航班价格数据分析与预测实战教程（附19段代码及53.07MB完整数据集）.zip

简介：
本教程深入浅出地讲解了如何分析和预测航班价格，并提供了19段实用代码以及一个包含53.07MB完整数据集，适用于希望掌握相关技能的数据分析师与研究人员。 AI实战：航班价格数据集分析预测实例（包括19个源代码文件及53.07 MB完整的数据集） 所有代码经过手工整理，确保无语法错误且可运行。 使用到的模块： pandas, sklearn.model_selection.train_test_split, sklearn.ensemble.RandomForestRegressor, math.sqrt, sklearn.metrics.mean_absolute_error, sklearn.metrics.mean_squared_error, sklearn.metrics.r2_score, matplotlib.pyplot, sklearn.model_selection.GridSearchCV, numpy, seaborn, warnings random, os sklearn.linear_model.LinearRegression sklearn.linear_model.Ridge sklearn.linear_model.Lasso sklearn.ensemble.GradientBoostingRegressor sklearn.ensemble.AdaBoostRegressor xgboost sklearn.preprocessing.LabelEncoder sklearn.svm.SVR sklearn.neighbors.KNeighborsRegressor catboost, lightgbm plotly.express sklearn.preprocessing.OneHotEncoder sklearn.preprocessing.MinMaxScaler sklearn.ensemble.BaggingRegressor xgboost.XGBRegressor pickle zstandard sklearn.preprocessing.StandardScaler sklearn.model_selection.cross_val_score statsmodels.stats.outliers_influence.variance_inflation_factor sklearn.model_selection.RandomizedSearchCV, scipy.stats.randint sklearn.inspection.PartialDependenceDisplay lime.lime_tabular sklearn.impute.SimpleImputer sklearn.pipeline.Pipeline sklearn.pipeline.FeatureUnion sklearn_features.transformers.DataFrameSelector sklearn.linear_model.SGDRegressor IPython.display.Image IPython.display.display sklearn.preprocessing.RobustScaler, sklearn.decomposition.PCA, sklearn.linear_model, sklearn.tree.DecisionTreeRegressor, sklearn.ensemble.ExtraTreesRegressor math