BDB Predictive and Prescriptive Workbench

A seamless web based visual drag & drop Data Science tool assimilating the power of Algorithms provided by R, Python, Spark ML, Keras & TensorFlow with rich dashboarding and ML Pipeline capabilities.

Augment business decisions by unifying Data Science/ Machine Learning/AI with Data Integration and Business Intelligence.

Collate data through data pipelines, transform feature sets, train/test, compare and deploy ML models into ML pipelines, consume as an ML service within dashboards. Empower frontline decision makers with valuable insights to forecast, segment, discover data patterns and anomaly’s, understand user sentiments, classify and optimize. ‘Predict’ what will happen in each scenario based on the meaningful analysis of the past. Analyze the ‘How?’ and ‘Why?’ factors behind each move to decide the next probable business strategy.

How to Use BDB Predictive Model?

  • Experience the most intuitive and interactive predictive models. BDB Predictive Workbench, a web based UI, brings together advanced analytics spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, and machine learning.
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The Predictive Analytics workbench

Target Determination
Connect to a Data Source

Determine what you want to predict or understand. Define the aspect on which you want to use BDB Predictive Workbench.

Data Acquisition & Preparation
Data Preparation – transform, cleanse and format the data set

Acquire data in any form and modify to make it analysis ready. Use inbuilt allied components to prepare and transform data.

Prediction Building
Prediction building

split data into train/test, drag & drop components in a web UI, configure algorithms, execute, view summary & results , compare their performance, save the workflow or model and deploy as a service

Captivating Visualization
Captivating Visualization

Display the predicted results through rich and interactive visuals for well-informed insight.

Watch BDB Predictive Work Bench in Action.

Target Determination
HR Advanced Analytics

Predict the salary of an employee based on his/her skill and experience.

Data Acquisition & Preparation
Spark ML recommendation engine

Product recommendation using Spark ML – ALS

Prediction Building
Credit card scoring

Predict credit worthiness/score for loan applicants

Captivating Visualization
3.5 Release

New features which have been released in 3.5 release.

Salient features of Predictive Analytics

  • Workbench User Interface - Drag and drop panel for the user to easily configure and build a predictive workflow.

    Component Properties - Configure the independent/dependent columns, features/labels and allied algorithm properties through the Component Tab.

    Console – Keeps you updated on the progress of the workflow at real time.

    Summary – Key statistical information provided by the algorithms are displayed here, once the algorithms complete execution.

    Results – view the predicted output in a data grid.

    Visualization – Visualize the results using Bizviz charts. Integration with third party charts are also supported – RBokeh and Matplotlib

  • Data Sources supported - CSV, RDBMS, Cassandra, Elastic Search, Spark SQL
  • Data Preparation - filters, basic transformation, normalization, split, formula, data type definition, missing value replacement, sampling. Specifically for Spark - String indexer, Rformula, PCA, Chi Square, index to string, Spark SQL transformer, group by.
  • Save and reuse workflows - once you are convinced with the workflows, you can go ahead and save it for later use.
  • Save only the models - Provision to only save the spark/R/Python models (.rda, spark model) for reuse in machine learning pipelines.
  • Supported Algorithms -

    R - Clustering – Kmeans, Forecasting – Single/Double/Triple/ARIMA, Auto Forecasting, Market Basket Analysis, Regression - Linear/Multiple/Logistic, Outliers – inter quartile range, Classification – CNR/NB, Correlation, XG Boosting.

    Spark - Clustering, Classification – NB, Decision trees, Random Forest, Recommendation engine using Alternate least square (ALS)

    Python - Regression – linear, multiple, logistic

    You can split the data into training/testing sets, prepare the data, apply it to multiple algorithms, perform validations, compare performance, save the appropriate model for production use, deploy it as a service for dashboard/business story consumption and also save it into RDBMS/Cassandra.

  • Create R/Spark Scala/Python scripts - Furthermore, the Custom R/Scala/Python Script component facilitates the user to script custom algorithms and functionalities.
  • Data Writer - functionality to save the predicted data into an RDBMS/Cassandra
  • Share the workflows/scripts with other Bizviz Platform users/groups - Saved workflows and scripts can be shared with other users and groups with appropriate permission levels.
  • Scheduling - Provision to schedule the workflow on a timely basis.


Spark ML Work Bench


Custom Scripts


R Work Bench


Python Work Bench


Use Cases For Predictive Analysis

Target Determination
Identify data patterns and key relations across parameters

Using our Regression Analysis capabilities like Linear Regression, Multiple Linear Regression you can instantly predict market trends, foresee changes in demand and supply

Data Acquisition & Preparation

Custom Script feature lets you write your own R/Spark Scala/Python/Py Spark scripts

Prediction Building

By using our classification techniques like Decision Tree or Decision Matrix, Pareto Analysis, Conjoint Analysis (via custom R).It enables decision-makers to make optimized and well-informed decisions.

Captivating Visualization

Market Basket Analysis, Naive Bayes capabilities lets you uncover invisible patterns and associations within all types of data and transform them into predictive insight in order to aid frontline decision makers.

Captivating Visualization

Users can create rich, interactive data visualisations and will helps in gathering insights and improve business decision making.

Captivating Visualization

Decision tree Modelling, Clustering, Logistic Regression can be adopted to predict market trends and price vitality for creating customized offers for each segment and channel.

Captivating Visualization
Data processing for prediction building

Predicted data can be combined with other data sets to create rich interactive dashboard visualizations.

Captivating Visualization
Deploy models in Machine learning/Data pipelines

productionize models by deploying them in data/machine learning pipelines.

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