CAR EVALUATION SIYANG CHEN ECE 539 | Dec 07 2018

EXECUTIVE SUMMARY PURPOSE, WORK & RESULT PURPOSE Classify the value of cars into 4 classes using 6 attributes WORK PERFORMED Processed dataset Developed training algorithm Performed training RESULT Training is concluded with an accuracy of 97% 4,000 Epochs 65% 96% 94% 87% 81% 97% 95% 92% 74%

APPROACHES + DATA, PROGRAM & PLATFORM Car Evaluation Dataset - UCI Machine Learning Repository (Public) Personal PC + Spyder 3.3.1 with TensorFlow Backend

APPROACHES DOWNLOAD DATA TRAIN IN NETWORK OUTPUT & DISPLAY PROCESS DATA, PROGRAM & PLATFORM DOWNLOAD DATA TRAIN IN NETWORK OUTPUT & DISPLAY PROCESS GRAPHING Self written data processing module (one-hot command used) Configured matplotlib module with training parameters Self written, switch controlled download module Self established training network using TensorFlow backend functions Automated matplotlib output

(graph once per 50 epochs) RESULT VISUALIZED LEARNING 97% 97% Accuracy for 4,000 Epochs 2% ~ 3% Loss (graph once per 50 epochs)

DISCUSSION LESSON LEARNED Data Formatting/Cut Appropriate data formatting and separation is crucial for the process of data. Data Imbalance Significant difference in training-testing data split could reduce learning accuracy. Shuffle & Shuffle A well shuffled training and testing dataset contributes to a better accuracy significantly. Change Parameters An appropriate balance of epoch number and batch size could improve learning accuracy.

THANK YOU SIYANG CHEN ECE 539 | Dec 07 2018