Add Home Descriptions & Contributors (#20)

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* Rename dir

* Add Descriptions

* Add Home Description & Contributor

Author: Chen Quan

DigitRecognizer/README.md

@@ -19,8 +19,8 @@
 
 |翻译项目原地址|作者|本库地址|翻译后版本|
 |---|---|---|---|
-|[introduction-to-cnn-keras-0-997-top-6](https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6)|[Yassine Ghouzam](https://www.kaggle.com/yassineghouzam)|[GO](introduction-to-cnn-keras-0-997-top-6.ipynb)|[GO](introduction-to-cnn-keras-0-997-top-6-中文.ipynb)|
-| [welcome-to-deep-learning-cnn-99](https://www.kaggle.com/toregil/welcome-to-deep-learning-cnn-99) | [Peter Grenholm](https://www.kaggle.com/toregil) | [GO](welcome-to-deep-learning-cnn-99.ipynb) |[GO](welcome-to-deep-learning-cnn-99.ipynb)|
+|[introduction-to-cnn-keras-0-997-top-6](https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6)|[Yassine Ghouzam](https://www.kaggle.com/yassineghouzam)|[GO](introduction-to-cnn-keras-0-997-top-6.ipynb)|[GO](introduction-to-cnn-keras-0-997-top-6-cn.ipynb)|
+| [welcome-to-deep-learning-cnn-99](https://www.kaggle.com/toregil/welcome-to-deep-learning-cnn-99) | [Peter Grenholm](https://www.kaggle.com/toregil) | [GO](welcome-to-deep-learning-cnn-99.ipynb) |[GO](welcome-to-deep-learning-cnn-99-cn.ipynb)|
 | --- | --- | --- | --- |
 
 ## 介绍

DigitRecognizer/introduction-to-cnn-keras-0-997-top-6-中文.ipynb renamed to DigitRecognizer/introduction-to-cnn-keras-0-997-top-6-cn.ipynb

@@ -0,0 +1,42 @@
+# House Prices: Advanced Regression Techniques #10
+
+> Predict sales prices and practice feature engineering, RFs, and gradient boosting
+
+**翻译的朋友们请先[【Fork】](https://github.com/OpenSourceAI/kaggle-competition-details/fork)本库，在自己的库中当前文件夹下面，以原文件名加【-中文】的方式命名【新建文件】（例如：原本文件名：introduction-to-cnn-keras-0-997-top-6.ipynb，新建文件名即：introduction-to-cnn-keras-0-997-top-6-中文.ipynb），完成翻译整理后进行`Pull Request`，经过其他朋友代码审查，会合并到本库，同时你们的贡献也会被记录在此页面。**
+
+>**在翻译整理的过程中请尽量深入理解文章，并提出自己的理解，不要只是为了翻译而翻译**
+
+>**如果会其他语言的同学也欢迎翻译成其他语言，例如：俄语、韩语、日语等等**
+## 贡献者：
+
+- [@ChenQuan](https://github.com/ChenQuan)
+
+
+
+## 比赛：
+
+地址：[https://www.kaggle.com/c/house-prices-advanced-regression-techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)
+
+|翻译项目原地址|作者|本库地址|翻译后版本|
+|---|---|---|---|
+|[COMPREHENSIVE DATA EXPLORATION WITH PYTHON](https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python)|[Pedro Marcelino](https://www.kaggle.com/pmarcelino)|[GO](data-sciencetutorial-for-beginners.ipynb)|[GO](data-sciencetutorial-for-beginners-cn.ipynb)|
+| --- | --- | --- | --- |
+
+
+## 介绍
+
+Start here if...
+You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition. 
+
+Competition Description
+
+
+Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
+
+With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
+
+Practice Skills
+Creative feature engineering 
+Advanced regression techniques like random forest and gradient boosting
+Acknowledgments
+The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset. 

DigitRecognizer/welcome-to-deep-learning-cnn-99-中文.ipynb renamed to DigitRecognizer/welcome-to-deep-learning-cnn-99-cn.ipynb

@@ -2,14 +2,18 @@
 
 >Can you predict the battle royale finish of PUBG Players?
 
+## 贡献者：
+
+- [@ChenQuan](https://github.com/ChenQuan)
+
 
 ## 比赛：
 
 地址：[https://www.kaggle.com/c/pubg-finish-placement-prediction](https://www.kaggle.com/c/pubg-finish-placement-prediction)
 
 |翻译项目原地址|作者|本库地址|翻译|
 |---|---|---|---|
-|[eda-is-fun](https://www.kaggle.com/deffro/eda-is-fun)|[Dimitrios Effrosynidis](https://www.kaggle.com/deffro)|[GO](eda-is-fun.ipynb)|[GO](eda-is-fun-中文.ipynb)|
+|[eda-is-fun](https://www.kaggle.com/deffro/eda-is-fun)|[Dimitrios Effrosynidis](https://www.kaggle.com/deffro)|[GO](eda-is-fun.ipynb)|[GO](eda-is-fun-cn.ipynb)|
 | --- | --- | --- | --- |
 
 ## 介绍

House-Prices-Advanced-Regression-Techniques/README.md

@@ -10,6 +10,8 @@
 |---|---|
 |Digit Recognizer|[DigitRecognizer](DigitRecognizer)|
 |PUBGFinishPlacementPrediction|[PUBGFinishPlacementPrediction](PUBGFinishPlacementPrediction)|
+|House Prices: Advanced Regression Techniques|[House Prices: Advanced Regression Techniques](House-Prices-Advanced-Regression-Techniques)|
+|Pokemon- Weedle's Cave|[Pokemon- Weedle's Cave](pokemon-challenge)|
 | --- 待续 --- | --- 待续 ---|
 
 ## 项目贡献者

PUBGFinishPlacementPrediction/README.md

@@ -0,0 +1,31 @@
+# Pokemon- Weedle's Cave
+
+>Welcome to Weedle's cave
+
+## 贡献者：
+
+- [@ChenQuan](https://github.com/ChenQuan)
+
+
+## 比赛：
+
+地址：[https://www.kaggle.com/terminus7/pokemon-challenge](https://www.kaggle.com/terminus7/pokemon-challenge)
+
+|翻译项目原地址|作者|本库地址|翻译|
+|---|---|---|---|
+|[Data ScienceTutorial for Beginners](https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners)|[DATAI](https://www.kaggle.com/kanncaa1)|[GO](data-sciencetutorial-for-beginners.ipynb)|[GO](data-sciencetutorial-for-beginners-cn.ipynb)|
+| --- | --- | --- | --- |
+
+## 介绍
+Welcome to Weedle's cave. Will you be able to predict the outcome of future matches?
+
+To do it you will have the pokemon characteristics and the results of previous combats.
+
+Three files are available. The first one contains the Pokemon characteristics (the first column being the id of the Pokemon). The second one contains information about previous combats. The first two columns contain the ids of the combatants and the third one the id of the winner. Important: The Pokemon in the first columns attacks first.
+
+The goal is to develop a Machine Learning model able to predict the result of future pokemon combats.
+
+If you have any questions, please email: [email protected]
+
+DISCLAIMER
+The Pokemon characteristics come from the actual game but the battle data has been generated by a custom algorithm that doesn't take into account some of the game characteristics to simplify the dataset.