06566-8576	DarkGreen	31.926272	11.109461	37.268959	2.664034	392.204933
2	pallen@yahoo.com	24645 Valerie Unions Suite 582\nCobbborough, D...	Bisque	33.000915	11.330278	37.110597	4.104543	487.547505
3	riverarebecca@gmail.com	1414 David Throughway\nPort Jason, OH 22070-1220	SaddleBrown	34.305557	13.717514	36.721283	3.120179	581.852344
4	mstephens@davidson-herman.com	14023 Rodriguez Passage\nPort Jacobville, PR 3...	MediumAquaMarine	33.330673	12.795189	37.536653	4.446308	599.406092

In [5]:

data.tail()

Out[5]:

	Email	Address	Avatar	Avg. Session Length	Time on App	Time on Website	Length of Membership	Yearly Amount Spent
495	lewisjessica@craig-evans.com	4483 Jones Motorway Suite 872\nLake Jamiefurt,...	Tan	33.237660	13.566160	36.417985	3.746573	573.847438
496	katrina56@gmail.com	172 Owen Divide Suite 497\nWest Richard, CA 19320	PaleVioletRed	34.702529	11.695736	37.190268	3.576526	529.049004
497	dale88@hotmail.com	0787 Andrews Ranch Apt. 633\nSouth Chadburgh, ...	Cornsilk	32.646777	11.499409	38.332576	4.958264	551.620145
498	cwilson@hotmail.com	680 Jennifer Lodge Apt. 808\nBrendachester, TX...	Teal	33.322501	12.391423	36.840086	2.336485	456.469510
499	hannahwilson@davidson.com	49791 Rachel Heights Apt. 898\nEast Drewboroug...	DarkMagenta	33.715981	12.418808	35.771016	2.735160	497.778642

In [6]:

data.sample()

Out[6]:

	Email	Address	Avatar	Avg. Session Length	Time on App	Time on Website	Length of Membership	Yearly Amount Spent
287	matthewgraves@mills-shaffer.com	66636 Jason Parkway\nKellyside, NV 67033	DarkSeaGreen	33.908565	12.914847	39.068864	1.48236	432.472061

In [7]:

data.shape

Out[7]:

(500, 8)

In [8]:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 500 entries, 0 to 499
Data columns (total 8 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Email                 500 non-null    object 
 1   Address               500 non-null    object 
 2   Avatar                500 non-null    object 
 3   Avg. Session Length   500 non-null    float64
 4   Time on App           500 non-null    float64
 5   Time on Website       500 non-null    float64
 6   Length of Membership  500 non-null    float64
 7   Yearly Amount Spent   500 non-null    float64
dtypes: float64(5), object(3)
memory usage: 31.4+ KB

In [9]:

data.describe()

Out[9]:

	Avg. Session Length	Time on App	Time on Website	Length of Membership	Yearly Amount Spent
count	500.000000	500.000000	500.000000	500.000000	500.000000
mean	33.053194	12.052488	37.060445	3.533462	499.314038
std	0.992563	0.994216	1.010489	0.999278	79.314782
min	29.532429	8.508152	33.913847	0.269901	256.670582
25%	32.341822	11.388153	36.349257	2.930450	445.038277
50%	33.082008	11.983231	37.069367	3.533975	498.887875
75%	33.711985	12.753850	37.716432	4.126502	549.313828
max	36.139662	15.126994	40.005182	6.922689	765.518462

In [10]:

sns.pairplot(data)

Out[10]:

<seaborn.axisgrid.PairGrid at 0x1a90900e850>

불필요한 컬럼 제거¶

In [11]:

# 고객의 연간 지출액 예측에 불필요한 컬럼 제거

data.drop(['Email', 'Address', 'Avatar'], axis=1, inplace=True)

Train/Test Set 분리¶

In [12]:

from sklearn.model_selection import train_test_split

In [13]:

X = data.drop('Yearly Amount Spent', axis=1)

In [14]:

y = data['Yearly Amount Spent']

In [15]:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)

Linear Regression 모델 만들기¶

In [16]:

import statsmodels.api as sm

In [17]:

model = sm.OLS(y_train, X_train).fit()

In [18]:

model.summary()

Out[18]:

OLS Regression Results

Dep. Variable:	Yearly Amount Spent	R-squared (uncentered):	0.998
Model:	OLS	Adj. R-squared (uncentered):	0.998
Method:	Least Squares	F-statistic:	4.798e+04
Date:	Sat, 29 Jan 2022	Prob (F-statistic):	0.00
Time:	12:29:00	Log-Likelihood:	-1820.0
No. Observations:	400	AIC:	3648.
Df Residuals:	396	BIC:	3664.
Df Model:	4
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
Avg. Session Length	11.9059	0.869	13.696	0.000	10.197	13.615
Time on App	34.3257	1.121	30.610	0.000	32.121	36.530
Time on Website	-14.1405	0.812	-17.405	0.000	-15.738	-12.543
Length of Membership	61.0149	1.144	53.318	0.000	58.765	63.265

Omnibus:	0.490	Durbin-Watson:	1.987
Prob(Omnibus):	0.783	Jarque-Bera (JB):	0.606
Skew:	-0.022	Prob(JB):	0.739
Kurtosis:	2.814	Cond. No.	55.4

Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

예측 및 평가¶

In [19]:

predictions = model.predict(X_test)

In [20]:

predictions

Out[20]:

69     418.211323
29     567.097473
471    534.706617
344    425.690888
54     474.931682
          ...    
460    570.877250
152    564.267305
154    557.093996
56     489.285778
392    550.720695
Length: 100, dtype: float64

In [21]:

y_test

Out[21]:

69     451.575685
29     554.722084
471    541.049831
344    442.722892
54     522.404141
          ...    
460    618.845970
152    555.892595
154    595.803819
56     520.898794
392    549.131573
Name: Yearly Amount Spent, Length: 100, dtype: float64

In [22]:

sns.scatterplot(x=y_test, y=predictions)

Out[22]:

<AxesSubplot:xlabel='Yearly Amount Spent'>

In [23]:

from sklearn import metrics

In [24]:

print('MSE:', metrics.mean_squared_error(y_test, predictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))

MSE: 482.2890139088915
RMSE: 21.961079525125616