項目官網地址

新手可以參考這篇 8、Getting Started With Titanic，教你如何操作、提交等

自己簡要再記錄一下：

• Join the competition
  
  各個 tab 下可以查看數據Data、代碼編寫Notebooks、討論、排名、比賽規則、隊伍
• 點擊 Notebooks，新建文件
  
• 添加比賽數據集
  
• 編寫代碼

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)) ? # 讀取數據 test_data = pd.read_csv("../input/titanic/test.csv") test_data.head() train_data = pd.read_csv("../input/titanic/train.csv") train_data.head()# 簡要的數據查看，分析男女生存狀況 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)# 隨機森林模型，選取4個特征 from sklearn.ensemble import RandomForestClassifier y = train_data["Survived"] features = ["Pclass", "Sex", "SibSp", "Parch"] X = pd.get_dummies(train_data[features])# get_dummies編碼處理 X_test = pd.get_dummies(test_data[features])# 設置模型參數 model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1) model.fit(X, y)#訓練 predictions = model.predict(X_test)#預測# 輸出預測文件 output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions}) # 寫入csv文件 output.to_csv('my_submission.csv', index=False) print("Your submission was successfully saved!")

• 保存、運行
  
  
  
  往下找到 output files
  
  

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完成課程 Intro to Machine Learning，發了一張證書，哈哈，加油！

總結

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