DESIGN-PROJECT-01

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DESIGN-PROJECT-01

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1. Set Up Python on Your System
   - Install Python 3.x from the [official Python website](https://www.python.org/).  
   - During installation, ensure the option Add Python to PATH is selected.

2. Install Required Libraries 
   - After Python is installed, open the terminal or command prompt.  
   - Install the necessary libraries using the following commands:
     - pip install numpy
     - pip install scikit-learn
     - pip install matplotlib
   - These libraries are essential for data handling, model training, and visualization.

3. Prepare the Project Files  
   - Place all project files in a single folder for easy access.
   - Ensure the script file, Digit Recognition Training.py, is included in this folder.

4. Run the Training Script
   - Open the terminal or command prompt.  
   - Use the cd command to navigate to the folder containing the project files. For example:
     bash
     cd path/to/your/project-folder
     
   - Run the training script using:
     bash
     python "Digit Recognition Training.py"
     
   - The script will perform the following:
     - Load the digits dataset from scikit-learn.
     - Split the data into training and testing sets.
     - Train a Support Vector Machine (SVM) classifier.
     - Predict outcomes on the test set.
     - Save example predictions as image files.
     - Generate an HTML file displaying the model's accuracy and prediction images.

5. View the Results  
   - After execution, check the project folder for:
     - Several image files (digit_0.png, digit_1.png, etc.) showing test predictions.
     - An HTML file named digit_recognition.html.
   - Open the HTML file in any web browser to view:
     - The accuracy of the model.
     - Images of predicted digits along with their labels.

6. Verify and Adjust 
   - Review the accuracy displayed in the browser.  
   - If needed, modify the script parameters (e.g., kernel type in SVM) to optimize the model performance.  
   - Re-run the script to test the changes.

7. Next Steps (Optional) 
   - Save the trained SVM model for later use.
   - Modify the HTML to allow real-time user input for new digit predictions

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