1. Annual Income Prediction Modeling Using SVM
Xinjue YU
12/14/2010

2. Annual Income Prediction
Why this problem?
Useful in industries such as insurance, banking, marketing, etc
Interested in the income distribution
The goal:
To predict whether a person has an annual income of more than $50,000
The information we have:
Age, gender, education level, working hours per week, etc.

3. The Dataset The Adult dataset: 32561 total with 16281 for testing
Extracted from the 1994 Census database.
A set of reasonably clean records was extracted
((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) 
databases/adult/

4. Preparation
There are 14 features in the raw dataset, using 4 out of 14
The 4 features that are used: gender, education level, aged and working hours per week
Quantizing the features
Education level: 1(<=high school), 2(<grad school) & 3(>=grad school)
Gender: 0(Female) & 1(male)
Age: 1(<30), 2(30-50) & 3(>50)
Working hours per week: 1(<=40) & 2(>40)

5. The Approach Using Support Vector Machine in artificial neural network
The data are supposed to be non-separable
Using SVM for non-separable pattern classification
Trying different kernels such as
Linear
RBF
Polynomial
Sigmoid
Using 2-D feature pairs first
gender & education level
Age and working hours per week
Using 4 features in further study (increased complexity)

6. The Expected Results
Predict a person’s annual income is whether more than 50K by the result using SVM (classification/clustering involved)
Using testing data to get the error rates of different kernels
Comparison of the results of different kernels
Linear kernels are supposed to have the highest error rate
Try to limit the error rate within 20%-30%