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Lab | Deploying a Data Science Project with Django

In this lab, you will create a web application using Django to deploy a machine learning model. You can use the Iris dataset classification model (same as used for Flask) or train and save another model of your choice.

Prerequisites

  • Python 3.7+ installed
  • Basic knowledge of Python and machine learning
  • Virtual environment setup knowledge

Lab Instructions

Step 1: Project Setup

  1. Create a new project directory and navigate to it
  2. Create and activate a virtual environment
  3. Install required packages: django, scikit-learn, pandas, numpy, joblib
  4. Create a new Django project called ml_project
  5. Create a Django app called predictor
  6. Add your app to INSTALLED_APPS in settings.py

Step 2: Prepare Your Model

Choose one of the following options:

Option A: Use Iris Classification Model (Recommended)

  • Use the same Iris dataset classification model from the Flask lesson

Option B: Build Your Own Model

  1. Choose your own dataset and machine learning problem
  2. Train a classification or regression model of your choice
  3. Save your trained model as a .pkl file in your project root

Step 3: Create Views

  1. Create views in predictor/views.py:
    • A home view to display the input form
    • A predict view to handle form submission and return predictions
  2. Load your saved model in the views file
  3. Handle user input validation and error cases

Step 4: Configure URLs

  1. Create predictor/urls.py with URL patterns for your views
  2. Include your app URLs in the main ml_project/urls.py

Step 5: Create Templates

  1. Create a templates/predictor/ directory structure
  2. Create the following HTML templates:
    • base.html - Base template with navigation and Bootstrap CSS
    • home.html - Form for user input (extends base template)
    • result.html - Display prediction results (extends base template)

Step 6: Test Your Application

  1. Run Django migrations: python manage.py migrate
  2. Start the development server: python manage.py runserver
  3. Test your application in the browser
  4. Verify that:
    • Input form displays correctly
    • Model predictions work as expected
    • Error handling works for invalid inputs
    • Results are displayed properly

Deliverables

Your completed Django application should include:

  • Working web interface for model predictions
  • Proper error handling for invalid inputs
  • Clean, responsive design using Bootstrap
  • Proper project structure with templates and static files
  • Screenshot of your Django Web application running on Local Host.

Submission

  • Upon completion, add your deliverables to git.
  • Then commit git and push your branch to the remote.
  • Make a pull request and paste the PR link in the submission field in the Student Portal.

Good luck!