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Case: rfm analysis of member customer transaction data using Excel Background: A Member Service Enterprise has about 1200 member customers in the past year. As the company wants to activate promotions for different categories of inactive customers, it also plans to launch a series of promotions for key customers to retain these customers and maintain their activity. Therefore, we hope to use this
Rt Reply content:I highly recommend the Python class at Rice University, which is very well designed and the teacher is very responsible. ----------------------------------------------------------- Last night mobile phone answer, updated today; Rice University has a total of 3 courses, now seemingly dismantled into 6 doors, 8 weeks per course, according to the order of the more-than-digest. The first course is the Python Foundation, which introduces the basic syntax of Python. The second cours
promotion departments. In addition to focusing on conversion rates, as well as browsing pages and online time, it is an indicator to evaluate channel value. Item 5: Content Analysis Two main indicators: exit rate and hotspot content 1. the exit rate is a good doctor. It is very suitable for B2C health check. The exit rate is high, which will basically indicate some problems, focusing on login, registration, shopping cart, and user center, these are t
which one is best for use, so let's review it.Concat allows the user to append one or more dataframe (depending on how you define the axis) below or next to the table.Merge merges multiple dataframe to specify the same row as the primary key (key).Join, like merge, incorporates two dataframe. But it does not merge by a specified primary key, but is merged by the same column name or row name.Pandas ApplyApply is designed for the pandas series. If you're not familiar with series, you can think of
ObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft Data Mining algorithm summary serial, interested children shoes can be viewed, The algorithm we are going to summarize is: Mi
Massive Data processing and analysis In my practical work, Dai ziliang, Beijing myisch Technology Co., Ltd., was lucky to have access to the massive data processing problem. It was an arduous and complex task to deal with it. The reasons are as follows: 1. If the data volume is too large, there may be any situation in
, Ascending=false)Nine, index re -Reorder The sorted indexes df.reset_index (drop) Drop is a Boolean value, and True indicates that the index of the original data is modified. false preserves the original data index sequence. Df.reset_index (drop=false)10. Averaging10.1 Average information for all columnsDf.mean ()10.2 Average of a single columndf['age'].mean ()11. Vectorization Operatio
Introduction Since. Net was truly a developer, the word "efficiency" has become a huge numberProgramA hot topic. Whether it is from the Development Mode (Cross Language), the system framework (. NET Framework), or a variety of convenient tools (vs. Net), all reflect its superior. At the same time, on the other hand, whether. Net can be truly competent for enterprise application development is still controversial. Generally, there are many considerations for an enterprise-level application,
Summary of this section Basic EnvironmentIpython FoundationObjectiveThis is the first blog in 18, because boss for some of my job expectations, need to start doing some data analysis work, so began to write this series of blog. The main content of the classification is basically the landlord in view of the reading "Data anal
Where can we start with data analysis? For most friends who are familiar with the graphic work environment, spreadsheet tools are undoubtedly the first option. But the command line tool can solve the problem faster and more efficiently-and you only need to learn a little to get started. Where can we start with data analysis
The procedure of the fourth chapter of data analysis using Python introduces the basic use method of NumPy. (chapter III is the basic use of Ipython)Scientific calculations, common functions, array processing, linear algebra operations, random modules ...#-*-Coding:utf-8-*-# Python for data analysis, chapter fourth, Nu
sets. We can do the following: (click to enlarge the image) Here I give some statistics of the fans. You can do a lot of things, such as saving a fan list and making comparisons later to learn about the powder. We've shown you what you can do with your Instagram data. I hope you've learned how to use INSTAGRAMNBSP;API and have some basic ideas about what you can do with these APIs. Stay tuned for the offic
row index and the column index can be viewed as a dictionary consisting of series (using a common index). As with other similar data structures (such as data.frame in R), Dataframe row-and column-oriented operations are basically balanced. In fact, the data in Dataframe is stored in one or more two-dimensional blocks (not lists, dictionaries, or other).#-*-encoding:utf-8-*-import NumPy as Npimport pandas a
generic components 111.5.1 simple generic classes and Interfaces 111.5.2 Auto-boxing/ Unboxing 111.5.3 diamond operator 121.5.4 with restricted wildcard 121.5.5 generic static method 141.5.6 type gauge 141.5.7 type erase 151.5.8 limit for generics 151.6 function Object 16 Summary 18 Exercise 18 Reference 19 2nd Chapter Algorithm analysis 202.1 Mathematical basis 202.2 model 222.3 the problem to be analyzed 222.4 run time calculation 242.4.1 A simple
too TB-level, minicomputers should consider it. If there is a good method for general machines, you can consider it, but you must also increase the CPU and memory, it is like facing thousands of troops and horses, it is difficult to win without a single soldier. 3. Highly demanding handling methods and skills. This is also the purpose of writing this article. A good solution is the accumulation of long-term work experience of an engineer and the summary of personal experience. There are no gene
Content Profile ...The book is in the form of fiction, tells the protagonist Tawaraya too from the University of Liberal arts major after graduating into the credit, from scratch to learn the story of data analysis. The book takes the protagonist to work in the commercial Street as the stage, select close to the life of the case, the average, t test, chi-square test, correlation, regression
the tools you need, so you don't need to worry about it. Now, if you really want to use Python for big data analysis, there is no doubt you need to become a Python developer. This does not mean that you need to become a master of this language, but you need to understand Python syntax and regular expressions, knowing what is a tuple, string, dictionary, dictionary derivation,
MySQL series: Basic Data Structure of innodb Engine Analysis Over the past year, I have been analyzing database-related source code. Some time ago I analyzed the implementation of levelDB and BeansDB. There are many articles on the network analysis of these two databases, it is also relatively in-depth analysis, so the
should consider it. If there is a good method for general machines, you can consider it, but you must also increase the CPU and memory, it is like facing thousands of troops and horses, it is difficult to win without a single soldier. 3. Highly demanding handling methods and skills. This is also the purpose of writing this article. A good solution is the accumulation of long-term work experience of an engineer and the summary of personal experience. There are no general processing methods, but
Labels: Metric out scan alt Plot Distribution Cluster Analysis I. Concepts Clustering Analysis classifies individuals based on their characteristics, so that individuals in the same category have a high degree of similarity and there is a big difference between different categories. Cluster analysis belongsUnsupervised learning Clustering objects can be divided i
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