Description:
Fraud risk is everywhere, but for companies that advertise online, click fraud can happen at an overwhelming volume, resulting in misleading click data and wasted money. Ad channels can drive up costs by simply clicking on the ad at a large scale. With over 1 billion smart mobile devices in active use every month, China is the largest mobile market in the world and therefore suffers from huge volumes of fradulent traffic.
TalkingData, China’s largest independent big data service platform, covers over 70% of active mobile devices nationwide. They handle 3 billion clicks per day, of which 90% are potentially fraudulent. Their current approach to prevent click fraud for app developers is to measure the journey of a user’s click across their portfolio, and flag IP addresses who produce lots of clicks, but never end up installing apps. With this information, they've built an IP blacklist and device blacklist.
While successful, they want to always be one step ahead of fraudsters and have turned to the Kaggle community for help in further developing their solution. In their 2nd competition with Kaggle, you’re challenged to build an algorithm that predicts whether a user will download an app after clicking a mobile app ad. To support your modeling, they have provided a generous dataset covering approximately 200 million clicks over 4 days!
TimeLine:
- April 30, 2018 - Entry deadline. You must accept the competition rules before this date in order to compete.
- April 30, 2018 - Team Merger deadline. This is the last day participants may join or merge teams.
- May 7, 2018 - Final submission deadline.
- All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
Evaluation
Submissions are evaluated on area under the ROC curve between the predicted probability and the observed target.
Submission File
For each click_id in the test set, you must predict a probability for the target is_attributed variable. The file should contain a header and have the following format:
click_id,is_attributed
1,0.003
2,0.001
3,0.000
etc.
地址： https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection#description
Description:
Like most companies, Red Hat is able to gather a great deal of information over time about the behavior of individuals who interact with them. They’re in search of better methods of using this behavioral data to predict which individuals they should approach—and even when and how to approach them.
In this competition, Kagglers are challenged to create a classification algorithm that accurately identifies which customers have the most potential business value for Red Hat based on their characteristics and activities.
With an improved prediction model in place, Red Hat will be able to more efficiently prioritize resources to generate more business and better serve their customers.
Evaluation
Submissions are evaluated on area under the ROC curve between the predicted and the observed outcome.
Submission File
For each activity_id in the test set, you must predict a probability for the outcome variable, represented by a number between 0 and 1. The file should contain a header and have the following format:
activity_id,outcome
act1_1,0
act1_100006,0
act1_100050,0
etc.
TimeLine:
- September 12, 2016 - First Submission deadline. A team must make at least one submission by this date in order to compete.
- September 12, 2016 - Team Merger deadline. This is the last day competitors may join or merge teams.
- September 19, 2016 - Final submission deadline.
- All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
地址： https://www.kaggle.com/c/predicting-red-hat-business-value#description
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
Practice Skills
- Binary classification
- Python and R basics
Goal
It is your job to predict if a passenger survived the sinking of the Titanic or not.
For each PassengerId in the test set, you must predict a 0 or 1 value for the Survived variable.
Metric
Your score is the percentage of passengers you correctly predict. This is known simply as "accuracy”.
Submission File Format
You should submit a csv file with exactly 418 entries plus a header row. Your submission will show an error if you have extra columns (beyond PassengerId and Survived) or rows.
The file should have exactly 2 columns:
- PassengerId (sorted in any order)
- Survived (contains your binary predictions: 1 for survived, 0 for deceased)
PassengerId,Survived
892,0
893,1
894,0
Etc.
地址： https://www.kaggle.com/c/titanic#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
Goal
It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.
Metric Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)
Submission File Format
The file should contain a header and have the following format:
Id,SalePrice
1461,169000.1
1462,187724.1233
1463,175221
etc.
地址： https://www.kaggle.com/c/house-prices-advanced-regression-techniques#description