IBM SPSS Modeler Reviews

1. Adequate learning resources and extensive documentation.

2. Supports various models and is easy to implement them.

3. Easy integration with other IBM tools like Watson

4. People with less exposure to coding can use it efficiently.

1. Complicated GUI, improvements are required in this aspect.

2. A bit pricey when it comes to multiple teams.

3. Limited integration with other languages like Java or Python

Optimization of manufacturing equipment throughput (OEE->Overall Equipment Effectiveness). Prediction of cycle times of products based on past information and allocation of resources based so that idle time of resources could be reduced and throughput maximized.

IBM SPSS Modeler is an impressive and comprehensive data analysis tool that caters to the needs of both novice and experienced data scientists. Its array of features and functionalities make it a powerful choice for those seeking to extract valuable insights from their data. While it's not without its limitations, its strengths far outweigh its weaknesses.

SPSS Modeler boasts an intuitive interface that is relatively easy to navigate, even for users with limited coding experience. This accessibility makes it a great option for users who want to delve into data analysis without getting bogged down by complex programming.

While the interface is user-friendly, mastering some of the more advanced features can still be challenging. In-depth understanding of statistics and machine learning concepts is required to utilize the software to its full potential.

he software provides an extensive selection of machine learning and statistical algorithms, allowing users to explore various modeling techniques. From decision trees and clustering to neural networks and time series analysis, SPSS Modeler covers a wide spectrum of analytical approaches.

One of its standout features is its robust data preparation tools. With a graphical interface for data cleaning, transformation, and manipulation, users can efficiently preprocess their data without the need for external tools.

SPSS Modeler includes AutoML capabilities, making it easier to find the most suitable model for a given dataset. This feature is particularly beneficial for users who are new to machine learning and may not have the expertise to manually select and tune models.

IBM SPSS Modeler is a program for statistical analysis and data processing. The software also offers strong data analysis capabilities, enabling you to do the most traditional statistical analyses. Because the SPSS modeler can accept various data types as input, you can handle the work yourself without taking additional steps to modify the data before using it.

The GUI is well-designed and user-friendly; practically anyone can use the program with minimum investment in training.

Escalability allows you to increase your licensing expenditure by your actual needs, from a single annual authorized user to unlimited concurrent users and Big Data and Machine Learning capabilities.

A little expensive when considering multiple teams.

Complicated user interface; changes are needed in this area.

It can occasionally make data transformation more complicated.

Incompatibility with Java or Python programming languages, SQL management tools, or both.

Customer Segmentation, Churn Prediction

Porque puedo hacer una base de datos completa y de esta manera describir analizar y modelar datos

Lo que no me gusta aún es que en ocasiones no puedo editar algunos gráficos con requisitos que piden las revistas para publicar investigaciones

Pues me ayuda a modelar datos en salud y de esta manera tener decisiones en relación a problemáticas de salud

Intrectional visualization is based on both drop-down and scripting. This feature makes it very strong

Slow process while preparing complex tabulation. It should be more user friendly and fast

I used to develop Statistical Models i.e., Advanced Regression, Classification, and time series Models

It's produces very good visualizations of statistic results.

It's requires abit of time to know how to use especially for people with limited knowledge in Statistics.

It has helped to solve data analysis assignment requiring detailed structured equation modelling.

I thoroughly enjoyed working with IBM SPSS. It was one of the tool I selected to work on for my Master Project. It's so easy to understand and work with as Data Analytics. It gives a nice visual dashboard and also helps in prediction with easy drag and drop tool. completely enjoyed working with!

It was a little bit of unease to arrange the formula.

Of course! I have already recommended many of my classmates who are working for their Master projects and also the company where I have interned.

I was analysing, predicting and visualizing a set of a dataset as data analytics. As soon as had an understanding of using the tool, I was able to save time and be more productive because of its AI features .

Can get the useful insights and predictions in very less period of time.Drag and Drop options allow us to implement models faster and to evaluate models performance and model fitting in very less period of time.

solutions are expensive to deploy.Can't tune model much .. cant pre-process dataas much as we can perform using numy and pandas.

To analyze data IBM SPSS Modeler is used,smooth modeling, Quick Analysis option. Large dataset capability.Easy to export the information out to other forms.SPSS should be easier to interpret with screen readers for disabled users.

It's easy to append and merge heavy CSV format files

Sometimes its may be bags when you using distinct function

Dont be afraid to use a combination of several nodes

Making the segmentation and clustering the data into several groups

You barely need any knowledge of coding to be able to use SPSS Modeler. Just with a little bit of know-how of things, you can already start building prediction and segmentation models. It's super light-weight so most of the models you can run on the laptop instead of having to deploy them on a server. Drag and drop interface, and connecting together the data flow is amazing.

Deploying a stream on a SPSS server is not that straight-forward

Building prediction model for the business. Managed to get things off to a very quick start.