Machine Learning Project Phases¶
Typical Steps in a Machine Learning Project:¶
Let's go through the different states of a machine learning project!
1. Problem Definition¶
In this phase, we define the problem that we want to solve & determine the goals of the project
2. Data Collection¶
During this phase of the project, we gather the relevant data that will be used to train & test the model
3. Data Preprocessing¶
During this phase, we conduct various data preprocessing procedures that will make it suitable for data analysis
4. Data Exploration¶
During this phase of the project, we explore our dataset using various statistical and visualisation analysis techniques in order to gain some insight into our data
5. Feature Engineering¶
During this phase, we select relevant features that will be used as input into our model or create some additional features in the process
6. Model Selection¶
During this phase of the project, we select the relevant machine learning models that will be used in the project
7. Model Training¶
During this phase of the project, we train the model on the data that we prepared
8. Model Evaluation¶
During this phase, we evaluate the performance of the trained model using the desired evaluation metrics
9. Hyperparameter Optimisation¶
During this phase of the project, we optimise the parameters of the model in order to improve the model perform
10. Deployment¶
The final phase of the project involves saving the model for use on new data
Integrated Machine Learning Projects¶
Integrated modules tend to revolve around various machine learning projects, here are the status of project based content.