Machine Learning Life Cycle Explained

Building a machine learning model is not a one-step process. It follows a clear cycle, from collecting data to deploying the final model.

Phase 1: Problem Definition
Understand the business or research problem you are trying to solve.

Phase 2: Data Collection
Gather relevant data from databases, sensors, files, or APIs.

Phase 3: Data Preprocessing
Clean the data, handle missing values, and prepare it for training.

Phase 4: Model Selection
Choose the right algorithm based on the problem type, like regression or classification.

Phase 5: Model Training
Feed the prepared data into the algorithm so it can learn patterns.

Phase 6: Model Evaluation
Test the model on new data to check its accuracy and performance.

Phase 7: Hyperparameter Tuning
Adjust the model's settings to improve results further.

Phase 8: Deployment
Put the trained model into a real application where it can make live predictions.

Phase 9: Monitoring and Maintenance
Track the model's performance over time and retrain it as new data comes in.

Following this cycle ensures a machine learning project is structured, reliable, and continuously improving, rather than a one-time effort.