- Data Acquisition: This involves identifying relevant data sources, collecting and storing data in a suitable format for analysis. This may include obtaining data from internal databases, public datasets, web scraping, or data generated by sensors and devices.
- Data Cleaning and Preparation: In this stage, the collected data is cleaned, transformed and prepared for analysis. This includes removing missing values, duplicates, outliers and handling errors.
- Exploratory Data Analysis (EDA): The purpose of EDA is to gain a better understanding of the data and identify patterns, trends, and relationships. This includes descriptive statistics, visualization, and data profiling.
- Feature Engineering and Selection: Feature engineering involves selecting relevant features from the dataset, transforming or creating new features to improve model performance. Feature selection involves identifying the most important features that contribute to model accuracy.
- Model Development: This involves selecting an appropriate machine learning algorithm, training and validating the model on the data. Model selection, hyperparameter tuning and performance evaluation are important aspects of model development.
- Model Deployment: Once a model is developed, it is deployed in a production environment to make predictions on new data. This may involve integrating the model into a web application or API for use by end-users.
- Model Monitoring and Maintenance: After deployment, it is important to monitor the performance of the model and maintain it by retraining with new data or updating the model if necessary.
These stages are iterative and involve collaboration between data scientists, domain experts, and stakeholders to ensure the project meets the desired outcomes.