Anti-spam emails filtering

1. Define Model

1.1. The project's goal: classifing the spam and non-spam.

1.2. Users: Individuals using emails frequently

1.3. Functional Need: classifing the spam and non-spam

1.4. Non-functional Need: working accurately and in time

2. Empathize Model

2.1. Think in others' shoes and know their real needs

2.2. Select the spam and non-spam

2.3. Delete the spam automatically

2.4. Do not make any mistake

2.5. Have access to check the mailbox

3. Flow Diagram

3.1. BIG DATA: 1) Websites, like kaggle 2) Mobile devices, like personal email boxes 3) Internet 4) Scientific field and research

3.2. Features: 1) Physical Attributes 2) Psychological Attributes 3) Social Attributes 4) Survice Attributes

3.3. Classification Problem using Naive Bayes as the algorithm

3.4. Demonstration of functions

3.5. Make predictions

4. Machine Learning

4.1. Problem Position: Classification Problem

4.2. Algorithm: Naive Bayes

4.3. Procedure: BIG DATA---Features---Label---Machine Learning Algorithm---Function---Predict Model