Machine Learning Workflow
The process of building a machine learning model involves a series of structured steps that transform raw data into actionable insights.
Steps in Machine Learning
1. Problem Definition: Clearly outline the problem you are trying to solve. This step includes identifying the objective (e.g., classification, regression, or clustering) and the expected outcomes.
2. Data Collection: Gather data from reliable sources. Data can be collected from databases, APIs, user-generated content, or sensors. The quality and relevance of the data directly impact the model's performance.
3. Data Preprocessing: Clean and prepare the data for analysis. This includes:
- Handling Missing values
- Encoding Categorical Data,
- Scaling Numerical Features
- Removing Duplicates.
4. Feature Engineering: Select or create the most relevant features for modelling. Feature engineering may include feature selection, extraction, or transformation.
5. Model Selection: Choose an algorithm based on the problem type and dataset. For example, use linear regression for regression tasks, decision trees for classification, or k-means clustering for unsupervised learning.
6. Model Training: Train the model on labeled data (if supervised learning) or allow it to discover patterns in data (if unsupervised learning). The model learns by minimizing the error it makes while training.
7. Model Evaluation: Test the model using metrics such as accuracy, precision, recall, or R-squared. Evaluate its performance on unseen data.
8. Hyperparameter Tuning: Fine-tune model parameters (settings that control the learning process) to improve performance.
9. Deployment: Deploy the model into production, integrating it into applications or systems for real-time predictions or decision-making.
10. Monitoring and Maintenance: Continuously monitor the model's performance. Update it periodically with new data to address model drift (degradation of performance due to changes in the data) and ensure accuracy.
Machine Learning Workflow