fordatasetincombine:dataset['Title']=dataset.Name.str.extract('([A-Za-z]+)\.',expand=False)dataset['Title']=dataset['Title'].replace(['Lady','Countess','Col','Don','Dr','Major','Rev','Sir','Jonkheer','Dona'],'Rare')dataset['Title']=dataset['Title'].replace('Mlle','Miss')...
from sklearn import preprocessing df=pd.read_csv('D:\\dataset\\Titanic-train.csv') #Embarked列用众数填充空值,强制转化为str类型,这里如果不转化会报错 df.Embarked=df.Embarked.fillna(df.Embarked.mode()).astype(str) #Age空值根据已有值得平均数来填充 df.Age=df.Age.fillna(df.Age.mean()) #删除...
parch: The dataset defines family relations in this way… Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them. 知道了上面的数据定义,那么处理数据也就有方向了,首先加载数据: 代码语言:javascript 代码运行次数:0...
pythonmachine-learningalgorithmslinear-regressionjupyter-notebookpython3logistic-regressionunsupervised-learningwine-qualitymachine-learning-tutorialstitanic-datasetxor-neural-networkheadbrain-datasetrandom-forest-mnistpca-titanic-dataset UpdatedNov 20, 2022
A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original ...
from dataprep.datasets import load_dataset # 内置数据集 from dataprep.eda import plot # 绘图 from dataprep.eda import plot_correlation # 相关性 from dataprep.eda import create_report # 分析报告 from dataprep.eda import plot_missing # 缺失值 ...
#Divide the dataset in random order into training and test in proportions#https://www.cnblogs.com/cindycindy/p/13515115.html train_test_split函数training, testing = train_test_split(train, test_size=0.2, random_state=0) cols= ['Pclass','Sex','Age','Fare','Embarked','Family','Alone'...
This project uses the Titanic dataset to predict whether a passenger survived or not based on various features such as age, gender, fare, class, and titles derived from names. The goal is to apply feature engineering and train multiple classification models to evaluate their performance. Features...
28.0s22For own created test dataset 28.0s23precision recall f1-score support 28.0s24 28.0s250 0.88 0.89 0.88 142 28.0s261 0.80 0.78 0.79 81 28.0s27 28.0s28accuracy 0.85 223 28.0s29macro avg 0.84 0.83 0.83 223 28.0s30weighted avg 0.85 0.85 0.85 223 ...
http:///tutorial-titanic-machine-learning-from-distaster/ Full Titanic Example with Random Forest https:///watch?v=0GrciaGYzV0 [Tutorial: Titanic dataset machine learning for Kaggle] ( http://corpocrat.com/2014/08/29/tutorial-titanic-dataset-machine-learning-for-kaggle/) [Getting Started with...