book_code:《Python广告数据挖掘与分析实战》配套代码-源码

[{"title":"（ 57 个子文件 1.13MB ） book_code:《Python广告数据挖掘与分析实战》配套代码-源码","children":[{"title":"book_code-main","children":[{"title":"chapter_10","children":[{"title":"10.2 常用特征选择方法.ipynb <span style='color:#111;'> 62.75KB </span>","children":null,"spread":false},{"title":"10.3 PCA主成分分析法.ipynb <span style='color:#111;'> 56.90KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"10.2 常用特征选择方法-checkpoint.ipynb <span style='color:#111;'> 62.75KB </span>","children":null,"spread":false},{"title":"10.2 IV值的计算-checkpoint.ipynb <span style='color:#111;'> 14.81KB </span>","children":null,"spread":false},{"title":"10.3 随机森林筛选特征-checkpoint.ipynb <span style='color:#111;'> 5.19KB </span>","children":null,"spread":false},{"title":"10.3 PCA主成分分析法-checkpoint.ipynb <span style='color:#111;'> 56.90KB </span>","children":null,"spread":false},{"title":"10.4 L1和L2综合选取特征-checkpoint.ipynb <span style='color:#111;'> 7.56KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"chapter_6","children":[{"title":"6.1 随机森林.ipynb <span style='color:#111;'> 4.43KB </span>","children":null,"spread":false},{"title":"6.2 GBDT.ipynb <span style='color:#111;'> 2.99KB </span>","children":null,"spread":false},{"title":"6.3 XGBoost.ipynb <span style='color:#111;'> 4.25KB </span>","children":null,"spread":false},{"title":"6.4 Stacking.ipynb <span style='color:#111;'> 9.58KB </span>","children":null,"spread":false},{"title":"6.5 LR+GBDT.ipynb <span style='color:#111;'> 9.06KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"6.6 FM-checkpoint.ipynb <span style='color:#111;'> 5.30KB </span>","children":null,"spread":false},{"title":"6.2 GBDT-checkpoint.ipynb <span style='color:#111;'> 2.99KB </span>","children":null,"spread":false},{"title":"6.3 XGBoost-checkpoint.ipynb <span style='color:#111;'> 4.25KB </span>","children":null,"spread":false},{"title":"6.1 随机森林-checkpoint.ipynb <span style='color:#111;'> 4.43KB </span>","children":null,"spread":false},{"title":"6.4 Stacking-checkpoint.ipynb <span style='color:#111;'> 9.58KB </span>","children":null,"spread":false},{"title":"6.5 LR+GBDT-checkpoint.ipynb <span style='color:#111;'> 9.06KB </span>","children":null,"spread":false}],"spread":true},{"title":"6.6 FM.ipynb <span style='color:#111;'> 5.30KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter_3","children":[{"title":"3.1 数据基础运算工具：NumPy.ipynb <span style='color:#111;'> 47.86KB </span>","children":null,"spread":false},{"title":"3.3 数据可视化分析工具：Matplotlib.ipynb <span style='color:#111;'> 156.80KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"3.2 数据预处理工具：Pandas-checkpoint.ipynb <span style='color:#111;'> 88.45KB </span>","children":null,"spread":false},{"title":"3.3 数据可视化分析工具：Matplotlib-checkpoint.ipynb <span style='color:#111;'> 156.80KB </span>","children":null,"spread":false},{"title":"3.1 数据基础运算工具：NumPy-checkpoint.ipynb <span style='color:#111;'> 47.86KB </span>","children":null,"spread":false}],"spread":true},{"title":"3.2 数据预处理工具：Pandas.ipynb <span style='color:#111;'> 88.45KB </span>","children":null,"spread":false}],"spread":true},{"title":"chapter_4","children":[{"title":"4.2 分类模型常用评价指标.ipynb <span style='color:#111;'> 156.58KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"4.2 分类模型常用评价指标-checkpoint.ipynb <span style='color:#111;'> 156.58KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"data","children":[{"title":"chapter_9.4.csv <span style='color:#111;'> 10.70KB </span>","children":null,"spread":false},{"title":"ex3.xlsx <span style='color:#111;'> 9.67KB </span>","children":null,"spread":false},{"title":"req_user.csv <span style='color:#111;'> 374B </span>","children":null,"spread":false},{"title":"ex2.csv <span style='color:#111;'> 1.69KB </span>","children":null,"spread":false},{"title":"diabetes_test.txt <span style='color:#111;'> 17.07KB </span>","children":null,"spread":false},{"title":"train.csv <span style='color:#111;'> 58.89KB </span>","children":null,"spread":false},{"title":"chapter_9.2.csv <span style='color:#111;'> 509B </span>","children":null,"spread":false},{"title":"chapter_9.1.csv <span style='color:#111;'> 3.78KB </span>","children":null,"spread":false},{"title":"chapter_9.4(2).csv <span style='color:#111;'> 9.47KB </span>","children":null,"spread":false},{"title":"test.csv <span style='color:#111;'> 28.37KB </span>","children":null,"spread":false},{"title":"diabetes_train.txt <span style='color:#111;'> 35.17KB </span>","children":null,"spread":false}],"spread":false},{"title":"数据挖掘与AI算法.jpg <span style='color:#111;'> 140.78KB </span>","children":null,"spread":false},{"title":"chapter_5","children":[{"title":"5.5 神经网络.ipynb <span style='color:#111;'> 5.32KB </span>","children":null,"spread":false},{"title":"5.3 KNN.ipynb <span style='color:#111;'> 3.44KB </span>","children":null,"spread":false},{"title":"5.2 决策树.ipynb <span style='color:#111;'> 48.70KB </span>","children":null,"spread":false},{"title":"5.1 逻辑回归.ipynb <span style='color:#111;'> 62.89KB </span>","children":null,"spread":false},{"title":"5.4 SVM.ipynb <span style='color:#111;'> 87.67KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"5.3 KNN-checkpoint.ipynb <span style='color:#111;'> 3.44KB </span>","children":null,"spread":false},{"title":"5.1 逻辑回归-checkpoint.ipynb <span style='color:#111;'> 62.89KB </span>","children":null,"spread":false},{"title":"5.2 决策树-checkpoint.ipynb <span style='color:#111;'> 48.70KB </span>","children":null,"spread":false},{"title":"5.5 神经网络-checkpoint.ipynb <span style='color:#111;'> 5.32KB </span>","children":null,"spread":false},{"title":"5.4 SVM-checkpoint.ipynb <span style='color:#111;'> 87.67KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"chapter_9","children":[{"title":"9.4 Lookalike相似用户聚类分析.ipynb <span style='color:#111;'> 5.98KB </span>","children":null,"spread":false},{"title":"9.2 广告用户曝光与点击率分析.ipynb <span style='color:#111;'> 16.02KB </span>","children":null,"spread":false},{"title":"9.1 广告用户曝光与响应率分析.ipynb <span style='color:#111;'> 29.44KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"9.1 广告用户曝光与响应率分析-checkpoint.ipynb <span style='color:#111;'> 29.44KB </span>","children":null,"spread":false},{"title":"9.4 Lookalike相似用户聚类分析-checkpoint.ipynb <span style='color:#111;'> 5.98KB </span>","children":null,"spread":false},{"title":"9.2 广告用户曝光与点击率分析-checkpoint.ipynb <span style='color:#111;'> 16.02KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"《Python广告数据与分析实战》.jpg <span style='color:#111;'> 22.03KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 980B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]