The process of selecting a subset of relevant features for model building, improving accuracy and reducing overfitting.

The loss of customers over time, often measured as a percentage of total customers, critical for business strategy.

A feature selection technique that recursively removes the least important features based on model performance.

A regression analysis method that performs both variable selection and regularization to enhance prediction accuracy.

A measure of how well a machine learning model predicts outcomes, often evaluated using metrics like accuracy and F1 score.

The process of creating a model that forecasts future outcomes based on historical data.

A popular Python library for machine learning that provides simple and efficient tools for data mining and analysis.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

The graphical representation of information and data, making complex data more accessible and understandable.

A technique that ranks features based on their contribution to the model's predictive power.

The process of optimizing the parameters of a machine learning model to improve performance.

The practice of using data to tell a compelling story, making insights more relatable and actionable.

Quantitative measures used to assess the performance of a machine learning model, such as precision and recall.

A modeling error that occurs when a model learns the training data too well, failing to generalize to new data.

A modeling error that occurs when a model is too simple to capture the underlying trend of the data.

The process of selecting a subset of relevant features from a larger dataset to improve model performance.

The steps taken to clean and prepare raw data for analysis, ensuring accuracy and consistency.

A subset of data used to train a machine learning model, allowing it to learn patterns and relationships.

A separate subset of data used to evaluate the performance of a trained machine learning model.

A plan of action designed to achieve specific business goals, often informed by data analysis.

Customers who continue to engage with a business, crucial for sustaining revenue and growth.

The process of conveying analytical findings effectively to stakeholders, ensuring understanding and action.

The practice of summarizing data analysis findings in a structured format, often for decision-making purposes.

Practical uses of theoretical concepts in everyday business scenarios, enhancing relevance and effectiveness.

The process of using domain knowledge to create new features from raw data, improving model performance.