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新手可以参考这篇 8、Getting Started With Titanic#xff0c;教你如何操作、提交等
自己简要再记录一下#xff1a;
Join the competition 各个 tab 下可以查看数据Data、代码编写Notebooks、讨论、排名、比赛规则、队伍点击 Notebooks#xff0c;新建文…项目官网地址
新手可以参考这篇 8、Getting Started With Titanic教你如何操作、提交等
自己简要再记录一下
Join the competition 各个 tab 下可以查看数据Data、代码编写Notebooks、讨论、排名、比赛规则、队伍点击 Notebooks新建文件 添加比赛数据集 编写代码
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
for dirname, _, filenames in os.walk(/kaggle/input):for filename in filenames:print(os.path.join(dirname, filename))
# 读取数据
test_data pd.read_csv(../input/titanic/test.csv)
test_data.head()
train_data pd.read_csv(../input/titanic/train.csv)
train_data.head()# 简要的数据查看分析男女生存状况
women train_data.loc[train_data.Sex female][Survived]
rate_women sum(women)/len(women)
print(% of women who survived:, rate_women)men train_data.loc[train_data.Sex male][Survived]
rate_men sum(men)/len(men)
print(% of men who survived:, rate_men)# 随机森林模型选取4个特征
from sklearn.ensemble import RandomForestClassifier
y train_data[Survived]
features [Pclass, Sex, SibSp, Parch]
X pd.get_dummies(train_data[features])# get_dummies编码处理
X_test pd.get_dummies(test_data[features])# 设置模型参数
model RandomForestClassifier(n_estimators100, max_depth5, random_state1)
model.fit(X, y)#训练
predictions model.predict(X_test)#预测# 输出预测文件
output pd.DataFrame({PassengerId: test_data.PassengerId, Survived: predictions})
# 写入csv文件
output.to_csv(my_submission.csv, indexFalse)
print(Your submission was successfully saved!)保存、运行 往下找到 output files 完成课程 Intro to Machine Learning发了一张证书哈哈加油
