Building and Evaluating Predictive Models using SAS Enterprise Miner

BUILDING AND EVALUATING PREDICTIVE MODELS USING SAS MINER ENTERPRISE

Building and Evaluating Predictive Models using SAS Enterprise Miner

1. Setting up the project and exploratory analysis

2. Decision tree-based modelling and analysis

3. Regression-based modelling and analysis 4. MODEL COMPARISION

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Building and Evaluating Predictive Models using SAS Enterprise Miner

SAS MINER DIAGRAM FOR THE BMW DATA SET

1. Setting up the project and exploratory analysis B. STATS EXPLORE

The key focus points provided by the stat explorer are following: • Year has a positive correlation with the price and whereas a higher reading of the odometer has a negative impact on the price.

• Both year and odometer are the most suitable variables.

2. Decision tree-based modelling and analysis

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Building and Evaluating Predictive Models using SAS Enterprise Miner A. DECISION TREE-1 (TWO WAY SPLIT)

DECISION TREE-2 (THREE WAY SPLIT)

I.

How many leaves are in the optimal tree?

OPTIMUL LEAVES (DECISION TREE-1) ARE 32 OPTIMUL LEAVES IN DECISION TREE-1 (DECISION TREE-2) THERE ARE 38 LEAVES IN THE DECISION TREE-2

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Building and Evaluating Predictive Models using SAS Enterprise Miner

II.

Which variable was used for the first split?

FIRST SPLIT IN BOTH OF THE DECISION TREE IS YEAR III.

What were the competing splits for this first split?

DECISION TREE-1 (FIRST SPLIT NODE)

SPLIT NODE-1 (DECISION TREE-2)

2. Which of the decision tree models appears to be better? Justify your answer B. SAS HAS AUTOMATICALLY SELECTED THE DECISION TREE-2 BUT WE CAN ALSO SAY THAT THE DECISION TREE-2 HAS THE LEAST AVEREAGE SQUARED ERROR OF BOTH THE TREES, WHICH MAKES IT OPTIMAL THAN THE OTHER TREE.

C. I. The variables with good predictive performance.

• split= condition->0, node= <0.5, node ID= 11 • split= condition->0.8, node= >0.5, node ID-> 24 The variables with bad predictive performance.

• Split= convertible, node= >0.5, node id= 74 • Split= convertible, node =<0.5, node id= 47 ii. the good splits rectify that they consist of sufficient amount of data. iii. For this selection there are mainly 2 rules for selecting good performance leaf node.

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Building and Evaluating Predictive Models using SAS Enterprise Miner The split leading to the validation should be good and also should include sufficient data in the leaf node. This means that the paths which are darker, define the path of the data and the denser blue the leaves are, the better they are.

3. Regression-based modelling and analysis A. IMPUTE SUMMARY

As seen in the figure above there are 8 variables which have been imputed. The imputation was done before the generation of the decision trees. c.

REPLACEMENT COUNT

D. Data exploration

It is clear from the above figure that what best variables SAS has selected by the use of variable selection node. We can see that year has the maximum impact on the pricing.

VARIABLE CLUSTERING

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Building and Evaluating Predictive Models using SAS Enterprise Miner

E&F. REGERRISION MODEL

I. the following are best variables choosen by SAS for the purpose.

The selected model, based on the error rate for the validation data, is the model trained in Step 17. It consists of the following effects: Intercept IMP_paint_colororange IMP_paint_colorred IMP_paint_coloryellow IMP_title_statuslien IMP_transmissionmanual M_condition0 M_cylinders0 M_drive0 M_odometer0 REP_IMP_condition0_8 REP_IMP_cylinders10 REP_IMP_cylinders3 REP_IMP_cylinders8 REP_IMP_drive0_5 REP_year typeconvertible typecoupe

II. ASE OF THE REGERRISON MODEL

The average squared error is the gap between the actual value and the estimated value as per the model.

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Building and Evaluating Predictive Models using SAS Enterprise Miner

4. MODEL COMPARISION

SAS has chosen the decision tree model as the best. And it justified by having a view of the fit stats in which the tree-2 model has the least ASE of all.

B. The creation of different models let us help in comparing all the models on different factors and also SAS helps us to identify the best model of all.

C. SCORE PRDICTED PRICE

SUBSETING

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Building and Evaluating Predictive Models using SAS Enterprise Miner

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