General Architechture of Machine Learning
Business Understanding: Understand the give use case and also it’s good to know more about the domain for which the use cases are built.
Data Acquisition and Understanding: Data gathering from different sources and understanding the data. Cleaning the data, handling the missing data if any, data wrangling, and EDA (Exploratory Data Analysis)
Read More: My Sample Material EDA Course on Sepuluh Nopember Institut of Technology
Modeling: Feature engineering, scaling the data, feature selection. Backward elimination method, correlation factors, PCA, and domain knowledge to select the features. Model training (Based on trial and error method of by experience, we select the algorithm and train with selected features). Model evaluation (Accuracy of the model, confusion matrix and cross-validation).
Deployment: Once the model has good performance, we deploy the model in the cloud. Once we deploy, we monitor the performance of the model. If it’s good, we go live with the model or reiterate all the process until our model performance is good.
Source: iNeuronai