├── .gitignore ├── 1.环境安装.md ├── 2.KMeans算法与交通事故理赔审核预测.md ├── 3.Matplotlib学习笔记.md ├── 4.NumPy学习笔记.md ├── 5.Pandas学习笔记.md ├── 6.数据预处理笔记.md ├── 7.机器学习部分.md ├── 8.SKlearn模型评估方法.md ├── 9.Kaggle杂记.md ├── README.md ├── img ├── 1.jpg ├── 10.jpg ├── 11.jpg ├── 12.jpg ├── 13.jpg ├── 2.jpg ├── 3.jpg ├── 4.jpg ├── 5.jpg ├── 6.jpg ├── 7.jpg ├── 8.jpg ├── 9.jpg ├── feature_engineering_total.png ├── modeling_total.png └── 数据科学算法.png ├── 实战篇 ├── 1.数据探索 │ ├── README.md │ ├── data │ │ ├── catering_dish_profit.xls │ │ ├── catering_sale.xls │ │ └── catering_sale_all.xls │ ├── img │ │ ├── img_1.png │ │ ├── overview.png │ │ ├── programmer_2.png │ │ └── programmer_3.png │ ├── output_23_1.png │ ├── output_25_0.png │ ├── output_27_0.png │ ├── output_29_0.png │ ├── output_41_0.png │ ├── output_9_1.png │ ├── 数据探索_part01.ipynb │ ├── 数据探索_part02.ipynb │ └── 数据探索介绍.ipynb ├── 2.数据预处理 │ ├── README.md │ ├── data │ │ ├── catering_sale.xls │ │ ├── discretization_data.xls │ │ ├── electricity_data.xls │ │ ├── leleccum.mat │ │ ├── normalization_data.xls │ │ ├── principal_component.xls │ │ └── segdata.csv │ ├── tmp │ │ └── sales.xls │ ├── 数据预处理.ipynb │ ├── 数据预处理_part02.ipynb │ ├── 数据预处理_part1.ipynb │ ├── 数据预处理介绍.ipynb │ └── 数据预处理介绍 │ │ ├── output_37_1.png │ │ ├── output_37_2.png │ │ ├── output_37_3.png │ │ └── 数据预处理介绍.md ├── 3.电力窃漏电用户自动识别 │ ├── EDA1.ipynb │ ├── MODEL1.ipynb │ ├── README.md │ └── data │ │ ├── missing_data.xls │ │ ├── missing_data_processed.xls │ │ ├── model.xls │ │ └── ═╪╒╣╦╝┐╝╤∙▒╛╩¤╛▌.xls ├── 4.地震后建筑修复建议预测 │ ├── EDA.ipynb │ ├── MODEL_EDA_NN.ipynb │ ├── README.md │ └── img │ │ ├── output_13_2.png │ │ ├── output_17_2.png │ │ ├── output_20_1.png │ │ ├── output_25_2.png │ │ ├── output_26_2.png │ │ ├── output_5_2.png │ │ └── output_73_2.png └── 5.Titanic │ └── Kaggle Titanic Best Score.ipynb └── 高级特征工程 ├── Advanced Feature Engineering I.ipynb ├── Advanced Feature Engineering II.ipynb ├── Emsembling.ipynb ├── Hyperparameter tuning.ipynb ├── Tips and tricks.ipynb └── img ├── NMF.png 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