What is Predictive and Prescriptive Workbench?

BDB Platform comes with an end to end Predictive Workbench with Integrated Algorithms from R, Spark ML, Python, Keras + Tensorflow for Data Scientist & Citizen Data Scientist to use to create Algorithmic workflows, Prescriptive flows to give Recommendation to businesses. Apart from using standard libraries, there is a provision to write Custom Algorithms in R, Spark ML (Scala), Python and add to your workflows. It also gives Neural Network Workspace using Keras High Level APIs and Tensor flow packages, which can be leveraged with Real Time data and Event driven data to give Machine Learning and Deep Learning. Sentiment Analytics, Image Analytics and Video analytics is something that users can expect to achieve using NN Workbench. BDB data science team works on many verticals and keeps on adding more refined Algorithms and Models to the Pre-Packaged Models.

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 Workbench in Action.

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 Source Supported - It supports most of the key data sources including 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 -

    Keras + Tensorflow (Releasing Soon – Aug 18) – Train/Retrain Neural Network, Pre trained Sentiment Analysis model.

    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.

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Spark ML Workbench

R-workBench

Custom Scripts

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R Workbench

R-workBench

Python Workbench

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Use Cases For Predictive Analysis

Visualize Market Trends
Visualize Market Trends & Take Actions

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

Custom Scripts
Custom Scripts

Custom Scripts lets you write down your R/ Spark Scala/ Python / Py Spark scripts. Custom Scripts lets you create customized algorithms for R, Spark, and Python environments.

Classification Techniques
Classification Techniques

Use our classification techniques like Decision Tree or Decision Matrix, Naive Bayes, Pareto Analysis, Conjoint Analysis (Via custom R) to make optimized and well-informed decisions.

Get Relational Insights
Get Relational Insights

Uncover invisible patterns and associations to glean predictive insights into your data using our Market Basket Analysis.

Predictive Modelling
Predictive Modelling

Highly scaled (in performance) Predictive Models based on Decision Tree, clustering, Logistic Regression can be created for accurate prediction.

Machine Learning
Deploy Models in Machine Learning/ Data Pipelines

Deploy the trained predictive models in ML-based Data Pipeline and combine them with other test datasets to produce seamless insights. Visualize the predictive results through rich and interactive dashboards.

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