10+ Best Alternatives to Predictive Analytics Software in 2024
Predictive analytics software mines and analyzes historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends. … While the majority of predictive analytics software is proprietary, versions that are based on open-source technology do exist. Read More
Top Alternatives for Predictive Analytics Software
- #1Alteryx Predictive Analytics
- #2TIMi Suite Predictive Analytics
- #3IBM Watson Studio Predictive Analytics
- #4SAP Analytics Cloud Predictive
- #5SAP Predictive Analytics
- #6Information Builders WebFOCUS
- #7Board Predictive Analytics
- #8Tableau Server
- #9IBM SPSS Modeler
- #10RapidMiner Predictive Analytics
No. | Logo | Name | Website | Rating | Pricing | Reviews |
---|---|---|---|---|---|---|
1 | Alteryx Predictive Analytics | Visit Now | 4.5 | $2300 | 4670+ Reviews | |
2 | TIMi Suite Predictive Analytics | Visit Now | 4.9 | Free | 7800+ Reviews | |
3 | IBM Watson Studio Predictive Analytics | Visit Now | 4.1 | Custom | 1757+ Reviews | |
4 | SAP Analytics Cloud Predictive | Visit Now | 4 | Free | 1400+ Reviews | |
5 | SAP Predictive Analytics | Visit Now | 4 | Free | 1400+ Reviews | |
6 | Information Builders WebFOCUS | Visit Now | 4.4 | $700 | 5900+ Reviews | |
7 | Board Predictive Analytics | Visit Now | 4.4 | Custom | 1888+ Reviews | |
8 | Tableau Server | Visit Now | 4.1 | $12 | 1788+ Reviews | |
9 | IBM SPSS Modeler | Visit Now | 3.9 | $444.68 | 5700+ Reviews | |
10 | RapidMiner Predictive Analytics | Visit Now | 4.6 | $10 | 4870+ Reviews |
Alteryx Predictive Analytics
BENEFITS:
- Obtain the information needed to make the decision. This might be previous data that aid in making predictions, such as behavioral data, equipment data, social data, or financial data.
- Cleanse, combine, and integrate training data. For analytical approaches to be employed, make sure the data used to train the model is in the appropriate shape and format.
- Construction of the predictive model. Once an algorithm and initial parameter values have been chosen, an iterative process of comparing the model's prediction and the desired output will commence. This process will continue until the model is correctly predicting the training data.
- Verify the prediction model. To make sure the model is not overfitted with the training data, present the model with "unseen" historical data and compare its predictions to what transpired.
- Implement a prediction model. Provide access to incoming data for scoring while keeping track of model performance and retraining the model as necessary.
- System integration for businesses. Take action based on the predictive score.