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✶ Preprocess data: Simplify numerical features, convert categorical variables, and create new metrics like
customer tenure.
• Modelling
✶ Select techniques: Choose machine learning algorithms suitable for classification tasks, such as logistic
regression, decision trees, and random forests.
✶ Train models: Use the prepared data to train multiple models, adjusting hyperparameters and performing
cross-validation to optimise performance.
• Evaluation
✶ Evaluate models: Assess the performance of each model using metrics like accuracy, precision, recall, and
F1-score.
✶ Compare models: Compare the performance of different models to select the best-performing one for
deployment.
• Deployment
✶ Deploy model: Integrate the selected model into the company's customer management system to predict
churn risk for new customers.
✶ Monitor performance: Monitor the model's predictions in real-time, track churn rates, and gather feedback
from customer service interactions.
In this example, each phase of the AI project cycle builds upon the outputs of the previous phase:
AI Project Cycle Mapping Template
Data Data
Problem Scoping Modelling Evaluation Deployment
Acquisition Exploration
The Gather customer Analyse Select machine Evaluate Integrate the
telecommunications demographics, customer learning each model's model to predict
company wants to usage patterns, demographics, algorithms for performance new customer
reduce customer service history, usage patterns, classification, using accuracy, churn risk.
churn rates. and churn and churn like logistic precision, recall,
status data rates with regression, and F1-score.
from company visualisations decision trees,
databases. and statistical and random
summaries. forests.
• Problem Scoping: States the problem that needs attention.
• Data Acquisition: Data acquisition consists of two words: Data and Acquisition. Data refers to the raw facts,
figures, information, or statistics; where as, acquisition refers to acquiring data for the project.
• Data Exploration: It is the first step of data analysis that is used to visualise data. It generates insights that are
used to inform modelling decisions.
• Modelling: Develops predictive models based on insights gained from data exploration.
• Evaluation: Assesses model performance by feeding the data into the model and comparing the output with
the actual answers. It is used to determine the best model for deployment.
• Deployment: Integrates the selected model into the company's systems for real-world usage.
220 Touchpad Artificial Intelligence (Ver. 3.0)-IX

