Need to predict who is likely to respond to a direct marketing piece and who is likely to buy.
Data mining with RapidMiner
Doctoral candidate requesting a Rapid Miner professional experienced with text data mining. Must be certified and will need to provide evidence of certification.
Looking for help with trend/topic detection in a ~2000 record dataset. 1 column/field. As many as 400-500 words in a record. See attached data sample.
The data is extracted from a Help Desk database. I'm interested in data mining, trend/topic detection for column F (Description).
Looking for someone to do the mining tasks. I can do data cleansing. I'm looking for a partner that I can discuss the project with. I'll tell you what I'm trying to do, you tell me how you need the data cleaned and prepped, then I'll tell you what (I think) I need from mining round 1. You provide the results and details of the methodology, I'll review and request a second round. We'll do this a few times. My ultimate goal is something like a ranked list of topics reported to the help desk.
As this is for a doctoral dissertation, I'll need clear details on exactly what steps you executed.
Data is .csv or Excel, or anything else you need. Attached example is in Excel.
Deliverables are the results of the initial analyses and some secondary analyses. Final deliverable is a list of trends across the records in the dataset, with frequency (etc) information. Excel or Word or whatever format you like. Will need to discuss analyses and results with the miner.
When providing proposal, please consider this is a student project, and not a commercial project. Please submit proposal accordingly.
I am a doctoral candidate and paying for this out of pocket. It's not a huge pay day but it's not much work either! I can provide a research assistant credit if you'd like. (Dates in the posting are approximate).
Amway operates in more than 100 countries and is ranked 29th among the largest private companies in the United States. We have been collecting customer reviews and survey data that has been transcribed from phone conversations in different languages. We would like a data scientists well-versed in natural langage processing (NLP) to engage in sentiment analysis of this data.
The customer review data is in unstructured format and contains approximately 2,000 records. The initial data set is in the Chinese language and surveys from additional languages will be added after the success of the first phase of this project. A data sample is attached.
The data will be analyzed by a RapidMiner-certified data scientist and the analysis results will be exported to Tableau.
We would like to look at the analysis by the slices listed below. For example can we see the sentiment between months, or in different regions, or by different SKUs, etc.
Be able to slice analysis by:
The above represents some of our ideas but we encourage data scientists to suggest other approaches. Please look at the sample data and provide your approach to the analysis and the kind of insights that can be drawn from it. Please also provide a ball-park estimate of hours this work may take.