本文共 1454 字，大约阅读时间需要 4 分钟。

新手可以，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 algebraimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)​import osfor 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 RandomForestClassifiery = 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_estimators=100, max_depth=5, random_state=1)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', index=False)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！

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