1. CAR EVALUATION
SIYANG CHEN
ECE 539 | Dec

2. 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%

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

4. 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

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

6. 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.

7. THANK YOU
SIYANG CHEN
ECE 539 | Dec