Machine learning Path: The Python decision tree classification predicts whether the Titanic passengers survived

Using Python3 to learn the API of Decision tree classifier

Related to feature extraction, data type retention, classification type extraction of new types

Need to download data sets online, I downloaded them to the local,

can download code and datasets to my git: https://github.com/linyi0604/MachineLearning

1 ImportPandas as PD2  fromSklearn.cross_validationImportTrain_test_split3  fromSklearn.feature_extractionImportDictvectorizer4  fromSklearn.treeImportDecisiontreeclassifier5  fromSklearn.metricsImportClassification_report6 7 " "8 Decision Tree9 multiple features, no apparent linear relationshipTen inference logic is very intuitive One no need to standardize the data A " " -  - " " the 1 Preparing Data - " " - #read Titanic passenger data, downloaded from the Internet to local -Titanic = Pd.read_csv ("./data/titanic/titanic.txt") + #observation data found missing phenomenon - #print (Titanic.head ()) +  A #extracting key features, sex, age, pclass are all likely to affect whether or not to be spared atx = titanic[['Pclass',' Age','Sex']] -y = titanic['survived'] - #View the currently selected feature - #print (X.info ()) - " " - <class ' pandas.core.frame.DataFrame ' > in rangeindex:1313 entries, 0 to 1312 - Data Columns (total 3 columns): to pclass 1313 Non-null Object + Age 633 Non-null float64 - sex 1313 Non-null object the Dtypes:float64 (1), Object (2) * Memory usage:30.9+ KB $ NonePanax Notoginseng " " - #There are only 633 age data columns, and the use of an average or median for vacancies is expected to have a small impact on the model thex[' Age'].fillna (x[' Age'].mean (), inplace=True) +  A " " the 2 Data Segmentation + " " -X_train, X_test, y_train, y_test = Train_test_split (x, Y, test_size=0.25, random_state=33) $ #feature extraction using a feature converter $VEC =Dictvectorizer () - #Type of data will be drawn out of the data type will remain unchanged -X_train = Vec.fit_transform (X_train.to_dict (orient="Record")) the #print (vec.feature_names_) # [' Age ', ' pclass=1st ', ' pclass=2nd ', ' pclass=3rd ', ' sex=female ', ' Sex=male '] -X_test = Vec.transform (X_test.to_dict (orient="Record"))Wuyi  the " " - 3 Training model for forecasting Wu " " - #Initialize decision tree classifier AboutDTC =Decisiontreeclassifier () $ #Training - Dtc.fit (X_train, Y_train) - #Predicting saved results -Y_predict =dtc.predict (x_test) A  + " " the 4 Model Evaluation - " " $ Print("accuracy:", Dtc.score (X_test, y_test)) the Print("Other indicators: \ n", Classification_report (Y_predict, Y_test, target_names=['died','survived'])) the " " the accuracy: 0.7811550151975684 the Other indicators: - Precision recall F1-score support in  the died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67 About  the avg/total 0.81 0.78 0.79 329 the " "

Machine learning Path: The Python decision tree classification predicts whether the Titanic passengers survived