Clarify open source licensing

1st Place/LICENSE

@@ -0,0 +1,22 @@
+MIT License
+
+Copyright (c) 2018 Li Ying Zheng and Hong Shen Sun (Team PINGANAI_ - daisyhyun, 
+PINGANAI)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

2nd Place/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Levi Viana (lviana)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

3rd Place/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Iana Valiuk (viana)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Best Report/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Pavel Kuzmin (pavel_kuzmin)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright: See individual winner LICENSE files
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -3,7 +3,8 @@
 
 ![Banner Image](https://s3.amazonaws.com/drivendata-public-assets/se-challenge-2-banner.jpg)
 
-#  Power Laws: Anomaly Detection
+# Power Laws: Anomaly Detection
+
 ## Goal of the Competition
 
 Energy consumption of buildings has steadily increased. There is an increasing realization that many buildings do not perform as intended by their designers. Typical buildings consume 20% more energy than necessary due to faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures.
@@ -12,15 +13,15 @@ The building systems may fail to meet the performance expectations due to variou
 
 Therefore, it is of great potential to develop automatic, quick-responding, accurate and reliable fault detection and to provide diagnosis schemes to ensure the optimal operations of systems to save energy.
 
-** In [this competition](https://www.drivendata.org/competitions/52/anomaly-detection-electricity/) data scientists all over the world built algorithms to forecast building consumption reliably. **
+**In [this competition](https://www.drivendata.org/competitions/52/anomaly-detection-electricity/), data scientists from all over the world built algorithms to forecast building consumption reliably.**
 
 Identifying anomalies was a tricky task, and the best performers combined human judgment with machine suggestions. Winners documented these approaches and sugggested new ways of thinking about anomalies in the context of energy usage. Ultimately, these ideas can help drive forward the kinds of anomalies that we hope to identify in building energy use.
 
 ## What's in this Repository
-This repository contains code from winning competitors in the [Power Laws: Anomaly Detection](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/) DrivenData challenge.
 
-#### Winning code for other DrivenData competitions is available in the [competition-winners repository](https://github.com/drivendataorg/competition-winners).
+This repository contains code from winning competitors in the [Power Laws: Anomaly Detection](https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/) DrivenData challenge. Code for all winning solutions are open source under the MIT License.
 
+**Winning code for other DrivenData competitions is available in the [competition-winners repository](https://github.com/drivendataorg/competition-winners).**
 
 ## Winning Submissions
 
@@ -31,4 +32,4 @@ Place |Team or User | Public Score | Private Score | Summary of Model
 3 | viana | 0.8001 | 0.8000 | Hand picking and LB probing algorithm taking advantage of optimization metric. Huge penalty for FP answers convinced me to find several concrete examples of anomalies and to get public score around 80%. Other motivation for that was to gain an understanding of what is meant by anomaly. To find these examples I grouped-by observations with similar time related features and marked 4 sigma deviations as anomalies. Then by manually examining groups of anomalies I started LB probing for each meter_id separately. After a week or so I got the expected score.  That said, my approaches to finding initial candidates for anomalies might be useful.
 Best Report | pavel_kuzman | 0.7111 | 0.6008 | Pavel created models for identifying anomalies using k-nearest neighbors and neiral networks. After this he explored the kinds of anomalies that were detected using these methods and how identification of anomalies could be improved with additional data.
 
-#### [Interview with winners](http://drivendata.co/blog/power-laws-anomalies-winners/)
+**[Interview with winners](https://drivendata.co/blog/power-laws-anomalies-winners/)**