In [1]:
import numpy as np
import pandas as pd
train = pd.read_csv("data/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("data/test.csv", dtype={"Age": np.float64}, )
def harmonize_data(titanic):
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
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)
In [2]:
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
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")
In [3]:
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(
alg,
train_data[predictors],
train_data["Survived"],
cv=3
)
print(scores.mean())