kaggle入门——Titanic - Machine Learning from Disaster

    # https://www.kaggle.com/competitions/titanic/code
    
    # This Python 3 environment comes with many helpful analytics libraries installed
    # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
    # For example, here's several helpful packages to load
    
    import numpy as np # linear algebra
    import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
    
    # Input data files are available in the read-only "../input/" directory
    # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
    
    import os
    for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
    
    # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
    # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
    
    # load data
    train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
    test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
    
    # 随机森林
    # features: Pclass Sex SibSp Parch
    from sklearn.ensemble import RandomForestClassifier
    
    y = train_data["Survived"]
    
    features = ["Pclass", "Sex", "SibSp", "Parch"]
    
    X = pd.get_dummies(train_data[features])
    X_test = pd.get_dummies(test_data[features])
    
    model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
    
    model.fit(X, y)
    
    predictions = model.predict(X_test)
    
    output = pd.DataFrame({'PassengerId' : test_data.PassengerId,
                       'Survived' : predictions})
    
    output.to_csv('submission.csv',index=False)
    print("Your submission was successfully saved!")
    
    
    python
    
    
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