The CRISP-DM methodology: developing machine learning models

the CRISP-DM methodology
Valora esta página

The success of projects doesn’t rely solely on tools or algorithms but also on a structured and well-defined process that guides each stage of development. This is where the CRISP-DM methodology (Cross-Industry Standard Process for Data Mining) comes into play. This methodology provides a clear and systematic framework that enables data science teams to successfully organize and execute machine learning projects. In this article, we’ll discuss the CRISP-DM methodology, its phases, limitations, and some application examples.

What is the CRISP-DM methodology?

The CRISP-DM methodology is a standardized process model for conducting data mining projects and, by extension, machine learning projects. It was developed in the late 1990s by a consortium of companies, including SPSS, Daimler AG, and NCR. Its primary goal is to provide a flexible, non-proprietary guide applicable to a wide variety of industries and problems.

The CRISP-DM model consists of six main phases that cover everything from the initial understanding of the problem to the implementation of the final model. Although the process is presented sequentially, it is iterative and allows for constant review between phases. This flexibility makes it a robust methodology widely used in the industry.

The phases of the CRISP-DM methodology

1. Business understanding

This initial phase is critical for defining the project’s objectives from a business perspective. The team must work closely with stakeholders to:

  • Define the problem: understand the goal the business wants to achieve.
  • Translate business objectives into technical objectives: this involves converting a business need into a machine learning problem, such as classification, regression, or clustering.
  • Establish success criteria: decide which metrics or indicators will measure the model’s success.

For example, if a retail company wants to reduce customer churn, the technical objective could be to build a predictive model that identifies customers likely to leave.

2. Data understanding

In this phase, the data team explores and analyzes the available data to determine its quality and relevance. Activities include:

  • Data collection: obtain the necessary data sources.
  • Initial data exploration: use statistical analyses and visualizations to understand distributions, outliers, and patterns.
  • Identifying data quality issues: detect incomplete, inconsistent, or redundant data that may affect model performance.

For instance, in a sales prediction project, missing data for certain months might require imputation or removal of records.

3. Data preparation

The data preparation phase is one of the most labor-intensive and critical stages. Here, data is transformed and structured to be suitable for machine learning algorithms. Key tasks include:

  • Data cleaning: remove duplicates, impute missing values, and correct errors.
  • Feature engineering: create derived attributes that may be useful for the model.
  • Normalization and scaling: adjust variables to ensure they are on comparable ranges.
  • Dataset splitting: separate data into training, validation, and testing sets.

An example could be transforming dates into categorical variables like “day of the week” or “month of the year” to capture seasonality.

4. Modeling

In this phase, the prepared data is used to train machine learning models. The data science team selects and tunes algorithms and evaluates their performance. Key activities include:

  • Algorithm selection: choose the methods best suited to the problem, such as decision trees, neural networks, or ensemble methods.
  • Hyperparameter tuning: optimize configurations such as tree depth, learning rates, or the number of epochs.
  • Initial evaluation: use metrics like precision, recall, F1 score, or mean squared error (MSE) to evaluate the model.

A classification model to detect spam emails might use methods like SVM or Naive Bayes and compare performance in terms of false-positive rates.

5. Evaluation

The evaluation phase determines whether the model meets the objectives defined in the business understanding phase. This includes:

  • Reviewing key metrics: verify that the model’s performance is sufficient according to success criteria.
  • Validation with real-world data: test the model on data not used during training.
  • Ensuring interpretability: assess whether the results are understandable and actionable for business stakeholders.

If the model doesn’t meet expectations, the team may return to earlier phases to make adjustments.

6. Deployment

The final phase involves putting the model into production, where it can generate business value. This can include:

  • Integration into existing systems: implement the model into applications, dashboards, or processes.
  • Automation: configure data pipelines to periodically update the model.
  • Monitoring and maintenance: set up systems to supervise model performance and update it when necessary.

For example, a recommendation model in an e-commerce platform could be integrated to suggest personalized products in real time.

Limitations of the CRISP-DM methodology in machine learning projects

While the CRISP-DM methodology is widely used, it has some limitations that teams should consider:

  • Lack of specific guidance for complex projects: CRISP-DM provides a general framework but doesn’t offer technical details for implementing each step.
  • Traditional focus: it was designed for traditional data mining, so adaptations may be required for modern projects involving deep learning or big data.
  • Limited iteration: although iterative, it doesn’t strongly emphasize the need for constant feedback in agile environments.
  • Lack of focus on ethics and privacy: it doesn’t address important aspects like bias in data or regulatory compliance.

Despite these limitations, CRISP-DM remains a valuable and adaptable methodology, especially when complemented with other modern techniques or approaches.

Examples of applying the CRISP-DM methodology

1. Employee turnover prediction

A company wants to reduce employee turnover. Using CRISP-DM, they can collect HR data (business and data understanding), preprocess it to impute missing values, train a classification model, and evaluate its ability to identify high-risk employees. If successful, the model would alert HR, enabling preventive actions like improving work conditions or launching retention programs.

2. Customer segmentation in retail

A retailer wants to segment customers to personalize marketing strategies. With CRISP-DM, they collect purchase data, analyze spending patterns, create new features like purchase frequency, and apply clustering to identify key groups. For example, they might identify a «premium» customer segment making frequent purchases and a «bargain hunter» segment that responds well to discounts.

3. Fraud detection in finance

A bank uses CRISP-DM to build a model that detects suspicious transactions. They gather historical transaction data, clean it, and train machine learning models like random forests or neural networks to identify anomalies. The resulting system can be implemented for real-time assessments, flagging potential fraud and saving time and resources.

The CRISP-DM methodology remains a reliable standard for structuring machine learning projects thanks to its flexibility and iterative approach. While it has some limitations, its application can simplify the development of complex solutions and improve communication between technical and business teams.

For the best results, teams can combine CRISP-DM with modern tools and frameworks, such as agile environments, big data platforms, or MLOps (Machine Learning Operations) strategies. These approaches facilitate the deployment and continuous monitoring of models, enabling the creation of high-quality models that remain relevant and effective in today’s rapidly evolving digital landscape.

Facebook
Twitter
LinkedIn
Email