<commit_before>---
layout: post
title: Kaggle 入门一步一步记录
subtitle: 认真，坚持
author: <NAME>
tags: [Machine Learning, Competitions]
image: 
comments: true
published: true
---

为了不让手生，纯复现机器学习算法也蛮无聊的，想抽空也看看Kaggle上的比赛，当然，刚开始还是拿已经结束的比较经典的比赛来试试看吧！

## Titanic: Machine Learning from Disaster
先通过具体[比赛介绍](https://www.kaggle.com/c/titanic)了解一下题意，我们是要通过给定的数据预测一个人是否最后会在灾难中生存下来。


## References
1. [如何在 Kaggle 首战中进入前 10%](https://dnc1994.com/2016/04/rank-10-percent-in-first-kaggle-competition/)
2. [使用sklearn做单机特征工程](https://www.cnblogs.com/jasonfreak/p/5448385.html)
3. [分分钟带你杀入Kaggle Top 1%](https://zhuanlan.zhihu.com/p/27424282)
4. [Learn Machine Learning](https://www.kaggle.com/dansbecker/learn-machine-learning)
5. [Titanic Competition from Kaggle](https://www.kaggle.com/rochellesilva/simple-tutorial-for-beginners)
6. [Suggest an easy level/start level competition](https://www.kaggle.com/getting-started/12606)
7. [Introduction to Ensembling/Stacking in Python](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python)<commit_msg>commit<commit_after>---
layout: post
title: Kaggle 入门一步一步记录
subtitle: 认真，坚持
author: <NAME>
tags: [Machine Learning, Competitions]
image: 
comments: true
published: true
---

为了不让手生，纯复现机器学习算法也蛮无聊的，想抽空也看看Kaggle上的比赛，当然，刚开始还是拿已经结束的比较经典的比赛来试试看吧！

## Titanic: Machine Learning from Disaster
先通过具体[比赛介绍](https://www.kaggle.com/c/titanic)了解一下题意，我们是要通过给定的数据预测一个人是否最后会在灾难中生存下来。

了解了题目之后，接下来就是要看数据了。

### 数据预处理
#### 认识数据




## References
1. [如何在 Kaggle 首战中进入前 10%](https://dnc1994.com/2016/04/rank-10-percent-in-first-kaggle-competition/)
2. [使用sklearn做单机特征工程](https://www.cnblogs.com/jasonfreak/p/5448385.html)
3. [分分钟带你杀入Kaggle Top 1%](https://zhuanlan.zhihu.com/p/27424282)
4. [Learn Machine Learning](https://www.kaggle.com/dansbecker/learn-machine-learning)
5. [Titanic Competition from Kaggle](https://www.kaggle.com/rochellesilva/simple-tutorial-for-beginners)
6. [Suggest an easy level/start level competition](https://www.kaggle.com/getting-started/12606)
7. [Introduction to Ensembling/Stacking in Python](https://www.kaggle.com/arthurtok/introduction-to-ensembling-stacking-in-python)
8. [All You Need is PCA (LB: 0.11421, top 4%)](https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4)
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