【Kaggle】Intro to Machine Learning 第一次提交 Titanic

Michael阿明

发布于 2020-07-13 14:22:17

4010

发布于 2020-07-13 14:22:17

文章被收录于专栏：Michael阿明学习之路Michael阿明学习之路

项目官网地址

自己简要再记录一下：

Join the competition

各个 tab 下可以查看数据Data、代码编写Notebooks、讨论、排名、比赛规则、队伍

点击 Notebooks，新建文件

添加比赛数据集

编写代码

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

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

# 读取数据
test_data = pd.read_csv("../input/titanic/test.csv")
test_data.head()
train_data = pd.read_csv("../input/titanic/train.csv")
train_data.head()

# 简要的数据查看，分析男女生存状况
women = train_data.loc[train_data.Sex == 'female']["Survived"]
rate_women = sum(women)/len(women)
print("% of women who survived:", rate_women)

men = train_data.loc[train_data.Sex == 'male']["Survived"]
rate_men = sum(men)/len(men)
print("% of men who survived:", rate_men)

# 随机森林模型，选取4个特征
from sklearn.ensemble import RandomForestClassifier
y = train_data["Survived"]
features = ["Pclass", "Sex", "SibSp", "Parch"]
X = pd.get_dummies(train_data[features])# get_dummies编码处理
X_test = pd.get_dummies(test_data[features])

# 设置模型参数
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)#训练
predictions = model.predict(X_test)#预测

# 输出预测文件
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
# 写入csv文件
output.to_csv('my_submission.csv', index=False)
print("Your submission was successfully saved!")