docs: add docker instructions

README.md

@@ -6,7 +6,22 @@ Traffic-Waves is a voluntary project focused on daily traffic predictions in Par
 
 The project leverages ML and DL techniques to analyze historical traffic data and make predictions for daily traffic patterns in Paris. This aids in providing insights for commuters and city planners alike.
 
-## Download and Install
+## Pipeline components
+
+The above command runs the following components:
+
+**Data**:
+- **[Data collection](src/call_data_api.py)**: Call the Open data Paris API and save the data in batches.
+- **[Data processing](src/process_data.py)**: Merge the data and apply preprocessing steps to prepare data for batch predictions.
+
+**Machine learning**:
+- **[Model training](src/train.py)**: Import and train the ML model on the historical data.
+- **[Predictions](src/predict.py)**: Get the one-day ahead predictions using the trained model and batch data.
+
+**Visualization**:
+- **[Dashboard](src/app.py)**: Start a flask app to display the input data and predictions for all the links.
+
+## 1. Download and Install
 
 To install the requirements, run the following command in the parent:
 
@@ -16,7 +31,7 @@ cd traffic-waves
 make install
 ```
 
-## Usage
+### Usage
 
 Run the data collection, processing and machine learing pipeline (with default options):
 ```bash
@@ -29,20 +44,13 @@ make app
 ```
 Visit `http://127.0.0.1:5000/` in the web-browser to open the visualization dashboard. 
 
-## Pipeline components
+## 2. Building and running your application using Docker
 
-The above command runs the following components:
+When you're ready with Docker installed, start your application by running:
+`docker compose up --build`.
 
-**Data**:
-- **[Data collection](src/call_data_api.py)**: Call the Open data Paris API and save the data in batches.
-- **[Data processing](src/process_data.py)**: Merge the data and apply preprocessing steps to prepare data for batch predictions.
+Your application will be available at http://localhost:5000.
 
-**Machine learning**:
-- **[Model training](src/train.py)**: Import and train the ML model on the historical data.
-- **[Predictions](src/predict.py)**: Get the one-day ahead predictions using the trained model and batch data.
-
-**Visualization**:
-- **[Dashboard](src/app.py)**: Start a flask app to display the input data and predictions for all the links.
 
 ## License
 **to be updated**