The goal of this project is to design, develop, and deploy a machine learning application that optimizes a specific process or extracts valuable insights from a dataset. Learners will gain hands-on experience in data preprocessing, model selection, training, evaluation, and deployment.

Tasks and Activities:

1. Problem Identification:

• Collaborate with the project lead to define the problem or process that will be optimized using machine learning.
• Research relevant machine learning techniques and algorithms to address the problem.

1. Data Collection and Preprocessing:

• Identify and gather appropriate datasets for the project.
• Clean, preprocess, and transform the data to ensure it is suitable for model training.

1. Model Development:

• Select and implement appropriate machine learning algorithms (e.g., regression, classification, clustering) based on the problem.
• Train models using the prepared data and fine-tune hyperparameters to improve performance.

1. Model Evaluation and Validation:

• Evaluate model performance using appropriate metrics (e.g., accuracy, precision, recall, etc.).
• Validate the model using cross-validation techniques and ensure it generalizes well to unseen data.

1. Deployment:

• Deploy the machine learning model in a simulated or real environment.
• Ensure the model is accessible and can be used effectively for decision-making or process optimization.

1. Documentation and Reporting:

• Document all steps, from data preprocessing to model deployment, ensuring transparency and reproducibility.
• Prepare a final report summarizing findings, challenges, and recommendations for further improvement.

Deliverables:



• A cleaned and preprocessed dataset ready for model training.
• A trained machine learning model, along with its code and documentation.
• A detailed report including model evaluation results, key insights, and suggested improvements.
• A deployed machine learning application, with instructions for use or demonstration.