AI Development Workflow Assignment

This repository contains all materials for the AI for Software Engineering assignment on the AI Development Workflow.
It includes notebooks, scripts, sample datasets, a PDF report, and visual diagrams demonstrating the AI workflow from problem definition to deployment.

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Repository Structure

AI_Development_Workflow_Assignment/
│
├── notebooks/
│ ├── student_dropout_prediction.ipynb # Notebook for student dropout prediction case study
│ └── patient_readmission_prediction.ipynb # Notebook for patient readmission prediction case study
│
├── scripts/
│ ├── data_preprocessing.py # Script for data cleaning and preprocessing
│ ├── train_model.py # Script to train machine learning models
│ ├── evaluate_model.py # Script to evaluate model performance
│ └── api_integration_stub.py # Example API integration for model inference
│
├── data/
│ ├── student_data_sample.csv # Sample dataset for student dropout prediction
│ └── patient_data_sample.csv # Sample dataset for patient readmission prediction
│
├── PDF_Report/
│ └── AI_Workflow_Assignment.pdf # Full PDF report of the assignment
│
├── diagrams/
│ └── AI_Workflow_Flowchart.png # Flowchart of the AI development workflow
│
├── requirements.txt # Python dependencies
└── README.md # This file

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Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/AI_Development_Workflow_Assignment.git
cd AI_Development_Workflow_Assignment

2. Install Dependencies

It is recommended to use a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Usage Instructions

1. Run Jupyter Notebooks

Open the notebooks and execute all cells:

• notebooks/student_dropout_prediction.ipynb – Preprocessing, model training, and evaluation for student dropout prediction.

• notebooks/patient_readmission_prediction.ipynb – Preprocessing, model training, and evaluation for patient readmission prediction.

jupyter notebook

2. Run Scripts (Optional)

You can run the scripts independently for data processing, model training, or evaluation.

Preprocess Data

python scripts/data_preprocessing.py

Train Models

python scripts/train_model.py

Evaluate Models

python scripts/evaluate_model.py

3. API Integration Testing

The api_integration_stub.py file demonstrates how to send JSON payloads to the /predict endpoint for inference:

python scripts/api_integration_stub.py

Note: This is a stub for demonstration; ensure the API server is running if integrating with a real endpoint.

Project Highlights

• Student Dropout Prediction: Predict high-risk students for online learning platforms.

• Patient Readmission Prediction: Predict 30-day hospital readmission risk.

• Data Preprocessing Pipelines: Handling missing data, encoding, normalization, and feature engineering.

• Model Training & Evaluation: Random Forest and Gradient Boosted Trees with hyperparameter tuning.

• Visualization: Flowcharts and diagrams to illustrate the AI workflow.

• Documentation: Full Markdown-based PDF report included in PDF_Report/.

License

This project is for educational purposes. Please cite appropriately if reused