data science , data mining and forecasting business problem using weka and documentation

Hi, I have done several data mining,big-data based projects, here I carefully read your project description. I couldn't find automobile data set as you proposed attributes in your proposal but I found auto mobile data set that having this description: This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less). Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe. Attribute Information: Attribute: Attribute Range 1. symboling: -3, -2, -1, 0, 1, 2, 3. 2. normalized-losses: continuous from 65 to 256. 3. make: alfa-romero, audi, bmw, ... 4. fuel-type: diesel, gas. 5. aspiration: std, turbo. 6. num-of-doors: four, two. 7. body-style: hardtop, wagon.... 8. drive-wheels: 4wd, fwd, rwd. 9. engine-location: front, rear. 10. wheel-base: continuous from 86.6 120.9. 11. length: continuous from 141.1 to 208.1. 12. width: continuous from Thus there total 26 attributes and 206 instances. Here I can apply data mining techniques to mined knowledge from this data set and can find association between multiple attributes. Thanks, Rabia

Hi, I would love to work on this project. I am a chartered accountant (CPA equivalent) student with my result awaited for the degree. I have 8 years of experience in similar kind of work. Please contact with further details if interested. Regards Raghav Bansal

Hi, I have 5 years of professional experience in solving the type of business problems like the one you mentioned. For classification and prioritizing variables we can use advanced machine learning algos like Support vector machine and Random forest.. major advantage being no over fitting in RF and v good results typically, such algos can be easily implemented in R. We can have further discussion on any of the points if you want.

I have done project in data mining. And i know how to do all these. I can easily write this. If you need any more information ask me.

Hi there, I've used Weka extensively and should be able to help out with this project. I'm open to answer any questions that you'll have during my analysis.