Extracted & tweaked code from https://www.dataquest.io/mission/74/getting-started-with-kaggle/

titanic_kaggle.py

import pandas
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation

def sanitize(dataset):
    dataset["Age"] = dataset["Age"].fillna(dataset["Age"].median())
    dataset["Fare"] = dataset["Fare"].fillna(dataset["Fare"].median())

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

    dataset["Embarked"] = dataset["Embarked"].fillna("S") 
    dataset.loc[dataset["Embarked"] == "S", "Embarked"] = 0
    dataset.loc[dataset["Embarked"] == "C", "Embarked"] = 1
    dataset.loc[dataset["Embarked"] == "Q", "Embarked"] = 2

    return dataset

if __name__ == '__main__':
    titanic      = sanitize(pandas.read_csv("train.csv"))
    titanic_test = sanitize(pandas.read_csv("test.csv"))

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

    alg = LogisticRegression(random_state=1)
    alg.fit(titanic[predictors], titanic["Survived"])
    predictions = alg.predict(titanic_test[predictors])
    
    scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)
    print(scores.mean())
 
    submission = pandas.DataFrame({"PassengerId": titanic_test["PassengerId"], "Survived": predictions })
    submission.to_csv("kaggle.csv", index=False)

Extracted & tweaked code from Getting started with Kaggle tutorial for the Titanic Kaggle challenge.