Reduced communication with suppliers

Higher inventory holding costs

Improved responsiveness to customer demand

Increased reliance on sales forecasts

Supervised learning - Classification

Supervised learning - Regression

Unsupervised learning 

Reinforcement learning

Feature importance analysis

Data cleaning

Hyperparameter tuning 

Cross-validation

The relationship between the model's complexity and its error rate.

The trade-off between true positive rate and false positive rate.

The distribution of feature importance values.

The accuracy of the model on the training data.

Decreased computational cost but increased data requirements

Decreased accuracy but increased interpretability

Increased accuracy but decreased interpretability

Increased computational cost but decreased data requirements

Variance

Underfitting

Bias

Overfitting 

Creating rule-based systems to capture expert knowledge.

Systems automatically learning and improving from experience without explicit programming.

Building algorithms that mimic human decision-making processes.

Manually programming systems to perform specific tasks.

Clustering similar data points together.

Building a single, complex decision tree.

Finding linear relationships between features and the target variable.

Averaging the predictions of multiple decision trees.

The company's IT infrastructure is future-proof and requires minimal adjustments.

The current infrastructure exceeds the needs of the growing business.

The infrastructure's limitations are directly impacting customer satisfaction and data security.

The infrastructure's primary focus is on internal processes rather than customer-facing services.

Enterprise software applications (SAP, Oracle, Odoo)

Network hardware (routers, switches, firewall, cabling)

Data management and storage (IBM DB2, MySQL)

Operating systems (Windows Server, Linux)