churn prediction

Customer churn is a big problem facing banks in the increasingly competitive market. By analyzing the reasons of bank customer churn, this paper puts forward a method of establishing customer churn prediction model. By using the model, we find out the forecast loss group, forecast the loss trend, and then formulate eff

For China's major telecom operators, in the overall market size is relatively stable, the maintenance of the existing customers is to ensure that the most important part of their profits. Therefore, the prediction of the possibility of customer churn is directly related to the operator's customer maintenance focus is correct or not. This article will be based on the "Bear" base case: Collect customer

and the characteristics of the user to develop the corresponding rules, such as the loss of the user's age, income level, credit and so on. Use such a rule to filter user data, meet these rules of users belong to high risk users, may occur user churn, enterprises to these users to take some retention action and care strategy, and ensure that users continue to use Enterprise services. Disadvantages: Because of the more and more abundant services prov

‘’‘ Long ago wanted to write this article, their work has been cast CVPR2018, want to sum up. Update: CVPR2018 has been received and will have time to introduce in the next blog post. ’‘’ There are many early methods, such as HMM,CRBM, Gaussian processes and so on. This article mainly summarizes the recent top article to take the method. Forecasting Human Dynamics from Static Images (CVPR2017) This article is mainly on the Hourglass network added RNN, as shown above. In the way of skip connect

TroubleAs a data analyst, you have been working in this multinational bank for half a year.This morning, the boss called you to the office, dignified complexion.You play the drums in your heart and you think you've stabbed something. Fortunately, the boss's words made you quickly dismiss your concerns.Customers are mainly located in France, Germany and Spain.The information you have in your hands, including their age, gender, credit, card information, etc. Whether the customer has lost informati

') + \ Stat_function (fun = lambda x:baseline, color= ' green ') + \ Xlim ( -0.05, 1.05) + Ylim ( -0.05,1.05) + \ Ggtitle (" Random Forest ") + \ xlab (" predicted probability ") + ylab (" Relative frequency of outcome ") You may also have noticed that I draw two lines I use Stat_function (). The red line represents the perfect prediction for a given group, or when the predicted loss probability equals the result frequency. The Gree

, the monthly variables accounted for the sum of the variables. With these cleaning and transformation work, we generate a dataset for modeling. (iv) Establishment of models. We choose the SAS EM Package as the modeling tool and choose the decision tree algorithm in the mining algorithm. The decision tree algorithm can handle hundreds of fields, has exploratory function and is highly automated. Considering the big difference between the fixed and PHS products, it is necessary to establish a fore

As a hand-tour operator, all know that hand Grand is also a long process, we simply call the entire operation activity is a life cycle. In this tedious and compact process, there are a lot of problems that make running comrades really have a headache. The most common is the user churn problem, it is really like when you play micro-bo when, on a toilet back to find out two powder the same feeling, then you really will doubt whether you toilet posture i

Prevent user churn which strong? Testin Crash Analysis seconds kill Flurry2014/11/28Testin·Exclusive ReviewsAs we all know, each app is more or less a quality hazard, which is unavoidable for developers, but you can use development tools to minimize the impact on users. When users are faced with Bug-like apps and can't find developers directly, they can only go to the app store to leave a bad comment and growl in the social network. This can be avoide

When programming with Eclipse or myeclipse, you often encounter garbled characters when some files are opened (especially after importing items)1: Right click on Project selection Properties->resource>other select Utf-8,apply->ok toThis is usually just a change in the Java file encodingContent Types, Preferences, 2:windowsClick "Text" in the form on the right to select JSP---Default Encoding input UTF-8 and then click Update to change the encoding of JSP files in bulkFiles in other formats, and

Editor's note: This article from the United States famous red dot venture Tom tunguz Hand, by a number of well-known overseas science and technology Media and VC Media reprinted, Chinese version by Heaven Zhuhai Branch Rudder compiled. How do you calculate the user churn rate for your own startups? How many users are lost per month? That can't hold the key, because there are big customers and small customers. By the upward sales rate? That is biased,

For details, please refer to: http://lab.fs.uni-lj.si/lasin/wp/IMIT_files/neural/nn05_narnet/Format compact% Data SETTINGSN = 249; % Number of Samplesnu = 224; % Number of learning Samplesy = data;% Input your data% prepare training Datayt = Con2seq (Y (1:nu) ');% prepare Test datayv = Con2seq (Y (nu+1:end) ');% Choose a Training function% for a list of all Training functions type:help nntrain% ' TRAINLM ' I s usually fastest.% ' TRAINBR ' takes longer but may is better for challenging problems.

', ' E/E.la Bel '] Load_datasets (Featurepaths[:4], labelpaths[:4]) ' Load data ' x_train, Y_train = Load_datasets (feat Urepaths[:4],labelpaths[:4]) x_test, y_test = Load_datasets (featurepaths[4:],labelpaths[4:]) X_train, X_, Y_train, Y_ = Train_test_split (X_train, y_train, test_size = 0.0) print (' Start training KNN ') KNN = Kneighborsclassifier ( ). Fit (X_train, Y_train) print ("Training done") ANSWER_KNN = Knn.predict (x_test) print (' predict