Milestones for 6-Month AI Project: Customer Churn Prediction

Month 1: Project Initialization and Data Collection

• Milestone 1: Define Project Scope and Objectives
  • Clearly define the business problem (customer churn prediction) and success criteria.
  • Outline specific goals: Predict churn probability, identify at-risk customers, improve retention strategies.
• Milestone 2: Collect and Clean Data
  • Gather historical customer data, including demographics, transaction history, customer interactions, and churn labels.
  • Perform initial data cleaning: handle missing values, correct inconsistencies, and remove duplicates.
• Milestone 3: Data Exploration and Preprocessing
  • Conduct exploratory data analysis (EDA) to understand distributions, correlations, and key patterns.
  • Preprocess the data: feature scaling, one-hot encoding, categorical variable transformation, and feature selection.

Month 2: Feature Engineering and Model Selection

• Milestone 4: Feature Engineering
  • Create new features based on domain knowledge, such as customer tenure, usage frequency, and customer service interactions.
  • Use techniques like interaction terms, feature encoding, and aggregation to improve model input.
• Milestone 5: Select Initial Machine Learning Models
  • Evaluate various classification models such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting.
  • Select a baseline model to establish initial performance metrics.

Month 3: Model Training and Hyperparameter Tuning

• Milestone 6: Model Training
  • Train the selected models using the prepared training dataset.
  • Evaluate initial performance using metrics like accuracy, precision, recall, and F1-score on the validation set.
• Milestone 7: Hyperparameter Tuning
  • Use cross-validation and grid/random search techniques to optimize hyperparameters (e.g., number of trees for Random Forest, max depth, learning rate for Gradient Boosting).
  • Monitor overfitting and adjust model complexity.

Month 4: Model Evaluation and Iteration

• Milestone 8: Model Evaluation
  • Evaluate models on a hold-out test dataset to assess generalization and avoid overfitting.
  • Compare different models’ performance using precision, recall, ROC-AUC, and F1-score.
  • Analyze performance in terms of business impact, such as identifying the most at-risk customer segments.
• Milestone 9: Model Refinement
  • Refine the model based on performance results. This may involve further feature engineering, removing irrelevant features, or retraining models with adjusted hyperparameters.

Month 5: Model Deployment Preparation and Integration

• Milestone 10: Model Interpretability and Validation
  • Assess model explainability using tools like SHAP or LIME to understand feature importance and ensure the model’s decisions are interpretable.
  • Validate the model with business stakeholders to ensure the predictions align with operational needs and objectives.
• Milestone 11: Prepare for Model Deployment
  • Develop scripts and pipelines for integrating the churn prediction model into the production environment.
  • Create a monitoring system to track the model’s performance post-deployment (e.g., retraining schedules, feedback loops).

Month 6: Model Deployment and Final Reporting

• Milestone 12: Model Deployment
  • Deploy the model to a production environment where it can provide real-time predictions on customer churn.
  • Ensure the model is integrated with customer relationship management (CRM) tools or other business platforms for actionable insights.
• Milestone 13: Final Reporting and Documentation
  • Prepare comprehensive documentation detailing the model’s development, performance, and deployment.
  • Present a final report summarizing the project’s objectives, milestones, evaluation results, and recommendations for improving customer retention.
• Milestone 14: Post-Deployment Monitoring and Maintenance
  • Set up a post-deployment monitoring system to track the model’s performance over time.
  • Schedule periodic model evaluations and retraining based on new data and business requirements.