predictive models built automaticaly

© Algolytics. All rights reserved. 1

Creating Predictive

Models Automatically

AUTOMATIC BUSINESS MODELER by


Overview of ABM tool

automatically builds accurate and

interpretable predictive models


EXAMPLARY APPLICATIONS

Selecting customers likely

to stop using the company’s products

(churners)





(churners)

Assessing creditworthiness

of a debtor (Credit scoring)





(churners)



Assessing the probability of the

response to a marketing

campaign (targeting)





(churners)






Predicting offer take-up –

selecting offers most likely to be

accepted by customers





(churners)









Lead scoring…



…tell us about your project

and we’ll advice you on predictive models use



(churners)









Lead scoring…

support_abm @ algolytics.pl

click to email us

mailto:[email protected]?subject=Predictive Model Question from Slideshare


Automates activities, like fast variable

selection, interaction and transformations of variables or best

model selection

Provides 3 working modes presenting different

approaches to data processing and modeling

Handles prediction and

classification problems

WHAT ABM DOES


Available via an easy-to-use SaaS web-based

interface. No need to install

anything

Provides data scoring Generates detailed

reports for each stage of the model building

Automates activities, like fast variable

selection, interaction and transformations of variables or best

model selection

Provides 3 working modes presenting different

approaches to data processing and modeling

Handles prediction and

classification problems

WHAT ABM DOES


WHY TO USE ABM?

• Lack of data scientists – addressing gap in your

workforce

• Time for modeling decreased from days to minutes

• Savings - no need to hire a team of data scientists &

analysts. Non-data scientists can learn to quickly

build accurate models using ABM

• Get better insight about your data


HOW IT WORKS?

1. Data import 2. Data split 3. Data sampling 4. Exploratory data

analysis

5. Variables

selection

6. Data quality

• Handling of missing values

• Handling of outliers

7. Variables

transformation

8. Variables

interactions

9. Variables

selection

10. Models

building and

selection

11. Reports

generation 12. Data scoring


4 steps to calculate prediction


4 STEPS TO MAKE PREDICTIONS

• Amazon Elastic Compute

Cloud

• Transferred securely using

SSL protocol

• File format: CSV

Import the source dataset

STEP 1



Parameters

• Working Mode (Quick, Advanced, Gold)

• Target variable name and its positive value (numerical, text)

• Variables’ type and role

• Sample selection method and sample size

• Stratified sampling method and proportion of positive target in the resulting sample

• Model quality measure (Lift, Accuracy, Recall, Captured Response)

• Cut off value (Lift, Captured Response)

• Classification Threshold

Run the model building process

STEP 2



• 7 reports available • Raw data

• Data split

• Data sampling

• Exploratory data analysis

• Variables selection

• Models statistics (e.g. variables significance, quality measure – statistics and graphs)

• Re-run the process with different settings and test various approaches

Review and understand the model

STEP 3


MAKE PREDICTIONS IN 4 STEPS

• Score data, so you can apply the prediction to new data (e.g. score new customers)

• Output file format: .csv • Id variable

• Predicted target value

• The probability of positive value of the target variable

Score data STEP 4

Try out

ABM tool

Visit

www.Algolytics.com click here to open

http://algolytics.com/products/automatic-business-modeler/abm-free-trial/?utm=slshabm1

Questions? Leave a comment or email us

abm_support @ algolytics.com

Visit

www.Algolytics.com click here to open

http://algolytics.com/products/automatic-business-modeler/abm-free-trial/?utm=slshabm1

predictive models built automaticaly

Software