We will follow the general machine learning workflow step-by-step:
- Problem Definition
- Data cleaning and formatting
- Exploratory data analysis
- Feature engineering and selection
- Compare several machine learning models on a performance metric
- Perform hyperparameter tuning on the best model
- Evaluate the best model on the testing set
- Interpret the model results
- Draw conclusions and document work
Top comments (4)
Is this the beginning of a series? Is this the whole article? I'm a bit confused.
Yes, This only steps for Start data science project.
You can use this steps for create project.
Soon i send article that develope data science project step by step.
You might want to mention that. This just looks like you accidentally published this prematurely.
Gentle Blogger of Data Science,
May I suggest a book.
I think Max Kuhn has a ton of great ideas in his book,
"Applied Predictive Modeling", DOI 10.1007/978-1-4614-6849-3.
It's worth a look. :)