데이터 과학 기반의 파이썬 빅데이터 분석 Chapter10 회귀 분석

01 [선형 회귀 분석 + 산점도/선형 회귀 그래프]

환경에 따른 주택 가격 예측하기

!pip install sklearn
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting sklearn
  Downloading sklearn-0.0.post1.tar.gz (3.6 kB)
  Preparing metadata (setup.py) ... done
Building wheels for collected packages: sklearn
  Building wheel for sklearn (setup.py) ... done
  Created wheel for sklearn: filename=sklearn-0.0.post1-py3-none-any.whl size=2344 sha256=e24eb5c956a7392f04815bf5c56a58bed831c50349cf4ae9d09bafe39fab3095
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Installing collected packages: sklearn
Successfully installed sklearn-0.0.post1

import numpy as np
import pandas as pd

from sklearn.datasets import load_boston
boston = load_boston()
/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.

    The Boston housing prices dataset has an ethical problem. You can refer to
    the documentation of this function for further details.

    The scikit-learn maintainers therefore strongly discourage the use of this
    dataset unless the purpose of the code is to study and educate about
    ethical issues in data science and machine learning.

    In this special case, you can fetch the dataset from the original
    source::

        import pandas as pd
        import numpy as np


        data_url = "http://lib.stat.cmu.edu/datasets/boston"
        raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
        target = raw_df.values[1::2, 2]

    Alternative datasets include the California housing dataset (i.e.
    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
    dataset. You can load the datasets as follows::

        from sklearn.datasets import fetch_california_housing
        housing = fetch_california_housing()

    for the California housing dataset and::

        from sklearn.datasets import fetch_openml
        housing = fetch_openml(name="house_prices", as_frame=True)

    for the Ames housing dataset.
    
  warnings.warn(msg, category=FutureWarning)
  
  print(boston.DESCR)
  .. _boston_dataset:

Boston house prices dataset
---------------------------

**Data Set Characteristics:**  

    :Number of Instances: 506 

    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.

    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - AGE      proportion of owner-occupied units built prior to 1940
        - DIS      weighted distances to five Boston employment centres
        - RAD      index of accessibility to radial highways
        - TAX      full-value property-tax rate per $10,000
        - PTRATIO  pupil-teacher ratio by town
        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town
        - LSTAT    % lower status of the population
        - MEDV     Median value of owner-occupied homes in $1000's

    :Missing Attribute Values: None

    :Creator: Harrison, D. and Rubinfeld, D.L.

This is a copy of UCI ML housing dataset.
https://archive.ics.uci.edu/ml/machine-learning-databases/housing/


This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
prices and the demand for clean air', J. Environ. Economics & Management,
vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics
...', Wiley, 1980.   N.B. Various transformations are used in the table on
pages 244-261 of the latter.

The Boston house-price data has been used in many machine learning papers that address regression
problems.   
     
.. topic:: References

   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

boston_df = pd.DataFrame(boston.data, columns = boston.feature_names)
boston_df.head()

boston_df['PRICE'] = boston.target
boston_df.head()

print('보스톤 주택 가격 데이터셋 크기:', boston_df.shape)
보스톤 주택 가격 데이터셋 크기: (506, 14)

boston_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 14 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   CRIM     506 non-null    float64
 1   ZN       506 non-null    float64
 2   INDUS    506 non-null    float64
 3   CHAS     506 non-null    float64
 4   NOX      506 non-null    float64
 5   RM       506 non-null    float64
 6   AGE      506 non-null    float64
 7   DIS      506 non-null    float64
 8   RAD      506 non-null    float64
 9   TAX      506 non-null    float64
 10  PTRATIO  506 non-null    float64
 11  B        506 non-null    float64
 12  LSTAT    506 non-null    float64
 13  PRICE    506 non-null    float64
dtypes: float64(14)
memory usage: 55.5 KB

분석 모델 구축, 결과 분석 및 시각화

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

#X, Y 분할하기
Y = boston_df['PRICE']
X = boston_df.drop(['PRICE'], axis = 1, inplace = False)

#훈련용 데이터와 평가용 데이터 분할하기
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 156)

#선형 회귀 분석 : 모델 생성
lr = LinearRegression()

#선형 회귀 분석 : 모델 훈련
lr.fit(X_train, Y_train)
LinearRegression()

#선형 회귀 분석 : 평가 데이터에 대한 예측 수행 -> 예측 결과 Y_predict 구하기
Y_predict = lr.predict(X_test)

mse = mean_squared_error(Y_test, Y_predict)
rmse = np.sqrt(mse)

print('MSE : {0:.3f}, RMSE : {1:.3f}'.format(mse, rmse))
print('R^2(Variance score) : {0:.3f}'.format(r2_score(Y_test, Y_predict)))
MSE : 17.297, RMSE : 4.159
R^2(Variance score) : 0.757

print('Y 절편 값:', lr.intercept_)
print('회귀 계수 값:', np.round(lr.coef_, 1))
Y 절편 값: 40.99559517216477
회귀 계수 값: [ -0.1   0.1   0.    3.  -19.8   3.4   0.   -1.7   0.4  -0.   -0.9   0.
  -0.6]
  
coef = pd.Series(data = np.round(lr.coef_, 2), index = X.columns)
coef.sort_values(ascending = False)
RM          3.35
CHAS        3.05
RAD         0.36
ZN          0.07
INDUS       0.03
AGE         0.01
B           0.01
TAX        -0.01
CRIM       -0.11
LSTAT      -0.57
PTRATIO    -0.92
DIS        -1.74
NOX       -19.80
dtype: float64

회귀 분석 결과를 산점도 + 선형 회귀 그래프로 시각화하기

import matplotlib.pyplot as plt
import seaborn as sns

fig, axs = plt.subplots(figsize = (16, 16), ncols = 3, nrows = 5)

x_features = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']

for i, feature in enumerate(x_features):
    row = int(i/3)
    col = i%3
    sns.regplot(x = feature, y = 'PRICE', data = boston_df, ax = axs[row][col])

02 [회귀 분석 + 산점도/선형 회귀 그래프]

항목에 따른 자동차 연비 예측하기

import numpy as np
import pandas as pd
data_df = pd.read_csv('auto-mpg.csv', header = 0, engine = 'python')

print('데이터셋 크기: ', data_df.shape)
data_df.head()

data_df = data_df.drop(['car_name', 'origin', 'horsepower'], axis = 1, inplace = False)
data_df.head()

print('데이터셋 크기: ', data_df.shape)
데이터셋 크기:  (398, 6)

data_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 6 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   mpg           398 non-null    float64
 1   cylinders     398 non-null    int64  
 2   displacement  398 non-null    float64
 3   weight        398 non-null    int64  
 4   acceleration  398 non-null    float64
 5   model_year    398 non-null    int64  
dtypes: float64(3), int64(3)
memory usage: 18.8 KB

분석 모델 구축, 결과 분석 및 시각화

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

#X, Y 분할하기
Y = data_df['mpg']
X = data_df.drop(['mpg'], axis = 1, inplace = False)

#훈련용 데이터와 평가용 데이터 분할하기
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 0)

#선형 회귀 분석: 모델 생성
lr = LinearRegression()

#선형 회귀 분석: 모델 훈련
lr.fit(X_train, Y_train)
LinearRegression()

#선형 회귀 분석: 평가 데이터에 대한 예측 수행 -> 예측 결과 Y_predict 구하기
Y_predict = lr.predict(X_test)

mse = mean_squared_error(Y_test, Y_predict)
rmse = np.sqrt(mse)
print('MSE : {0:.3f}, RMSE : {1:.3f}'.format(mse, rmse))
print('R^2(Variance score) : {0:.3f}'.format(r2_score(Y_test, Y_predict)))
MSE : 12.278, RMSE : 3.504
R^2(Variance score) : 0.808

print('Y 절편 값:', np.round(lr.intercept_, 2))
print('회귀 계수 값:', np.round(lr.coef_, 2))
Y 절편 값: -17.55
회귀 계수 값: [-0.14  0.01 -0.01  0.2   0.76]

coef = pd.Series(data = np.round(lr.coef_, 2), index = X.columns)
coef.sort_values(ascending = False)
model_year      0.76
acceleration    0.20
displacement    0.01
weight         -0.01
cylinders      -0.14
dtype: float64

회귀 분석 결과를 산점도 + 선형 회귀 그래프로 시각화하기

import matplotlib.pyplot as plt
import seaborn as sn

fig, axs = plt.subplots(figsize = (16, 16), ncols = 3, nrows = 2)
x_features = ['model_year', 'acceleration', 'displacement', 'weight', 'cylinders']
plot_color = ['r', 'b', 'y', 'g', 'r']
for i, feature in enumerate(x_features):
    row = int(i/3)
    col = i%3
    sns.regplot(x = feature, y = 'mpg', data = data_df, ax = axs[row][col], color = plot_color[i])

print("연비를 예측하고 싶은 차의 정보를 입력해주세요.")

cylinders_1 = int(input("cylinders : "))
displacement_1 = int(input("displacement : "))
weight_1 = int(input("weight : "))
acceleration_1 = int(input("accleration : "))
model_year_1 = int(input("model_year : "))
연비를 예측하고 싶은 차의 정보를 입력해주세요.
cylinders : 8
displacement : 350
weight : 3200
accleration : 22
model_year : 99

mpg_predict = lr.predict([[cylinders_1, displacement_1, weight_1, acceleration_1, model_year_1]])
/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
  
print("이 자동차의 예상 연비(MPG)는 %.2f입니다." %mpg_predict)
이 자동차의 예상 연비(MPG)는 41.32입니다.