xgboostclassifier.py

import matplotlib.pyplot as plt

# https://xgboost.readthedocs.io/en/stable/
import numpy as np
import pandas as pd
import seaborn as sns
from xgboost import XGBClassifier

training = pd.read_csv("../input/titanic/train.csv")
test = pd.read_csv("../input/titanic/test.csv")

# Commented out IPython magic to ensure Python compatibility.
training["train_test"] = 1
test["train_test"] = 0
test["Survived"] = np.NaN
all_data = pd.concat([training, test])
# %matplotlib inline
all_data.columns

all_data.describe()

all_data["cabin_mul"] = all_data.Cabin.apply(
    lambda x: 0 if pd.isna(x) else len(x.split(" "))
)
all_data["cabin_adv"] = all_data.Cabin.apply(lambda x: str(x)[0])
all_data["name_title"] = all_data.Name.apply(
    lambda x: x.split(",")[1].split(".")[0].strip()
)
all_data.Age = all_data.Age.fillna(training.Age.median())
all_data.Fare = all_data.Fare.fillna(training.Fare.median())
all_data.dropna(subset=["Embarked"], inplace=True)
all_data["norm_fare"] = np.log(all_data.Fare + 1)
all_data.Pclass = all_data.Pclass.astype(str)
all_data["Age"] = all_data["Age"].apply(np.int64)
all_dummies = pd.get_dummies(
    all_data[
        [
            "Pclass",
            "Sex",
            "Age",
            "SibSp",
            "Parch",
            "norm_fare",
            "Embarked",
            "cabin_adv",
            "cabin_mul",
            "name_title",
            "train_test",
        ]
    ]
)

from sklearn.preprocessing import StandardScaler

scale = StandardScaler()
all_dummies_scaled = all_dummies.copy()
all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]] = scale.fit_transform(
    all_dummies_scaled[["Age", "SibSp", "Parch", "norm_fare"]]
)
all_dummies_scaled.head()

X_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(
    ["train_test"], axis=1
)
X_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(
    ["train_test"], axis=1
)

y_train = all_data[all_data.train_test == 1].Survived

from xgboost import XGBClassifier

xgb = XGBClassifier()
xgb.fit(X_train_scaled, y_train)

y_hat_base_vc = xgb.predict(X_test_scaled).astype(int)
basic_submission = {"PassengerId": test.PassengerId, "Survived": y_hat_base_vc}
base_submission = pd.DataFrame(data=basic_submission)
base_submission.to_csv("xgb_submission.csv", index=False)