[AI] Machine Learning 실습

Library

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

sns.set()

• numpy : The fundamental package for scientific computing with Python
  ⇒ 벡터, 행렬 등 수치 연산을 수행 / 선형대수 라이브러리

• pandas : a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
  ⇒ 데이터 분석에 사용

• seaborn : Python data visualization library based on matplotlib
  ⇒ 시각화 라이브러리

• matplotlib : a comprehensive library for creating static, animated, and interactive visualizations in Python
  ⇒ 데이터 시각화 패키지
  - matplotlib.pyplot : a state-based interface to matplotlib

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Data loading & Visualization

Boston house dataset

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html

from sklearn import datasets
boston = datasets.load_boston()
boston

scikit-learn : Machine Learning in Python
https://scikit-learn.org/stable/

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feature data 이름 확인

boston.feature_names

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boston 데이터셋 설명
The full description of the dataset

print(boston.DESCR)

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The regression target

boston.target

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Boston data to pandas data frame

df = pd.DataFrame(boston.data, columns=boston.feature_names)
df['PRICE'] = boston.target

# 상위 5행 가져오기
print(df.head())

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각 열에서 NaN 발생 수 확인하기

df.isnull().sum()

• NaN 값이 없는 걸 확인할 수 있음

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Data visualization

변수에 대한 히스토그램을 표시

sns.histplot(data=df, x="PRICE")

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히트맵

corr_matrix = df.corr().round(2)
f, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(data=corr_matrix, annot=True)

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Data split (use train_test_split)

• https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test \
= train_test_split(boston.data, boston.target, test_size=0.2, random_state=5)

print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

• train 데이터와 test 데이터를 80%, 20%로 나눔
• random_state : 수행시마다 동일한 결과를 얻기 위해

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train, test = train_test_split(df, test_size=0.2, random_state=5)

print(train.shape)
print(test.shape)

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y = df.pop('PRICE')
X = df

X_train, X_test, y_train, y_test \
= train_test_split(X, y, test_size=0.2, random_state=5)

print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

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Linear regression

from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)

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