book_code:《Python广告数据挖掘与分析实战》配套代码
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2021-04-11
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book_code-main chapter_10 
10.2 常用特征选择方法.ipynb 63KB 10.3 PCA主成分分析法.ipynb 57KB .ipynb_checkpoints 10.2 常用特征选择方法-checkpoint.ipynb 63KB 10.2 IV值的计算-checkpoint.ipynb 15KB 10.3 随机森林筛选特征-checkpoint.ipynb 5KB 10.3 PCA主成分分析法-checkpoint.ipynb 57KB 10.4 L1和L2综合选取特征-checkpoint.ipynb 8KB chapter_6 6.1 随机森林.ipynb 4KB 6.2 GBDT.ipynb 3KB 6.3 XGBoost.ipynb 4KB 6.4 Stacking.ipynb 10KB 6.5 LR+GBDT.ipynb 9KB .ipynb_checkpoints 6.6 FM-checkpoint.ipynb 5KB 6.2 GBDT-checkpoint.ipynb 3KB 6.3 XGBoost-checkpoint.ipynb 4KB 6.1 随机森林-checkpoint.ipynb 4KB 6.4 Stacking-checkpoint.ipynb 10KB 6.5 LR+GBDT-checkpoint.ipynb 9KB 6.6 FM.ipynb 5KB chapter_3 3.1 数据基础运算工具：NumPy.ipynb 48KB 3.3 数据可视化分析工具：Matplotlib.ipynb 157KB .ipynb_checkpoints 3.2 数据预处理工具：Pandas-checkpoint.ipynb 88KB 3.3 数据可视化分析工具：Matplotlib-checkpoint.ipynb 157KB 3.1 数据基础运算工具：NumPy-checkpoint.ipynb 48KB 3.2 数据预处理工具：Pandas.ipynb 88KB chapter_4 4.2 分类模型常用评价指标.ipynb 157KB .ipynb_checkpoints 4.2 分类模型常用评价指标-checkpoint.ipynb 157KB data chapter_9.4.csv 11KB
ex3.xlsx 10KB req_user.csv 374B ex2.csv 2KB
diabetes_test.txt 17KB train.csv 59KB chapter_9.2.csv 509B chapter_9.1.csv 4KB chapter_9.4(2).csv 9KB test.csv 28KB diabetes_train.txt 35KB
数据挖掘与AI算法.jpg 141KB chapter_5 5.5 神经网络.ipynb 5KB 5.3 KNN.ipynb 3KB 5.2 决策树.ipynb 49KB 5.1 逻辑回归.ipynb 63KB 5.4 SVM.ipynb 88KB .ipynb_checkpoints 5.3 KNN-checkpoint.ipynb 3KB 5.1 逻辑回归-checkpoint.ipynb 63KB 5.2 决策树-checkpoint.ipynb 49KB 5.5 神经网络-checkpoint.ipynb 5KB 5.4 SVM-checkpoint.ipynb 88KB chapter_9 9.4 Lookalike相似用户聚类分析.ipynb 6KB 9.2 广告用户曝光与点击率分析.ipynb 16KB 9.1 广告用户曝光与响应率分析.ipynb 29KB .ipynb_checkpoints 9.1 广告用户曝光与响应率分析-checkpoint.ipynb 29KB 9.4 Lookalike相似用户聚类分析-checkpoint.ipynb 6KB 9.2 广告用户曝光与点击率分析-checkpoint.ipynb 16KB 《Python广告数据与分析实战》.jpg 22KB README.md 980B资源评论
