Data Science Practical Programs — VS Code Extension

Browse all 10 Data Science practical programs (from your PDF) in a sidebar, view the Aim/Explanation/Code/Output for each, and copy or run the code with one click.

Programs included

1. Dataset Loading
2. Data Cleaning
3. Data Transformation
4. Descriptive Statistics
5. Exploratory Data Analysis
6. Dimensionality Reduction using PCA
7. Linear Regression
8. K-Nearest Neighbours (KNN)
9. Model Evaluation Metrics
10. Ridge Regression

Install from the VS Code Marketplace

After publication, open the Extensions view in VS Code, search for Data Science Practical Programs, and select Install. The direct Marketplace URL will be:

https://marketplace.visualstudio.com/items?itemName=bharath-dev.dsml

Install locally (before Marketplace publication)

Option A — quickest, run from source:

1. Unzip this folder anywhere, e.g. ~/ds-practical-programs.
2. Open that folder in VS Code (code ~/ds-practical-programs or File → Open Folder).
3. Press F5 (or Run → Start Debugging). This launches an "Extension Development Host" window with the extension active.
4. In the new window, click the beaker icon in the Activity Bar (left side) to open the "DS Programs" view.

Option B — install as a real .vsix package (persists across restarts):

1. Install the packaging tool once: npm install -g @vscode/vsce
2. From inside the extension folder, run: vsce package (this creates ds-practical-programs-1.0.0.vsix)
3. In VS Code: Extensions view → ... menu → "Install from VSIX..." → select the generated file.
4. Reload VS Code. The "DS Programs" icon appears in the Activity Bar.

Using it

• Click the beaker icon in the Activity Bar → a list of all 10 programs.
• Click any program to open it in a panel showing Aim, Explanation, the full Python code, and the expected Output.
• Copy code — copies just the Python code to your clipboard.
• Run in Terminal — writes the code to a temp .py file and runs it with python3 (or python on Windows) in an integrated terminal named "DS Practical Programs".
• Create .py file — saves the program as a real file in your open workspace folder and opens it in the editor, ready to edit/run yourself.

Notes

• The sample code reads /content/sample_data/california_housing_test.csv (a Google Colab path). To run locally, either open the file in Colab, or edit the pd.read_csv(...) line in the created file to point at your own copy of that CSV (or any similar dataset) before running.
• Requires pandas, scikit-learn, matplotlib, and seaborn installed in whichever Python environment your terminal/command uses (pip install pandas scikit-learn matplotlib seaborn).