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
Latest comments (4)
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. :)
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.