GitHub - JT-Kim89/06.python_ProDS_Advanced: [ProDS] Python을 활용한 데이터 분석 실습(중급) / 멀티캠퍼스(온라인) / 2024.01.01~2024.01.31

1차시: 표본 추출

2차시: 데이터 전처리: 사용자 정의 함수 활용

3차시: 모평균 비교에 관한 가설검정: Two way ANOVA

4차시: 시계열 분석

5차시: 계층적 군집분석(Hierarchical Clustering)

6차시: 비계층적 군집분석(K-means Clustering)

7차시: 분류: 나이브 베이즈(Naive Bayes)

8차시: KNN(K-Nearest Neighbor)

9차시: 추천: 연관성 분석(Association Rule)

10차시: 주성분 분석(PCA)

11차시: 실전 종합 문제1

12차시: 실전 종합 문제2

13차시: 실전 종합 문제3

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Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
README.md		README.md
ch01 pandas df.sample, sklearn.model_selection train_test_split.ipynb		ch01 pandas df.sample, sklearn.model_selection train_test_split.ipynb
ch02 def.ipynb		ch02 def.ipynb
ch03 statsmodels.formula.api ols statsmodels.stats.anova anova_lm.ipynb		ch03 statsmodels.formula.api ols statsmodels.stats.anova anova_lm.ipynb
ch04 pandas df.rolling statsmodels.tsa.seasonal seasonal_decompose.ipynb		ch04 pandas df.rolling statsmodels.tsa.seasonal seasonal_decompose.ipynb
ch05 sklearn AgglomerativeClustering scipy.cluster.hierarchy dendrogram, linkage.ipynb		ch05 sklearn AgglomerativeClustering scipy.cluster.hierarchy dendrogram, linkage.ipynb
ch06 sklearn.cluster KMeans, sklearn.preprocessing MinMaxScaler.ipynb		ch06 sklearn.cluster KMeans, sklearn.preprocessing MinMaxScaler.ipynb
ch07 sklearn.naive_bayes GaussianNB.ipynb		ch07 sklearn.naive_bayes GaussianNB.ipynb
ch08 sklearn.neighbors KNeighborsClassifier, KNeighborsRegressor.ipynb		ch08 sklearn.neighbors KNeighborsClassifier, KNeighborsRegressor.ipynb
ch09 mlxtend.frequent_patterns apriori, association_rules.ipynb		ch09 mlxtend.frequent_patterns apriori, association_rules.ipynb
ch10 sklearn.decomposition PCA().ipynb		ch10 sklearn.decomposition PCA().ipynb
ch11 test01.ipynb		ch11 test01.ipynb
ch12 test02.ipynb		ch12 test02.ipynb
ch13 test03.ipynb		ch13 test03.ipynb