Diving Into the ML Process | Towards AI
The Machine Learning Process
The machine learning process includes 4 main stages:
1. Problem Framing
Define your project goals. What do you want to find out? Do you have the data to analyze?
This is where you decide what kind of problem are you trying to solve e.g. model to classify emails as spam or not spam, model to classify tumor cells as malignant or benign, model to improve customer experience by routing calls into different categories so that calls can be answered by personnel with the right expertise, model to predict if a loan will charge off after the duration of the loan, model to predict price of a house based on different features or predictors, and so on.
2. Data Analysis
Collect and refine your data. Prepare a repository to store your data
This is where you handle the data available for building the model. It includes data visualization of features, handling missing data, handling categorical data, encoding class labels, normalization and standardization of features, feature engineering, dimensionality reduction, data partitioning into training, validation and testing sets, etc.