How the EasyPrediction works?

The EasyPrediction is a Naive Bayes Algorithm used for the prediction of particular targets based on historical series. EasyPrediction has many use cases that could be applied for prediction: from medical (if patient has a particular disease or not) to social or business purpose (if someone wants to buy a product or not). In the follow an example of Naive Bayes Algorithm application.

In the follow tables you can see the training tables (historical series), the testing tables (what we want to predict), the math procedure solutions and the prediction of defect products. The greater historical series records and more accurate is the prediction value. So, when a prediction is made, the next step is to observe the real event and put it into training table for growing the historical records.

See in the bottom the predicted value

Try to change the testing set by yourself and see the predicted value

You can also import your own historical series and make the prediction you want: just export the historical data training and use it for template of your own data schedule

IMPORTANT!: you don't have to import any reserved data. Only sample data for understanding how the algorithm works. This service is published only for educational purposes and all other purposes are prohibited.

You can get your personal service prediction by a payment of a monthly fee. Contact us for more informations: info@interofthings.com

This work is only an example...

you can create new prediction as you want. Just contact us: info@interofthings.com

Importing historical series
Try to change testing table...
Attribute1 Attribute2 Attribute3 Attribute4 Attribute5 NumAttribute1 NumAttribute2 NumAttribute3 NumAttribute4 NumAttribute5 DateAttribute Target

Training Table Sample

Attribute1 Attribute2 Attribute3 Attribute4 Attribute5 NumAttribute1 NumAttribute2 NumAttribute3 NumAttribute4 NumAttribute5 DateAttribute Target Created
BIGPRODUCTCUSTOM200020000-00-00 00:00:00PAID2019-04-26 16:50:27
SMALLSERVICECUSTOM3500040000-00-00 00:00:00PAID2019-04-26 16:50:27
MIDDLESERVICECUSTOM5000140000-00-00 00:00:00UNPAID2019-04-26 16:50:27
BIGSERVICECUSTOM40000140000-00-00 00:00:00PAID2019-04-26 16:50:27
SMALLPRODUCTSTANDARD20000330000-00-00 00:00:00PAID2019-04-26 16:50:27
MIDDLEPRODUCTSTANDARD7000250000-00-00 00:00:00PAID2019-04-26 16:50:27
BIGPRODUCTSTANDARD12000480000-00-00 00:00:00PAID2019-04-26 16:50:27
SMALLSERVICESTANDARD12500020000-00-00 00:00:00UNPAID2019-04-26 16:50:27
MIDDLESERVICESTANDARD800360000-00-00 00:00:00PAID2019-04-26 16:50:27
BIGSERVICESTANDARD40000480000-00-00 00:00:00PAID2019-04-26 16:50:27
MIDDLEPRODUCTCUSTOM600030000-00-00 00:00:00PAID2019-04-26 16:50:27
SMALLPRODUCTCUSTOM4000430000-00-00 00:00:00PAID2019-04-26 16:50:27
BIGPRODUCTSTANDARD2000330000-00-00 00:00:00PAID2019-04-26 16:50:27
BIGSERVICECUSTOM4000120000-00-00 00:00:00PAID2019-04-26 16:50:27
MIDDLESERVICECUSTOM5000210000-00-00 00:00:00PAID2019-04-26 16:50:27
MIDDLESERVICECUSTOM1000320000-00-00 00:00:00PAID2019-04-26 16:50:27
BIGPRODUCTCUSTOM2000230000-00-00 00:00:00PAID2019-04-26 16:50:27
SMALLPRODUCTCUSTOM6000120000-00-00 00:00:00PAID2019-04-26 16:50:27
BIGPRODUCTCUSTOM4000320000-00-00 00:00:00PAID2019-04-26 16:50:27

Testing Table Sample

Attribute1 Attribute2 Attribute3 Attribute4 Attribute5 NumAttribute1 NumAttribute2 NumAttribute3 NumAttribute4 NumAttribute5 DateAttribute Target Created
0000-00-00 00:00:00TO PREDICT...2019-07-29 19:36:32

MATHS CALCULATIONS

Array ( [BIGT1] => 0.47058823529412 [SMALLT1] => 0.23529411764706 [MIDDLET1] => 0.29411764705882 [PRODUCTT1] => 0.58823529411765 [SERVICET1] => 0.41176470588235 [CUSTOMT1] => 0.64705882352941 [STANDARDT1] => 0.35294117647059 [T1] => NAN ) Array ( [BIGT2] => 0 [SMALLT2] => 0.5 [MIDDLET2] => 0.5 [PRODUCTT2] => 0 [SERVICET2] => 1 [CUSTOMT2] => 0.5 [STANDARDT2] => 0.5 [T2] => NAN )
TargetAttribute1Attribute2Attribute3Attribute4Attribute5

T1: PAID

T2: UNPAID

#T1: 17

#T2: 2

#T1+#T2: 19

P(PAID): 0.89473684210526

P(UNPAID): 0.10526315789474

P(BIG|PAID): 0.47058823529412

P(SMALL|PAID): 0.23529411764706

P(MIDDLE|PAID): 0.29411764705882

P(BIG|UNPAID): 0

P(SMALL|UNPAID): 0.5

P(MIDDLE|UNPAID): 0.5

P(PRODUCT|PAID): 0.58823529411765

P(SERVICE|PAID): 0.41176470588235

P(PRODUCT|UNPAID): 0

P(SERVICE|UNPAID): 1

P(CUSTOM|PAID): 0.64705882352941

P(STANDARD|PAID): 0.35294117647059

P(CUSTOM|UNPAID): 0.5

P(STANDARD|UNPAID): 0.5

P(|PAID): 0

P(|UNPAID): 0

P(|PAID): 0

P(|UNPAID): 0

Avg(T1): 11223.529411764706

Avg(T2): 65000

DevStand(T1) :13773.14095494219

DevStand(T2) :84852.8137423857

Numeric Attribute:

P(NUM_A1|PAID): 2.0782101653146E-5

P(NUM_A1|UNPAID): 3.5061219249975E-6

Avg(T1): 24.764705882352942

Avg(T2): 8

DevStand(T1) :15.126671030685776

DevStand(T2) :8.48528137423857

Numeric Attribute:

P(NUM_A2|PAID): 0.0069049164207183

P(NUM_A2|UNPAID): 0.030146052572583

Avg(T1):

Avg(T2):

DevStand(T1) :

DevStand(T2) :

Numeric Attribute:

P(NUM_A3|PAID): NAN

P(NUM_A3|UNPAID): NAN

Avg(T1):

Avg(T2):

DevStand(T1) :

DevStand(T2) :

Numeric Attribute:

P(NUM_A4|PAID): NAN

P(NUM_A4|UNPAID): NAN

Avg(T1):

Avg(T2):

DevStand(T1) :

DevStand(T2) :

Numeric Attribute:

P(NUM_A5|PAID): NAN

P(NUM_A5|UNPAID): NAN

Prediction Set

TextAttribute1:

TextAttribute2:

TextAttribute3:

TextAttribute4:

TextAttribute5:

NumAttribute1:

NumAttribute2:

NumAttribute3:

NumAttribute4:

NumAttribute5:

target1

PROD P(An|PAID): 1

target2

PROD P(An|UNPAID): 1

Predicted Value

PROD P(An|T1): 0.89473684210526

PROD P(An|T2): 0.10526315789474

PREDICTED VALUE IS PAID : 0.89473684210526