IBM SkillsBuild: Predictive Analytics for Customer Churn
Catégories
Compétences
Portée du projet
Quel est l'objectif principal de ce projet?
The objective of this project is to:
Predict customer churn using logistic regression or decision trees.
Desired outcomes by the end of the project:
- Explain the purpose and key features of IBM Watson Studio
- Set up a new project in IBM Watson Studio
- Import a data set
- Clean data using the data refinery tool
- Refine data using the data refinery tool
- Create a data visualization
- Conclude insights from a data visualization
The objective of this project is to:
Predict customer churn using logistic regression or decision trees.
Desired outcomes by the end of the project:
- Explain the purpose and key features of IBM Watson Studio
- Set up a new project in IBM Watson Studio
- Import a data set
- Clean data using the data refinery tool
- Refine data using the data refinery tool
- Create a data visualization
- Conclude insights from a data visualization
Quelles tâches les apprenant.es devront-iels accomplir pour atteindre l'objectif du projet?
By the end of the project, examples of outcomes are:
- Clean and explore a dataset (e.g., telecom churn data).
- Train/test a classification model.
- Evaluate accuracy, precision, recall
Final deliverables should include:
1. Jupyter notebook with code and visualizations
2. Model evaluation summary (1–2 pages)
3. Recommendations for business based on prediction patterns
By the end of the project, examples of outcomes are:
- Clean and explore a dataset (e.g., telecom churn data).
- Train/test a classification model.
- Evaluate accuracy, precision, recall
Final deliverables should include:
1. Jupyter notebook with code and visualizations
2. Model evaluation summary (1–2 pages)
3. Recommendations for business based on prediction patterns