mytitaic

mytitaic

import numpy as np import pandas as pd train = pd.read_csv(“../input/train.csv”, ) test = pd.read_csv(“../input/test.csv”, ) def fix_data(titanic): titanic[“Age”] = titanic[“Age”].fillna(titanic[“Age”].median()) titanic[“Age”].median() 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.loc[titanic[“Sex”] == “male”, “Sex”] = 0 titanic.loc[titanic[“Sex”] == “female”, “Sex”] = 1 titanic.loc[titanic[“Name”].str.find(“Sir.”) != -1, “NameClass”] = 1 titanic.loc[titanic[“Name”].str.find(“Sir.”) == -1, “NameClass”] = 0 return titanic train_data = fix_data(train) test_data = fix_data(test) features = [“Pclass”, “Sex”, “Age”, “NameClass”] X = train_data[features] y = train_data.Survived from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33) from sklearn.linear_model import LogisticRegression from sklearn import tree clf = tree.DecisionTreeClassifier(criterion=’entropy’, max_depth=3,min_samples_leaf=5) clf = clf.fit(X,y) print(“{:.2f}”.format(clf.score(X_test,y_test))) clf = LogisticRegression(random_state=3) clf.fit(X,y) print(“{:.2f}”.format(clf.score(X_test,y_test))) #0.83 from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier( random_state=1,…


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