lfunderburk/fuel-electric-hybrid-vehicle-ml

Vehicle data classification (supervised, unsupervised learning)

ml-vehicle-classification

Classifying data from hybrid, fuel only and electric vehicles

api-demo.mov

Medium post about how the natural language API works

Architecture [AWS deployment under development]

About the data

Data consists on vehicle model information for three kinds of vehicles: fuel-based, electric and hybrid vehicles.

Data is extracted from an API listed on this site. The data source and data processing steps Contains information licensed under the Open Government License - Canada.

The vehicles in this dateset underwent five cycle fuel consumption testing:

• City test
• Highway test
• Cold temperature operation
• Air conditioner use
• Higher speeds with more rapid acceleration and braking

Vehicles are assigned a CO2 rating, a smog rating, and CO2 emissions are evaluated.

I am interested to uncover patterns and interesting insights between fuel-based, electric and hybrid vehicles.

The data pipeline consists of five scripts:

1. Data download and wrangling: extracts data on vehicle models from this public API
2. CO2 ratings are missing in a large proportion of fuel-based vehicles. The goal of this script is to perform supervised learning (voting classifier) to impute missing CO2 scores based on fuel-based vehicles. A model is setup and saved.
3. Model is used to complete missing values for CO2 ratings. KNNImputer is used on to complete missing smog rating scores.
4. Once the data is labelled, clustering is perfomed with the purpose of uncovering patterns. Recursive feature elimination with cross-validation is used to identify key features. Once key features are selected, Agglomerative Clustering, TSNE is computed for 2 and 3 dimensions, then results are compared against labelled data.
5. Results are served via an API with two key entry points:

• Search: a natural language entry point that can ask questions about the data.
• Predict: use the API to predict CO2 rating scores based on key features.

6. [In progress] a dashboard with visualizations (includes interesting vehicle stats, clustering results in 3D)
7. [Future work] scrape vehicle purchases data and analyse consumer trends with a focus on changes in ratios of types of vehicles purchased over time

Accessing the application on AWS

Dashboard: http://18.221.229.86:8050/

Natural language text api: http://18.221.15.147:8000/docs#/

Set up

Setting up, with Docker

Ensure you have Docker installed. Ensure you have an OpenAI API key. Create a .env file with the parameters

OPENAI_API_KEY=<your-api-key>

docker pull lgfunderburk/vehicle_classification:search
docker run -it --rm -p 8000:8000 -v /path/to/your/.env:/app/.env lgfunderburk/vehicle_classification:search

Then visit http://localhost:8000

Alternatively, you can clone the repo and build the images locally

git clone https://github.com/lfunderburk/fuel-electric-hybrid-vehicle-ml.git
cd fuel-electric-hybrid-vehicle-ml
docker build -t myapi:latest -f Dockerfile.api .
docker build -t mydashapp:latest -f Dockerfile.dash .

Setting up, locally

Clone the repo

git clone https://github.com/lfunderburk/fuel-electric-hybrid-vehicle-ml.git
cd fuel-electric-hybrid-vehicle-ml

Create and activate a virtual environment

conda create --name mlenv python==3.10
conda activate mlenv

Install dependencies

pip install -r requrements.txt

Executing the data pipeline - locally

From command line at the project root directory level

ploomber build

This command will execute the following data pipeline

tasks:
  - source: src/data/data_extraction.py
    product:
      nb: notebooks/data_extraction.ipynb
  - source: src/models/train_model.py
    product:
      nb: notebooks/train_model.ipynb
      model: models/hard_voting_classifier_co2_fuel.pkl
  - source: src/models/predict_model.py
    product:
      nb: notebooks/predict_model.ipynb
  - source: src/models/clustering.py
    product:
      nb: notebooks/clustering.ipynb

Sample output

name             Ran?      Elapsed (s)    Percentage
---------------  ------  -------------  ------------
data_extraction  True          29.371        8.13723
train_model      True         136.637       37.8553
predict_model    True          52.2234      14.4685
clustering       True         142.715       39.5391

Running tests

From command line at the project root directory level

pytest

Run API

From command line at the project root directory level

uvicorn src.app.app:app

Deployment methods:

1. This application consists of a Dash app with a dashboard that allows the user to visualize trends in different kinds of vehicles and consumer trends with a time component.

2. The data pipeline is scheduled to refresh and retrain the model in batches, and saves the model's results to a database/api for easier retrieval.

Project based on the cookiecutter data science project template. #cookiecutterdatascience