...
- Import the Data
- Clean the Data
- Split the Data into Training/Test Sets (80% training/20% testing)
- Create a Model - select an algorithm
- Train the Model
- Make Predictions
- Evaluate and Improve
Libraries and Tools
| Library | Purpose |
|---|---|
| Numpy | Multi-dimensional array |
| Pandas |
References
| Reference | URL |
|---|---|
| Python Machine Learning Tutorial (Data Science) | https://www.youtube.com/watch?v=7eh4d6sabA0 |