Feedback issues

.github/ISSUE_TEMPLATE/tasks.md

@@ -0,0 +1,19 @@
+---
+name: Tasks
+about: To create and track tasks within a GitHub repository.
+title: Descriptive title of the task (Change this to suit the task)
+labels: ''
+assignees: ''
+
+---
+
+## **Description**
+Provide a concise overview of the task and its objectives.
+
+---
+
+##  **Tasks**
+
+- [ ] Task 1: Description of sub-task 1
+- [ ] Task 2: Description of sub-task 2
+- [ ] Task 3: Description of sub-task 3

.github/workflows/docker-publish.yml

@@ -0,0 +1,45 @@
+# Publishes docker image, pinning actions to a commit SHA,
+# and updating most recently built image with the latest tag.
+# Can be triggered by either pushing a commit that changes the `Dockerfile`, 
+# or manually dispatching the workflow.
+
+name: Publish Docker image
+
+on: 
+  workflow_dispatch:
+  push: 
+    paths: 
+      - 'Dockerfile'
+      - 'conda-linux-64.lock'
+
+jobs:
+  push_to_registry:
+    name: Push Docker image to Docker Hub
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out the repo
+        uses: actions/checkout@v4
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: hsshashank11/dsci522
+          tags: |
+            type=raw, value={{sha}},enable=${{github.ref_type != 'tag' }}
+            type=raw, value=latest
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ./Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

CONTRIBUTING.md

@@ -6,6 +6,9 @@ Thank you for your interest in contributing to the **522 Wine Quality Classifica
 
 ## How You Can Contribute
 
+### Small typo problems
+Little typo pro
+
 ### Reporting Issues
 If you encounter a bug or have a suggestion:
 1. Check the [existing issues](https://github.com/UBC-MDS/522-wine-quality-32/issues) to ensure it hasn’t been reported.
@@ -20,4 +23,8 @@ To contribute directly to the repository:
 1. Fork this repository and clone it to your local machine.
 2. Create a branch for your changes:
    ```bash
-   git checkout -b feature/your-feature-name
+   git checkout -b feature/your-feature-name
+3. Make your changes
+4. Create a new pull request for your changes
+5. Let other collaborator know your PR and wait for their merging
+6. Then you can work on other parts of project

Dockerfile

@@ -0,0 +1,8 @@
+FROM quay.io/jupyter/minimal-notebook:afe30f0c9ad8
+
+COPY conda-linux-64.lock /tmp/conda-linux-64.lock
+
+RUN mamba update --quiet --file /tmp/conda-linux-64.lock \
+    && mamba clean --all -y -f \
+    && fix-permissions "${CONDA_DIR}" \
+    && fix-permissions "/home/${NB_USER}"

README.md

@@ -1,4 +1,9 @@
-# [Wine Quality Prediction ](https://github.com/orgs/UBC-MDS/projects/177/views/1)
+# Wine Quality Prediction 
+
+### Project Board
+
+- [Milestone 1](<https://github.com/orgs/UBC-MDS/projects/177/views/1>)
+- [Milestone 2](https://github.com/orgs/UBC-MDS/projects/183)
 
 # Summary
 This project explores the relationship between physicochemical properties of wines and their quality ratings, aiming to predict wine quality and identify key factors influencing it using machine learning models such as Decision Trees. Through exploratory data analysis (EDA), we examine patterns, distributions, and correlations, addressing challenges such as class imbalances in wine quality ratings. The Decision Tree model is evaluated using metrics like accuracy, precision, recall, and feature importance to uncover significant predictors, such as density, alcohol, and volatile_acidity. The primary goal is to build an interpretable machine learning pipeline that provides actionable insights for winemakers to optimize production processes and for consumers to make informed choices. Additionally, the project sets the foundation for future work, including incorporating sensory attributes, addressing dataset imbalances, and leveraging more advanced ensemble methods for better predictions.
@@ -70,6 +75,28 @@ If you want to ensure reproducibility across different operating systems, use pl
     conda activate 522_milestone_env
     ```
 
+### Option 3: Using Docker Container
+### Running the analysis
+
+1. Navigate to the root of this project on your computer using the
+   command line and enter the following command:
+
+``` 
+docker compose up
+```
+
+2. In the terminal, look for a URL that starts with 
+`http://127.0.0.1:8888/lab?token=` 
+(for an example, see the highlighted text in the terminal below). 
+Copy and paste that URL into your browser.
+
+![Lab Token to copy](img/lab-token.png)
+
+3. To run the analysis,
+open `analysis.ipynb` in Jupyter Lab you just launched
+and under the "Kernel" menu click "Restart Kernel and Run All Cells...".
+
+
 ## Updating the Environment
 
 If the `environment.yaml` file is updated (e.g., new dependencies are added), you can update your existing environment with:
@@ -86,6 +113,11 @@ To remove the Conda environment:
 conda env remove -n 522_milestone_env
 ```
 
+To shut down the container and clean up the resources, 
+type `Cntrl` + `C` in the terminal
+where you launched the container, and then type `docker compose rm`
+
+
 ### Files Included for Reproducibility
 
 - `environment.yaml`: Contains the dependencies required for the project.