Building the ML Model Package

• Building a Machine Learning (ML) model package involves several steps. Here’s a high-level overview:

Steps	Description
1. Data Collection	Gather and organize your data. This could involve scraping data from the web, collecting data through surveys, using pre-existing datasets, etc.
2. Data Preprocessing	Clean and transform your data. This could involve handling missing values, dealing with outliers, encoding categorical variables, normalizing numerical variables, etc.
3. Feature Selection/Engineering	Identify the most relevant features to use for your model, or create new features from the existing ones.
4. Model Selection	Choose the type of model that’s most appropriate for your task (e.g., linear regression, decision tree, neural network, etc.).
5.Model Training	Train your model on your training data. This involves feeding your data through the model, and adjusting the model’s parameters based on the output.
6.Model Evaluation	Evaluate your model’s performance using appropriate metrics. This could involve looking at accuracy, precision, recall, F1 score, ROC AUC, etc., depending on the task.
7. Hyperparameter Tuning	Adjust the model’s hyperparameters to improve performance.
8.Model Validation	Validate your model using a separate validation set to ensure it generalizes well to unseen data.
9.Model Deployment	Once you’re satisfied with your model’s performance, deploy it so it can be used to make predictions on new data.
10. Model Monitoring and Updating	After deployment, monitor your model’s performance over time. If its performance drops, or if new data becomes available, you may need to retrain or update your model.

1. Create Package
2. Maintain the seperate modules.
3. Maintain seperate files for preprocessing, data handling, manual configuration, etc.
4. Build test cases - verify the integrity.