Adult-Census-Income

Purpose:

This project is to predict a person's salary lies in either 50K+ or 50K-.

1.1 Data Extraction

• The Adult-Census-Income is from kaggle:

  https://www.kaggle.com/uciml/adult-census-income

• Dataset Features:

  Salary, age, workclass, fnlwgt, education, education_num, marital-status, occupation, relationship, race, sex, capital-gain,capital-Loss, hours-per-week, native-country

• The Salary Feature is the label we want to predict.

1.2 Data Preprocess

• Transforming Feature

  Some Features contains string values, which can't be calculated in numpy library.
  Convert categorical values into numerical values.
  

  Method: Label Encoding, One-Hot Encoding

• Imputing Missing Values

  Some Features contain the missing values like NAN, ? or space.
  Imputate missing value via feature mode.
  

  Method: Feature Mode, Median and Mean

• Dealing with Imbalanced Data

  Salary Feature is unbalanced labeled. 
  The salary > 50k is around 80% and <= 50K is 20%.
  

  Method: Bagging and Undersampling

1.3 Model Selection

• Random Sampling of Training Data

  From training dataset, use Undersampling method by selecting a subset of the majority examples to match the number of minority examples to create a balanced dataset.

• Building Classifier

  Classification Algorithms Selected:

  K-nearest-neighborhood, Support Vector Machine, Logistic Regression, Random Forest, Navie Bayes, Decision Tree, Adaboost Decision Tree

• Ensemble Learning

  Each instance of training and test data is classified 0 (corresponding to less or equal than 50K dollars annually) or 1 (corresponding to greater than 50K annually) using the learned classifiers.

  Final prediction is made by taking a majority vote model among the predictions of these classifiers

1.4 Result

• Bagging Decision tree:

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.63	0.13
  >50K	0.05	0.19

  Prediction accuracy for instances label <= 50K is 0.83.

  Prediction accuracy for instances label > 50K is 0.80.

  Overall Test Accuracy is 0.82.

• Random Forest

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.62	0.15
  >50K	0.04	0.20

  Prediction accuracy for instances label <= 50K is 0.81.

  Prediction accuracy for instances label > 50K is 0.85.

  Overall Test Accuracy is 0.82.

• Logistic regression

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.64	0.12
  >50K	0.04	0.19

  Prediction accuracy for instances label <= 50K is 0.84.

  Prediction accuracy for instances label > 50K is 0.82.

  Overall Test Accuracy is 0.83.

• K-Neighbor

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.61	0.15
  >50K	0.05	0.19

  Prediction accuracy for instances label <= 50K is 0.80.

  Prediction accuracy for instances label > 50K is 0.80.

  Overall Test Accuracy is 0.80.

• Support Vector Machine(SVM)

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.63	0.13
  >50K	0.05	0.19

  Prediction accuracy for instances label <= 50K is 0.83.

  Prediction accuracy for instances label > 50K is 0.80.

  Overall Test Accuracy is 0.82.

• Naïve Bayes

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.61	0.15
  >50K	0.04	0.20

  Prediction accuracy for instances label <= 50K is 0.80.

  Prediction accuracy for instances label > 50K is 0.84.

  Overall Test Accuracy is 0.81.

• Ensemble: Majority Vote for 7 Learned Classifiers

  Confusion matrix:

  Prediction	Truth	Truth
  	<=50K	>50K
  <=50K	0.63	0.15
  >50K	0.04	0.20

  Prediction accuracy for instances label <= 50K is 0.82.

  Prediction accuracy for instances label > 50K is 0.84.

  Overall Test Accuracy is 0.83.