> 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about traditional Machine Learning. In this lesson group, you will learn about what is sometimes called 'classic' ML, using primarily Scikit-Learn as a library and avoiding deep learning, which is covered in our 'AI for Beginners' curriculum. Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about traditional Machine Learning. In this lesson group, you will learn about what is sometimes called 'classic' ML, using primarily Scikit-Learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum.
Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
**Hearty thanks to our authors (list all authors here)**
@ -23,7 +25,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson cur
- Start with a pre-lecture quiz
- Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
- Try to create the projects by comprehending the lessons rather than copying the solution code; however that code is available in the `/solution` folders in each project-oriented lesson.
- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `/solution` folders in each project-oriented lesson.
- Take the post-lecture quiz
- Complete the challenge
- Complete the assignment
@ -68,14 +70,14 @@ By ensuring that the content aligns with projects, the process is made more enga
| 05 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](Regression/2-Data/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](Regression/3-Linear/README.md) | Jen |
| 07 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build a Logistic Regression model | [lesson](Regression/4-Logistic/README.md) | Jen |
| 08 | Introduction to Classification | [Classification](Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](Classification/1-Data/README.md) | Cassie |
| 09 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Discriminative Model | [lesson](Classification/2-Descriminative/README.md) | Cassie |
| 10 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Generative Model | [lesson](Classification/3-Generative/README.md) | Cassie |
| 11 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Web App using your Model | [lesson](Classification/4-Applied/README.md) | Cassie |
| 12 | Introduction to Clustering | [Clustering](Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](Clustering/1-Visualize/README.md) | |
| 15 | A Web App 🔌 | [Web App](Web-App/README.md) | Build a Web app to use your trained model | [lesson](Web-App/README.md) | Jen |
| 08 | A Web App 🔌 | [Web App](Web-App/README.md) | Build a Web app to use your trained model | [lesson](Web-App/README.md) | Jen |
| 09 | Introduction to Classification | [Classification](Classification/README.md) | Clean, Prep, and Visualize your Data; Introduction to Classification | [lesson](Classification/1-Data/README.md) | Cassie |
| 10 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Discriminative Model | [lesson](Classification/2-Descriminative/README.md) | Cassie |
| 11 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Generative Model | [lesson](Classification/3-Generative/README.md) | Cassie |
| 12 | Delicious Asian Recipes 🍜 | [Classification](Classification/README.md) | Build a Web App using your Model | [lesson](Classification/4-Applied/README.md) | Cassie |
| 13 | Introduction to Clustering | [Clustering](Clustering/README.md) | Clean, Prep, and Visualize your Data; Introduction to Clustering | [lesson](Clustering/1-Visualize/README.md) | |
In this lesson, you will train a Linear Regression model and a Classification model on a dataset that's out of this world: UFO Sightings over the past century, sourced from [NUFORC's database](https://www.nuforc.org). We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app. To do this, you need to build a web app using Flask.
## [Pre-lecture quiz](link-to-quiz-app)
There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app. There are many questions you need to ask: Is it a web app, or a mobile app? Where will the model reside, in the cloud or locally? Does the app have to work offline? And what technology was used to train the model, because that may influence the tooling you need to use?
@ -15,12 +16,256 @@ You also have the opportunity to build an entire Flask web app that would be abl
## Tools
For this task, you need two tools: Flask and Pickle, both of which run on Python.
## [Pre-lecture quiz](link-to-quiz-app)
✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
## Clean your data
In this lesson you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot" or simply "the lights chased us". The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the city, state and country where the sighting occurred, the object's shape and its latitude and longitude.
In the blank [notebook](notebook.ipynb) included in this lesson, import pandas, matplotlib, and numpy as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
```python
import pandas as pd
import numpy as np
ufos = pd.read_csv('../data/ufos.csv')
ufos.head()
```
Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the Country field.
Now you can get ready to train a model by diving the data into the training and testing group. Select the three features you want to train on as your X vector, and the y vector will be the Country. You want to be able to input seconds, latitude and longitude and get a country id to return.
```python
from sklearn.model_selection import train_test_split
The accuracy isn't bad (around 95%), unsurprisingly, as country and latitude/longitude have a good correlation. The model you created isn't very revolutionary, but it's a good exercise to try to train from raw data that you cleaned, export, and then use this model in a web app.
## Pickle your model
Now, it's time to pickle your model! You can do that in just a few lines of code. Load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,
```python
import pickle
model_filename = 'ufo-model.pkl'
pickle.dump(model, open(model_filename,'wb'))
model = pickle.load(open('ufo-model.pkl','rb'))
print(model.predict([[50,44,-12]]))
```
The model returns '3', which is the country code for the UK. Wild! 👽
## Build a Flask app
Now you can build a Flask app to call your model and return similar results, but in a more visually pleasing way.
Start by creating a folder called web-app next to the notebook.ipynb file where your ufo-model.pkl file resides. In that folder create three more folders: `static`, with a folder `css` inside it, and `templates`.
> Refer to the solution folder for a view of the finished app
The first file to create in `web-app` is a `requirements.txt` file. Like `package.json` in a JavaScript app, this file lists dependencies required by the app. In `requirements.txt` add the lines:
```text
scikit-learn
pandas
numpy
flask
```
Now, run this file by navigating to `web-app` (`cd web-app`) in your terminal and typing `python requirements.txt`
> You might need to use `python3 requirements.txt`, depending on your local configuration.
Now, you're ready to create three more files to finish the app:
<buttontype="submit"class="btn">Predict country where the UFO is seen</button>
</form>
<p>{{ prediction_text }}</p>
</div>
</div>
</body>
</html>
```
Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.
Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:
In `app.py` add:
```python
import numpy as np
from flask import Flask, request, render_template
import pickle
app = Flask(__name__)
model = pickle.load(open("../ufo-model.pkl", "rb"))
@app.route("/")
def home():
return render_template("index.html")
@app.route("/predict", methods=["POST"])
def predict():
int_features = [int(x) for x in request.form.values()]
final_features = [np.array(int_features)]
prediction = model.predict(final_features)
output = prediction[0]
countries = ["Australia", "Canada", "Germany", "UK", "US"]
If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
Before doing that, take a look at the parts of `app.py`.
First, dependencies are loaded and the app starts. Then, the model is imported. Then, index.html is rendered on the home route. On the `/predict` route, several things happen when the form is posted:
1. The form variables are gathered and converted to a numpy array. They are then sent to the model and a prediction is returned.
2. The Countries that we want displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
🚀 Challenge: Add a challenge for students to work on collaboratively in class to enhance the project
Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction. In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
🚀 Challenge: Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
@ -196,13 +179,17 @@
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": 28,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"name": "stderr",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 41\n",
@ -217,10 +204,6 @@
"\n",
"Predicted labels: [4 4 4 ... 3 4 4]\n",
"Accuracy: 0.9512855209742895\n",
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
Jen Looper
4 years ago