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Django for Data Science
Will Vincent
Boston Python Meetup (March 2025)
Deploying Machine Learning Models with Django
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Will Vincent
● JetBrains/PyCharm Developer Advocate
● Django Board Member (2020-2022)
● LearnDjango.com
● Books: Django for Beginners/APIs/Professionals
● Django Chat podcast
● Django News newsletter
● Open source: awesome-django, DjangoX
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Image: AlmaBetter.com
Image: AlmaBetter.com
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What is Data Science?
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What do Data Scientists Do?
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Python 2010-Today
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The Rise of Data Science in Python
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Python Web Frameworks
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Why Django in 2025?
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Maths
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Train a Model
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Jupyter Notebook - Setup
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Jupyter Notebook - Layout
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Iris Dataset
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Iris Dataset
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requirements.txt
pandas~=2.2.3
scikit-learn~=1.6.1
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Jupyter Notebook
# Load libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from joblib import dump, load
# Load dataset
df = pd.read_csv("data/iris.csv")
# Extract features (X) and labels (y)
X = df[["SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm"]].values
y = df["Species"].values
# Split into training and testing data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.20, random_state=1
)
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Jupyter Notebook
# Train the model
model = SVC(gamma="auto")
model.fit(X_train, y_train)
# Make predictions on validation dataset
predictions = model.predict(X_test)
# Evaluate model accuracy
accuracy = accuracy_score(y_test, predictions)
print(f"Model Accuracy: {accuracy:.2f}") # Print accuracy score
# Save model using joblib
dump(model, "models/iris.joblib")
# Load model using joblib
model = load("models/iris.joblib")
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# Get input from the user
try:
sepal_length = float(input("Enter SepalLengthCm: "))
sepal_width = float(input("Enter SepalWidthCm: "))
petal_length = float(input("Enter PetalLengthCm: "))
petal_width = float(input("Enter PetalWidthCm: "))
except ValueError:
print("Invalid input. Please enter numeric values.")
exit()
# Make a prediction
input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
result = model.predict(input_data)
print("Prediction:", result[0]) # Iris Classification with SVM
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Live Demo?
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Visualize
# requirements.txt
pandas~=2.2.3
scikit-learn~=1.6.1
seaborn~=0.13.2
matplotlib~=3.10.0
# Import Seaborn and Matplotlib for visualizations
import seaborn as sns
import matplotlib.pyplot as plt
# Pairplot to visualize relationships between features
sns.pairplot(df, hue="Species", diag_kind="kde")
plt.show()
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Django Project
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Gameplan
● Create new Django project
● Load joblib file
● Forms for users to enter predictions -> see results
● Store user info in the database
● Deployment?
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New Django Project
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├── django_project
│ ├── __init__.py
│ ├── asgi.py
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py
├── manage.py
├── predict
│ ├── __init__.py
│ ├── admin.py
│ ├── apps.py
│ ├── migrations
│ │ └── __init__.py
│ ├── models.py
│ ├── tests.py
│ └── views.py
└── templates
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Add our ML Model
├── django_project
│ ├── __init__.py
│ ├── asgi.py
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py
├── iris.joblib # hi there!
├── manage.py
├── predict
│ ├── __init__.py
│ ├── admin.py
│ ├── apps.py
│ ├── migrations
│ │ └── __init__.py
│ ├── models.py
│ ├── tests.py
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Django Under The Hood: Part 1
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Djangoʼs MVT Architecture
● Model (M) --> Handles database interactions and business logic
● View (V) --> Acts as Controller in MVC, process requests and match
template
● Template (T) --> Presentation layer, HTML+ CSS + data
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Django Under The Hood: Part 2
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URLs
# django_project/urls.py
from django.contrib import admin
from django.urls import path, include # new
urlpatterns = [
path("admin/", admin.site.urls),
path("", include("predict.urls")), # new
]
# predict/urls.py
from django.urls import path
from .views import predict
urlpatterns = [
path("", predict, name="predict"),
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View + Template
# predict/views.py
from django.shortcuts import render
def predict(request):
return render(request, "predict.html")
Hello
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Runserver (again)
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# predict/views.py
import joblib
import numpy as np
from django.shortcuts import render
# Load the trained model
model = joblib.load("iris.joblib")
def predict(request):
prediction = None
if request.method == "POST":
try:
# Get input values from the form
sepal_length = float(request.POST.get("sepal_length"))
sepal_width = float(request.POST.get("sepal_width"))
petal_length = float(request.POST.get("petal_length"))
petal_width = float(request.POST.get("petal_width"))
# Make a prediction
features = np.array(
[[sepal_length, sepal_width, petal_length, petal_width]]
)
prediction = model.predict(features)[0]
except Exception as e:
prediction = f"Error: {str(e)}"
return render(request, "predict.html", {"prediction": prediction})
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Iris Prediction
body {
font-family: Arial, sans-serif;
max-width: 400px;
margin: 50px auto;
text-align: center;
}
form {
display: flex;
flex-direction: column;
gap: 10px;
}
input, button {
padding: 10px;
font-size: 16px;
}
button {
cursor: pointer;
background-color: #4CAF50;
color: white;
border: none;
}
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...
Iris Species Predictor
{% csrf_token %} Predict {% if prediction %}Prediction: {{ prediction }}
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Store User Inputs
# predict/views.py
...
def predict(request):
prediction = None
form_data = {} # To store user inputs
if request.method == "POST":
try:
# Store form inputs
form_data = {
"sepal_length": request.POST.get("sepal_length"),
"sepal_width": request.POST.get("sepal_width"),
"petal_length": request.POST.get("petal_length"),
"petal_width": request.POST.get("petal_width"),
}
# Convert inputs to float and make a prediction
features = np.array([[float(v) for v in form_data.values()]])
prediction = model.predict(features)[0]
except Exception as e:
prediction = f"Error: {str(e)}"
return render(
request, "predict.html", {"prediction": prediction, "form_data": form_data}
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Display User Inputs
{% if prediction %}
]
{% endif %}
Prediction: {{ prediction }}
Inputs:
- Sepal Length: {{ form_data.sepal_length }}
- Sepal Width: {{ form_data.sepal_width }}
- Petal Length: {{ form_data.petal_length }}
- Petal Width: {{ form_data.petal_width }}
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Add a Model to Store Predictions
# predict/models.py
from django.db import models
class IrisPrediction(models.Model):
sepal_length = models.FloatField()
sepal_width = models.FloatField()
petal_length = models.FloatField()
petal_width = models.FloatField()
prediction = models.CharField(max_length=100)
created_at = models.DateTimeField(auto_now_add=True)
def __str__(self):
return f"{self.prediction} ({self.created_at.strftime('%Y-%m-%d
%H:%M:%S')})"
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Update View to Save Predictions
# predict/views.py
import joblib
import numpy as np
from django.shortcuts import render
from .models import IrisPrediction. # new
# Load the trained model
model = joblib.load("iris.joblib")
def predict(request):
prediction = None
form_data = {}
if request.method == "POST":
try:
...
# Save prediction to database
IrisPrediction.objects.create(
sepal_length=form_data["sepal_length"],
sepal_width=form_data["sepal_width"],
petal_length=form_data["petal_length"],
petal_width=form_data["petal_width"],
prediction=prediction,
)
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Update Admin
# predict/admin.py
from django.contrib import admin
from .models import IrisPrediction
admin.site.register(IrisPrediction)
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DjangoForDataScience.com
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Deployment...
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Deployment Checklist
● configure static files and install WhiteNoise
● add environment variables with environs
● create a .env file and update the .gitignore file
● update DEBUG, ALLOWED_HOSTS, SECRET_KEY, and
CSRF_TRUSTED_ORIGINS
● update DATABASES to run PostgreSQL in production and
install psycopg
● install Gunicorn as a production WSGI server
● create a Procfile
● update the requirements.txt file
● create a new Heroku project, push the code to it, and
start a dyno web process
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LLMs...
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LLM Tips
1. Still fancy autocomplete (not AGI)
2. Be aware of training cut-off dates
3. Context is king
4. Prompt tips
5. Vibe coding...
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Takeaways
✅ Django is great for deploying ML models (web UI + APIs)
✅ Iris dataset is simple but powerful for learning ML deployment
✅ Try deploying a real-world ML model
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Questions?
@wsvincent.bsky.social
@wsvincent -- django_irisml & iris_ml
fosstodon.org/@wsvincent
@william-s-vincent
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Appendix C
More Django architecture diagrams...
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Django Under The Hood: Part 3
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Django Under The Hood: Part 4
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Deployment...
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1a. Static Files
(.venv) $ mkdir static
(.venv) $ mkdir static/css
(.venv) $ mkdir static/js
(.venv) $ mkdir static/img
(.venv) $ touch static/css/.keep
(.venv) $ touch static/js/.keep
(.venv) $ touch static/img/.keep
# django_project/settings.py
STATIC_URL = "static/"
STATICFILES_DIRS = [BASE_DIR / "static"] # new
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1b. Static Files
# django_project/settings.py
STATIC_URL = "static/"
STATICFILES_DIRS = [BASE_DIR / "static"]
STATIC_ROOT = BASE_DIR / "staticfiles" # new
(.venv) $ python manage.py collectstatic
# staticfiles/
└── admin
├── css
├── img
├── js
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1c. Static Files
{% load static %}
...
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1d. Static Files
INSTALLED_APPS = [
...
"whitenoise.runserver_nostatic", # new
"django.contrib.staticfiles",
"predict",
]
MIDDLEWARE = [
"django.middleware.security.SecurityMiddleware",
"django.contrib.sessions.middleware.SessionMiddleware",
"whitenoise.middleware.WhiteNoiseMiddleware", # new
...
]
STORAGES = {
"default": {
"BACKEND": "django.core.files.storage.FileSystemStorage",
},
"staticfiles": {
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2. Environment Variables
# django_project/settings.py
from pathlib import Path
from environs import Env # new
env = Env() # new
env.read_env() # new
# .gitignore
.venv/
__pycache__/
db.sqlite3
.env
.idea/