kaggle titanic 入门实例 逻辑回归的使用 & 随机森林的使用

[code]#coding:utf-8
import numpy as np
import pandas as pd

train = pd.read_csv("./csv/train.csv", dtype={"Age": np.float64},)
test = pd.read_csv("./csv/test.csv", dtype={"Age": np.float64},)

def harmonize_data(titanic):#填充空数据 和 把string数据转成integer表示

    titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())

    titanic.loc[titanic["Sex"] == "male", "Sex"] = 0
    titanic.loc[titanic["Sex"] == "female", "Sex"] = 1

    titanic["Embarked"] = titanic["Embarked"].fillna("S")

    titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0
    titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1
    titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2

    titanic["Fare"] = titanic["Fare"].fillna(titanic["Fare"].median())

    return titanic

def create_submission(alg, train, test, predictors, filename):

    alg.fit(train[predictors], train["Survived"])
    predictions = alg.predict(test[predictors])

    submission = pd.DataFrame({
        "PassengerId": test["PassengerId"],
        "Survived": predictions
    })

    submission.to_csv(filename, index=False)

train_data = harmonize_data(train)
test_data  = harmonize_data(test)

from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation

predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]

alg    = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(#对于cross_val_score的解释http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_score.html
    alg,
    train_data[predictors],
    train_data["Survived"],
    cv=3
)

print(scores.mean())

from sklearn.ensemble import RandomForestClassifier

predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]

alg = RandomForestClassifier(
    random_state=1,
    n_estimators=150,
    min_samples_split=4,
    min_samples_leaf=2
)

scores = cross_validation.cross_val_score(
    alg,
    train_data[predictors],
    train_data["Survived"],
    cv=3
)

print(scores.mean())

create_submission(alg, train_data, test_data, predictors, "run-01.csv")