EMAIL SPAM DETECTOR USING MACHINE LEARNING

1. Useful links to fill in your lean canvas

2. INITIALIZATION

2.1. Objectives

2.2. Timeline

2.3. Roles and responsibilities

3. DATA COLLECTION

3.1. Data sources

3.2. Feature extraction

4. EXPLORATORY DATA ANALYSIS

4.1. Visualize data distribution

4.2. Identify pattern

4.3. Analyze frequency of word

5. MODEL SELECTION

5.1. Choose algorithms(Naive bayes,svm etc)

5.2. Bench marking the model performance

5.3. Model evaluation metrics(accuracy,precision,recall)

6. MODEL TRAINING AND VALIDATION

6.1. Train the models

6.2. Validate models

6.3. Cross validation

6.4. Split datas into training and testing sets

7. MODEL DEPLOYMENT

7.1. Integrate model into application

7.2. Monitor model

7.3. Create APIs

8. MAINTENANCE AND UPDATES

8.1. Performance Monitoring

8.2. Model Retraining

9. REPORTING AND DOCUMENTATION

9.1. Methodology and Code documentation

9.2. Future improvements

9.3. Results and findings