Machine Learning Model Development Checklist: Phase 1: Problem Definition: - Define the problem statement and objectives. - Identify the target variable and relevant features. - Establish success metrics. Phase 2: Data Collection - Identify data sources and acquire datasets. - Perform preliminary data exploration. - Handle missing data. - Encode categorical variables. Phase 3: Model Development - Split the dataset into training and testing sets. - Choose appropriate machine learning algorithms. - Tune hyper parameters. - Train and evaluate models. - Address class imbalance. Phase 4: Model Deployment - Develop a deployment strategy. - Create APIs or model-serving endpoints. -Implement security measures. - Deploy the model in a production environment. Phase 5: Monitoring and Maintenance - Set up model monitoring for drift and performance. - Implement retraining and update procedures. - Document model versioning.