RapidMiner Homework Help

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RapidMiner Basics by RapidMiner Homework Help Experts

RapidMiner is designed and intended to help and utilize information mining devices and tools. This basic intuitive interface makes it simple to assemble hearty information mining models. It additionally enacts prescient investigation and visual examination to information specialists and experts. It manages unstructured information, for example, content records, web traffic logs, and pictures. It is utilized for business and business applications just as for look into, instruction, preparing, quick prototyping, and application improvement

The individuals who are looking to digest and abstract from big and large data and information, utilize the RapidMinor tool. The essential component of the genuine RapidMiner is the Predictive Analytics System and expands prescient investigation. The RapidMinor framework could undoubtedly control the volume identified with huge information without making a solitary line of code.

Topics Covered in RapidMiner Homework Help in Easy Steps

• SAS
• SPSS
• PHStat
• STATA
• MegaStat
• Business Statistics

Rapid Minor Analytical Engine

• In-memory
• In-Database
• In-Hadoop

Hadoop Tool RapidMiner Homework Help by Urgenthomework

RapidMiner pulls and gets its learning plans and furthermore quality evaluators through Weka, and as to genuine showing, gives possibly the neighborhood Rapid-I scripting condition or even the R language. AI, data mining, and substance mining, prescient examination, it is outfitted to oversee them. Made in Java, this continues running on each and every essential framework and moreover working framework. The Hadoop Tool RapidMiner Homework Help is a brilliant reaction as to overseeing unstructured data, for instance, content records, web development logs, and furthermore pictures. The grouping edge identified with colossal data doesn't act as new issue towards the system. The volume identified with huge data could easily manage, without making a solitary line of code.

Online RapidMiner Assignment Help at By Bigdata Experts

RapidMiner gives adaptable approaches to manage discarding any confinements all through educational assortment measures. Likely the routinely used engine identified with RapidMiner may be the In-Memory motor, in which data will be completely stacked in memory and is explored there and various motors. In-Memory, The proper stockpiling component identified with RapidMiner is in-memory data stockpiling, to a huge degree progressed concerning data complete to normally concerning logical assignments. The in-memory assessment is without a doubt the quickest method to gather diagnostic models. Instructive list gauge fixed through gear; the more arduous memory can be obtained the more huge data models which may look at. Instructive file gauge: On not very lousy hardware, up to ca. 100 million data focus.

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Sample RapidMiner Homework Help Solved by Experts

Question 1. Description of the dataset assigned to the student. 

• How many features does the dataset have? Is there a target variable? Explain. (200 words max.)
• How many features are numerical, textual, categorical, or Boolean? Is there any evident dependency between the features? Explain? (200 words max.)
• Is this a supervised or unsupervised data mining task? Explain (max. 200 words)

Question 2. Data quality and data cleansing.

• Are there missing values in the data? Explain the most common techniques used to deal with missing values in a dataset. Show how you would perform this analysis in RapidMiner. Explain. (200 words maximum, including screenshots)
• Are the classes balanced? Explain the most common techniques used to deal with the class imbalance problem. Explain. (200 words max.)
• Are there outliers in the dataset? Show how you would perform the analysis in RapidMiner Explain. (200 words maximum, including screenshots)

Question 3. Charting. 

Select three charts from Learning Unit 2 in the workbook, plot the dataset with RapidMiner using these three charts, and describe what you see in your own words. (maximum of 2 pages, including screenshots)

Question 4. Data analysis. 

Once you have identified the type of problem you have, produce screenshots showing the RapidMiner workflow you defined to analyze the data with RapidMiner. Explain the result and compare the algorithms when appropriate. (2 pages max.) - Unsupervised problem: analyze the data with k-means, DBSCAN, and k-medoids. Explain which of the algorithms performs the best in identifying groupings. - Supervised problem (classification): use k-NN, naive Bayes, and logistic regression and calculate the accuracy of the classification. - Supervised problem (regression): using linear regression and polynomial regression, explain which of the two algorithms performs the best using root mean squared error (RMSE) as the evaluation metric.