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Best Practices: Selecting the Right Data for Machine Learning Analytics

Selecting the right data for various types of machine learning analytics—such as PREDICT for predictive modeling, CLUSTER for segmentations, FORECAST for time-series analysis, or SENTIMENT for sentiment analysis—is essential for deriving actionable insights. This guide aims to arm your analytics team with best practices for identifying which data may be relevant, enabling you to operationalize machine learning for transformative growth directly on the Infer platform.

Identifying the Right Data

Business Understanding

Understanding the specific business question or challenge at hand is crucial. Are you looking to predict customer churn, improve customer retention, or analyze customer sentiment? For PREDICT models, you'll want to zero in on variables that historically influence the metric you're trying to predict. This business problem guides which kind of analysis to use and consequently, which data will be relevant.

Historical Relevance

Past behavior can often predict future behavior, which is especially pertinent when using the PREDICT function. For clustering algorithms, examine past data to identify trends or characteristics that cluster together. For time-series forecasting, past patterns of a metric will be critical.

Domain Expertise

Consult with business experts who understand the nuances of what you're trying to analyze. Their expertise can point you toward data variables that might not be obvious but are crucial in the analysis, especially for PREDICT models.

Exploratory Analysis Using Infer

The Infer platform is built for quick and intuitive EDA. Use it to attempt your analyses before settling on which variables to include. For PREDICT models, the platform provides immediate feedback on the quality of your model, and any issues with the data, allowing you to iterate quickly.

Hypothesis Generation

List the Possibilities

Begin by listing all the variables that you think could influence the outcome of your analysis. These variables will be your initial hypotheses.

Prioritize and Filter

Not all variables will have the same impact. If you're focusing on PREDICT, use the Infer platform's feedback and your domain expertise to prioritize variables. The platform makes it clear which variables are the most impactful, enabling you to focus on what truly matters.

Test Hypotheses Using Infer

The Infer platform's built-in validation measures mean that you don't have to step outside to test your hypotheses. Especially for PREDICT models, run the model and pay attention to the feedback; it will immediately inform you about the quality and predictive power of your chosen variables.

Operational Variables

Real-time Data Sources

For operationalizing machine learning, such as PREDICT for churn analysis or lead scoring, focus on variables that are updated in real-time or near real-time. This ensures that your insights remain dynamic and actionable.

Key Business Metrics

Operational variables like quarterly sales or monthly active users are often the most direct metrics to use when you're looking to operationalize a PREDICT or FORECAST model.

Customer Interactions

For SENTIMENT analysis, focus on variables that capture customer feedback, such as customer reviews, net promoter scores, or social media mentions.

In Summary

The Infer platform streamlines the process of selecting the right data for different types of machine learning commands. It makes it easier for your analytics team to operationalize machine learning analytics for transformative growth. Whether you're focusing on predictive models, clustering, time series analysis, or sentiment, the Infer platform provides a robust set of tools to ensure you're always on the right track.