Titanic

作为Kaggle中最经典的机器学习入门算法，总结了机器学习的思路

导入数据

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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

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# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('datasets/titanic'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

datasets/titanic\gender_submission.csv
datasets/titanic\test.csv
datasets/titanic\train.csv

简单看一眼数据

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train_data = pd.read_csv("datasets/titanic/train.csv")
train_data.head()

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S 
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test_data = pd.read_csv("datasets/titanic/test.csv")
test_data.head()

PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S

用原始的方法看一下性别对幸存的影响

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women = train_data.loc[train_data.Sex == 'female']["Survived"]
rate_women = sum(women)/len(women)

print("% of women who survived:", rate_women)
% of women who survived: 0.7420382165605095

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men = train_data.loc[train_data.Sex == 'male']["Survived"]
rate_men = sum(men)/len(men)

print("% of men who survived:", rate_men)
% of men who survived: 0.18890814558058924

使用决策树的方式对数据进行预测

原理如下

image.png

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from sklearn.ensemble import RandomForestClassifier

y = train_data["Survived"]

features = ["Pclass", "Sex", "SibSp", "Parch"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])

# 构建了100课随机树
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)

output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!")
Your submission was successfully saved!

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train_data.describe(include='all')

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
count 891.000000 891.000000 891.000000 891 891 714.000000 891.000000 891.000000 891 891.000000 204 889
unique NaN NaN NaN 891 2 NaN NaN NaN 681 NaN 147 3
top NaN NaN NaN Braund, Mr. Owen Harris male NaN NaN NaN 347082 NaN B96 B98 S
freq NaN NaN NaN 1 577 NaN NaN NaN 7 NaN 4 644
mean 446.000000 0.383838 2.308642 NaN NaN 29.699118 0.523008 0.381594 NaN 32.204208 NaN NaN
std 257.353842 0.486592 0.836071 NaN NaN 14.526497 1.102743 0.806057 NaN 49.693429 NaN NaN
min 1.000000 0.000000 1.000000 NaN NaN 0.420000 0.000000 0.000000 NaN 0.000000 NaN NaN
25% 223.500000 0.000000 2.000000 NaN NaN 20.125000 0.000000 0.000000 NaN 7.910400 NaN NaN
50% 446.000000 0.000000 3.000000 NaN NaN 28.000000 0.000000 0.000000 NaN 14.454200 NaN NaN
75% 668.500000 1.000000 3.000000 NaN NaN 38.000000 1.000000 0.000000 NaN 31.000000 NaN NaN
max 891.000000 1.000000 3.000000 NaN NaN 80.000000 8.000000 6.000000 NaN 512.329200 NaN NaN