├── .gitattributes ├── A_chapter2-Numpy数值计算基础 ├── 2_1.py ├── 2_2.py ├── 2_3.py ├── 2_4.py ├── 2_5.py ├── __init__.py ├── data │ └── iris_sepal_length.csv ├── test │ ├── 3.1.py │ └── __init__.py └── tmp │ ├── __init__.py │ ├── arr.txt │ ├── save_arr.npy │ └── savez_arr.npz ├── A_chapter3-Matplotlib数据可视化基础 ├── 3.1_pyplot.py ├── 3.2_line.py ├── 3.2_scatter.py ├── 3_2_3.py ├── 3_3.histogram.py ├── 3_3_4.py ├── 3_test_1.py ├── 3_test_2.py ├── 3_test_xiti.py ├── __init__.py ├── data │ ├── iris.npz │ ├── populations.npz │ └── 国民经济核算季度数据.npz └── tmp │ ├── 1996——2015人口关系数据特征散点图与折线图.png │ ├── 1996——2015人口关系数据特征散点图与折线图与饼图.png │ ├── 2000-2017各产业国民生产总值箱线图.png │ ├── 2000-2017年各产业季度生产总值散点图.png │ ├── 2000-2017年季度各产业生产总值折线图.png │ ├── 2000-2017年季度各产业生产总值折线散点图.png │ ├── 2000-2017年季度各行业生产总值折线子图.png │ ├── 2000-2017年季度各行业生产总值散点子图.png │ ├── 2000-2017年季度生产总值折线图.png │ ├── 2000-2017年季度生产总值散点图.png │ ├── 2000-2017年季度生产总值点线图.png │ ├── 2017年第一季度各产业国民生产总值直方图.png │ ├── 2017年第一季度各产业生产总值占比饼图.png │ ├── __init__.py │ ├── sincos.png │ ├── y=x^2.png │ ├── 修改rc参数后sin曲线.png │ ├── 国民生产总值分散情况箱线图.png │ ├── 国民生产总值构成分布直方图.png │ ├── 国民生产总值构成分布饼图.png │ ├── 无法显示中文标题sin曲线.png │ ├── 显示中文标题sin曲线.png │ ├── 鸢尾花特征散点图.png │ └── 默认sin曲线.png ├── A_chapter4-Pandas统计分析基础 ├── 4.1.1_mysql.py ├── 4.1.2_read_csv.py ├── 4.1.4_read_sql.py ├── 4.2.1-dataframe.py ├── 4.2.4_test_pd.py ├── 4.3.1_test_pd2.py ├── 4.4.1_groupby.py ├── 4.5.1_pivot_table.py ├── 4.5.2__crosstab.py ├── 4_test_1.py ├── 4_test_2.py ├── 4_test_3.py ├── 4_test_4.py ├── 4_xiti.py ├── __init__.py ├── data │ ├── meal_order_detail.xlsx │ ├── meal_order_detail1.sql │ ├── meal_order_detail2.sql │ ├── meal_order_detail3.sql │ ├── meal_order_info.csv │ ├── test_data │ │ ├── Training_LogInfo.csv │ │ ├── Training_Master.csv │ │ ├── Training_Userupdate.csv │ │ ├── mtcars.csv │ │ └── 数据特征说明.xlsx │ ├── users.xlsx │ └── 数据特征说明.xlsx └── tmp │ ├── orderInfo.csv │ └── userInfo.xlsx ├── A_chapter5-使用Pandas进行数据预处理 ├── 5.1.py ├── 5.2_clean_data.py ├── 5.3_standard_data.py ├── 5.4_transform_data.py ├── 5_test_1.py ├── 5_test_2.py ├── 5_test_3.py ├── 5_xiti.py ├── __init__.py ├── data │ ├── detail.csv │ ├── meal_order_detail1.sql │ ├── meal_order_detail2.sql │ ├── meal_order_detail3.sql │ ├── meal_order_info.csv │ ├── test │ │ ├── alarm.csv │ │ ├── ele_loss.csv │ │ ├── missing_data.csv │ │ ├── model.csv │ │ ├── new_missing_data.csv │ │ ├── tmpsales.csv │ │ └── 数据特征说明.xlsx │ ├── users_info.xlsx │ └── 数据特征说明.xlsx └── tmp │ └── 菜品异常数据识别.png ├── A_chapter6-使用Scikit-Learn构建模型 ├── 6.1_datasets.py ├── 6.3_sklearn_SVM.py ├── 6_2_K-Means.py ├── 6_4_sklearn_Linear_Regresion.py ├── 6_test_1.py ├── 6_xiti.py ├── __init__.py ├── data │ ├── abalone.data │ ├── cal_housing.data │ ├── seeds_dataset.txt │ └── test │ │ ├── wine.csv │ │ ├── winequality.csv │ │ └── 数据特征说明.xlsx └── tmp │ ├── test3_wine_FMI评价折线图.png │ ├── wine_quality_target线性回归预测.png │ └── 聚类结果.png ├── A_chapter7-航空公司客户价值分析 ├── 7_1_LRFMC.py ├── 7_3_kmeans_LRFMC.py ├── 7_test.py ├── 7_xiti.py ├── __init__.py ├── data │ ├── air_data.csv │ └── test │ │ ├── credit_card.csv │ │ └── data.csv └── tmp │ └── airline_scale.npz ├── A_chapter8-财政收入预测分析（Lasso+svm.LinearSVR） ├── __init__.py ├── code │ ├── 8.1_Lasso.py │ ├── 8_4_LinearSVR.py │ ├── 8_test_1.1.py │ ├── 8_test_1.py │ ├── GM11.py │ └── __pycache__ │ │ └── GM11.cpython-37.pyc ├── data │ ├── data.csv │ └── test │ │ ├── income_tax.csv │ │ ├── income_tax_Lasso.csv │ │ ├── income_tax_test.xls │ │ └── income_tax_test_LinearSVR.xls └── tmp │ ├── new_reg_data.csv │ ├── new_reg_data_GM11.xls │ └── new_reg_data_GM11_revenue.xls ├── A_chapter9-家用热水器用户行为分析与事件识别（BP神经网络MLPClassifier） ├── __init__.py ├── code │ └── 9.1.py ├── data │ ├── original_data.xls │ ├── test_data.xlsx │ ├── water_hearter.xlsx │ ├── water_heater.xls │ └── water_heater_log.xlsx └── tmp │ ├── sj.csv │ └── water_heart.csv ├── B_chapter06-电力盗窃电用户自动识别（CART+LM） ├── .ipynb_checkpoints │ ├── 1_dataExplore-checkpoint.ipynb │ ├── 2_1fullMissing-checkpoint.ipynb │ ├── 3_1buildModel-checkpoint.ipynb │ └── 3_2_10-fold cross-validation-checkpoint.ipynb ├── 1_dataExplore.ipynb ├── 1_dataExplore.py ├── 2.jpg ├── 2_1fullMissing.ipynb ├── 2_1fullMissing.py ├── 3_1buildModel.ipynb ├── 3_1buildModel.py ├── 3_2_10-fold cross-validation.ipynb ├── 3_2_10-fold_cross-validation.py ├── LOC.jpg ├── README.md ├── Series.csv ├── Serij.csv ├── cm_plot.py ├── compare.csv ├── confusionMatrix.jpg ├── confusionMatrix1.jpg ├── lagij.csv ├── lagrange.csv ├── missing_data.xls ├── model.xls ├── net.model ├── newij.csv ├── newton.csv ├── nls.csv ├── pic.xlsx ├── tree.pkl └── usertypes.jpg ├── B_chapter07-航空公司客户价值分析（kmeans） ├── 1_1data_explore.ipynb ├── 2_1data_preprocess.ipynb ├── 3_1buildModel.ipynb ├── README.md ├── air_data.csv ├── radar1.py └── radar1.pyc ├── B_chapter08-中医证型关联规则挖掘（Apriori关联规则） ├── .ipynb_checkpoints │ ├── 1_1dataPreprocess-checkpoint.ipynb │ └── 2_1buildModel-checkpoint.ipynb ├── 1_1dataPreprocess.ipynb ├── 1_1dataPreprocess.py ├── 2_1buildModel.ipynb ├── 2_1buildModel.py ├── README.md ├── apriori.txt ├── apriori.xlsx ├── data.xls ├── data_processed.xlsx ├── selfapriori.py └── selfapriori.pyc ├── B_chapter09-基于水色图像的水质评价（SVM） ├── .ipynb_checkpoints │ ├── 1_2Alldata-checkpoint.ipynb │ ├── 2_1buildModel-checkpoint.ipynb │ ├── Untitled-checkpoint.ipynb │ └── svm_waterimg-checkpoint.ipynb ├── 1_1dataPreprocessing.py ├── 1_2Alldata.py ├── 2_1buildModel.py ├── ALLDATA.xlsx ├── README.md ├── cm_plot.py ├── img_cut_pix │ ├── Finally.py │ ├── test.jpg │ └── testCode │ │ ├── a.py │ │ ├── b.py │ │ ├── c.py │ │ ├── d.py │ │ ├── e.py │ │ ├── f.py │ │ ├── g.py │ │ ├── test0.py │ │ └── test1.py ├── moment.csv ├── svcmodel.model ├── testPre.xlsx ├── trainPre.xlsx ├── type1.csv ├── type2.csv ├── type3.csv ├── type4.csv └── type5.csv ├── B_chapter10-家用电器用户行为分析与事件识别（DNN） ├── .ipynb_checkpoints │ ├── 1_1dataGuiyue-checkpoint.ipynb │ ├── 2_1dataExchange_divideEvent-checkpoint.ipynb │ ├── 2_2dataExchange_thresholdOptimization-checkpoint.ipynb │ ├── 2_3dataExchange_attributeConstruction-checkpoint.ipynb │ └── 3_1modelBuild-checkpoint.ipynb ├── 1TimeWaterDivide.xlsx ├── 1_1dataGuiyue.ipynb ├── 1_1dataGuiyue.py ├── 2_1dataExchange_divideEvent.ipynb ├── 2_1dataExchange_divideEvent.py ├── 2_2dataExchange_thresholdOptimization.ipynb ├── 2_2dataExchange_thresholdOptimization.py ├── 2_3_1time_gap_compute.py ├── 2_3dataExchange_attributeConstruction.ipynb ├── 2_3dataExchange_attributeConstruction.py ├── 3_1modelBuild.ipynb ├── 3_1modelBuild.py ├── README.md ├── Water-pause-times.jpg ├── attrConst_results.xlsx ├── cm_plot.py ├── cm_plot.pyc ├── dataExchange_divideEvent.xlsx ├── data_filter.xlsx ├── data_for_attr_const.xlsx ├── data_guiyue.xlsx ├── net.model ├── original_data.xls ├── test_neural_network_data.xls ├── test_output_data.xls ├── thresholdOptimization.xlsx ├── threshold_numofCase.jpg └── train_neural_network_data.xls ├── B_chapter11-应用系统负载分析与磁盘容量预测（ARIMA） ├── .ipynb_checkpoints │ ├── 1_dataExploration-checkpoint.ipynb │ ├── 2_attrsConstruction-checkpoint.ipynb │ ├── 3_1_1buildModel-checkpoint.ipynb │ ├── 3_1_2buildModel-checkpoint.ipynb │ ├── 3_1_3buildModel-checkpoint.ipynb │ ├── 3_1_4buildModel_HQ_ARIMA-checkpoint.ipynb │ ├── 3_1buildModel_C-checkpoint.ipynb │ ├── 3_2buildModel_D-checkpoint.ipynb │ ├── predictResultPicture-checkpoint.ipynb │ └── steadyCheck-checkpoint.ipynb ├── 1_dataExploration.ipynb ├── 1_dataExploration.py ├── 2_attrsConstruction.ipynb ├── 2_attrsConstruction.py ├── 3_1_1buildModel.ipynb ├── 3_1_2buildModel.ipynb ├── 3_1_3buildModel.ipynb ├── 3_1_4buildModel_HQ_ARIMA.ipynb ├── 3_1_4buildModel_HQ_ARIMA.py ├── 3_1buildModel_C.ipynb ├── 3_1buildModel_C.py ├── 3_2buildModel_D.ipynb ├── REMDME.md ├── acf_pacf.jpg ├── attrsConstruction.xlsx ├── c.jpg ├── d.jpg ├── dataCleaned.xlsx ├── data_predict_pic.jpg ├── discdata.xls ├── pedictdata_C.xlsx ├── pedictdata_C_AIC_ARMA.xlsx ├── pedictdata_C_BIC_ARIMA.xlsx ├── pedictdata_C_BIC_ARMA.xlsx ├── pedictdata_D.xlsx ├── predictResultPicture.ipynb ├── predictResultPicture.py ├── steadyCheck.ipynb └── steadyCheck.py ├── B_chapter12-电子商务网站用户行为分析及服务推荐（协同推荐） ├── 1_2_Data Exploration and Analysis.ipynb ├── 3_webpage_rank.ipynb ├── readme.md └── tmp │ ├── 1_1_3type_counts.xlsx │ ├── 1_4type107.xlsx │ ├── 2_2_2clickTimes.xlsx │ ├── 2_3_4typeID.xlsx │ ├── 2_3_5lookMorethan100.xlsx │ └── 3_flipPageResult.xlsx ├── B_chapter13-财政收入影响因素分析及预测模型（Lasso+GM11+LM+LinearSVR） ├── 1_1summaryMeasure.xlsx ├── 1_2relatedAnalysis.csv ├── 1_net.model ├── 1dataExplore.ipynb ├── 1dataExplore.py ├── 2_1_1Lasso.ipynb ├── 2_1_1Lasso.py ├── 2_1_2_1greyPredict.xlsx ├── 2_1_2greyPredict.ipynb ├── 2_1_2greyPredict.py ├── 2_1_3LMPredict.ipynb ├── 2_1_3LMPredict.py ├── 2_1_3_1revenue.xlsx ├── 2_2_1Lasso.ipynb ├── 2_2_1Lasso.py ├── 2_2_2_1greyPredict.xlsx ├── 2_2_2greyPredict.ipynb ├── 2_2_2greyPredict.py ├── 2_2_3LMPredict.ipynb ├── 2_2_3LMPredict.py ├── 2_2_3_1zengzhi.xlsx ├── 2_3_1zfxjjsrPredict.ipynb ├── 2_3_1zfxjjsrPredict.py ├── 2_net.model ├── GM11.py ├── 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