In my university, we used Weka. Weka was used in marketing by my professor, I was preparing a presentation for PhD proposals specifically on energy consumption and renewable resource utilization. So, I needed data mining tools.
I run the method "Association rules" in Weka, and also regression trees like RandomForest. Weka is written in Java like other standalone programs but as I don't know Java, I don't use its "Simple CLI" application. In Weka, I use menu options. I am mainly a programmer in R and although Weka has parsers for R, I don't use them. If I didn't know any language, Weka would be my solution. But as I know R and Python, Weka is not my first solution although I acknowledge that for methods that I don't usually handle, Weka is the quickest way to approach them. If the user is not a programmer, Weka is the best option to approach machine learning.
I used Weka for my Master's thesis. I've used it a couple of times for my personal usage or a quick analysis or graph. You can do a reselection quicker and you can get the graph and put it in our report and do classification. If any project is present, I could develop it.
I work a lot with university students. One of the latest projects I did was related to a classification problem. I had to use different algorithms such as neural networks, Support Vector Machines, nearest neighbor algorithm, decision trees — those types of different algorithms in order to do the machine learning parts. I can't remember the exact data set that I recently worked with, but when it comes to machine learning and data mining, I have worked with different data sets. I use many algorithms in Weka in order to train and test those data sets.
I've handled different projects with this solution. After college, I've handled different projects. The most recent project that I handled was for a company from India. They were looking for a measure classification in regards to the type of engines that cars have, and the pollution levels that they have. There was a mixture of text data that had to be classified. There was the need to transform the text data to a data type that would be easily classified. When employing text data you can't do classification directly. I had to clean the data and program all the variables to suit the required information.
My domain is pure data analysis and data science machine learning. The first time I used Weka, five years back, I did a research project. I prefer to work with Weka whenever I have small and clear projects. Weka is a very nice tool and it helped me to solve any machine learning problem in one minute. In case of machine learning algorithms, classification, or support machines, I used to use this tool to implement those algorithms. Whenever I get any work on any other platform suppose in hours. So what I initially do, I ran the data set in the Weka platform first. It gives me a clear view that this data set has certain attributes and offers some observations. I can implement different machine learning algorithms if this is a classification. I use two or three algorithms. If we find that the performance of the logistic regression is good then I can implement those in other platforms also. Weka is a good tool for any analysis. There are some missing values there. We can replace the missing values using the mean values. I use that filter to see which names were replaced. It's in the filter, then we have to go to that unsupervised, then replace missing values. I use that filter to replace missing data. Weka has the option to check important attributes. I use that internally, I found that everything is important. Then initially I applied my dataset to implement the classification problem. There is less demand for projects that require Weka as opposed to R or Python.
Solution Architect / Data Scientist (upwork) at Freelancer
Real User
Nov 10, 2020
Weka is a machine learning tool where we can use supervised and unsupervised learning tools to detect anomalies, for clustering, or classification algorithm. The deployment method depends on the business's requirements. When I worked at the Air Force, it was all cloud. I deployed it on the cloud but that was treated as on-premise because that is confined within the Air Force. It depends upon the requirement of the user. If they want it on-premise, I can provide that. If they want it to be hosted on AWS or any other cloud services, that can also be done.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
In my university, we used Weka. Weka was used in marketing by my professor, I was preparing a presentation for PhD proposals specifically on energy consumption and renewable resource utilization. So, I needed data mining tools.
I run the method "Association rules" in Weka, and also regression trees like RandomForest. Weka is written in Java like other standalone programs but as I don't know Java, I don't use its "Simple CLI" application. In Weka, I use menu options. I am mainly a programmer in R and although Weka has parsers for R, I don't use them. If I didn't know any language, Weka would be my solution. But as I know R and Python, Weka is not my first solution although I acknowledge that for methods that I don't usually handle, Weka is the quickest way to approach them. If the user is not a programmer, Weka is the best option to approach machine learning.
We are using Weka for machine-learning purposes.
I mainly use Weka to check data for anomalies.
I used Weka for my Master's thesis. I've used it a couple of times for my personal usage or a quick analysis or graph. You can do a reselection quicker and you can get the graph and put it in our report and do classification. If any project is present, I could develop it.
I have only used Weka for classification and clustering. I have also used classification with embossing.
I work a lot with university students. One of the latest projects I did was related to a classification problem. I had to use different algorithms such as neural networks, Support Vector Machines, nearest neighbor algorithm, decision trees — those types of different algorithms in order to do the machine learning parts. I can't remember the exact data set that I recently worked with, but when it comes to machine learning and data mining, I have worked with different data sets. I use many algorithms in Weka in order to train and test those data sets.
I mainly use this solution for regression trees, and for association rules. Also, some descriptive statistics because they are very easy.
I've handled different projects with this solution. After college, I've handled different projects. The most recent project that I handled was for a company from India. They were looking for a measure classification in regards to the type of engines that cars have, and the pollution levels that they have. There was a mixture of text data that had to be classified. There was the need to transform the text data to a data type that would be easily classified. When employing text data you can't do classification directly. I had to clean the data and program all the variables to suit the required information.
My domain is pure data analysis and data science machine learning. The first time I used Weka, five years back, I did a research project. I prefer to work with Weka whenever I have small and clear projects. Weka is a very nice tool and it helped me to solve any machine learning problem in one minute. In case of machine learning algorithms, classification, or support machines, I used to use this tool to implement those algorithms. Whenever I get any work on any other platform suppose in hours. So what I initially do, I ran the data set in the Weka platform first. It gives me a clear view that this data set has certain attributes and offers some observations. I can implement different machine learning algorithms if this is a classification. I use two or three algorithms. If we find that the performance of the logistic regression is good then I can implement those in other platforms also. Weka is a good tool for any analysis. There are some missing values there. We can replace the missing values using the mean values. I use that filter to see which names were replaced. It's in the filter, then we have to go to that unsupervised, then replace missing values. I use that filter to replace missing data. Weka has the option to check important attributes. I use that internally, I found that everything is important. Then initially I applied my dataset to implement the classification problem. There is less demand for projects that require Weka as opposed to R or Python.
Weka is a machine learning tool where we can use supervised and unsupervised learning tools to detect anomalies, for clustering, or classification algorithm. The deployment method depends on the business's requirements. When I worked at the Air Force, it was all cloud. I deployed it on the cloud but that was treated as on-premise because that is confined within the Air Force. It depends upon the requirement of the user. If they want it on-premise, I can provide that. If they want it to be hosted on AWS or any other cloud services, that can also be done.