#2 Machine Learning & Data Science Challenge 2

#2 Machine Learning & Data Science Challenge 2

Describe the general architecture of Machine learning.

1. Business Understanding:

  • Understand the given use case, and it's good to know more about the domain for which the use cases are built.

2. 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)

3.Modelling:

  • Feature Engineering - scaling the data, feature selection - not all features are important.

  • We use the backward elimination method, correlation factors, PCA and domain knowledge to select the features.

  • Model Training based on trial and error method or by experience, we select the algorithm and train with the selected features.

  • Model evaluation Accuracy of the model, confusion matrix and cross-validation.

  • If accuracy is not high, to achieve higher accuracy, we tune the model...either by changing the algorithm used or by feature selection or by gathering more data, etc.

4. Deployment:

  • Once the model has good accuracy, we deploy the model either in the cloud or Rasberry py or any other place.

  • Once we deploy, we monitor the performance of the model.

  • if it's good...we go live with the model or reiterate all processes until our model performance is good.