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PawFit: Intelligent Dog Gear Sizing and Recommendation System

Ligalah Hezron's photo
Ligalah Hezron
·

5 min read

  1. Imports:

    • pandas, numpy: These are libraries in Python used for data manipulation and numerical computations, respectively.

    • statsmodels.formula.api: This is a module from the Statsmodels library that provides a formula API for specifying regression models.

    • joblib: This is a library used for saving and loading Python objects (e.g., models) to disk.

    • streamlit: This is a Python library used for creating interactive web applications.

    • plotly.express: This is a high-level interface for creating figures with Plotly, a visualization library.

  2. Load Data:

    • data = pd.read_csv('doggy-boot-harness.csv'): This line reads a CSV file containing data related to dog harness and boot sizes and loads it into a pandas DataFrame called data.

  3. Model Training and Saving:

    • A linear regression model is trained using the statsmodels library. It predicts boot size based on harness size.

    • The trained model is saved using joblib.dump() to a file named avalanche_dog_boot_model.pkl.

  4. Function Definitions:

    • load_model_and_predict(harness_size): This function loads the trained model from the saved file and predicts the boot size based on the input harness size.

    • check_size_of_boots(selected_harness_size, selected_boot_size): This function compares the selected boot size with the estimated boot size based on the selected harness size. It returns a recommendation message based on the comparison.

  5. Data Visualization:

    • The visualize_data(data) function creates a scatter plot using Plotly Express, showing the relationship between harness size and boot size in the dataset.

    • This plot includes a trendline generated by ordinary least squares (OLS) regression.

  6. Main Function:

    • main(): This is the main function of the Streamlit application.

    • It creates the title and introductory text for the web app.

    • It adds a sidebar with an option to view the data visualization.

    • It provides input fields for users to enter the harness size and the boot size they are considering.

    • When the user clicks the "Check Boot Size" button, it calls the check_size_of_boots() function to get a recommendation based on the entered sizes.

  7. Execution:

    • if __name__ == "__main__":: This block ensures that the main() function is executed when the script is run as the main program.

Overall, this code creates a Streamlit web application called "PawFit" that allows users to input their dog's harness size and the boot size they are considering, and provides recommendations based on a trained linear regression model. Users can also visualize the relationship between harness size and boot size in the dataset.

Certainly! Let's enhance the project with more features, interactivity, and potentially real-world applications to make it suitable for a final year computer science project. Here's a refined version:

Title: "PawFit: Intelligent Dog Gear Sizing and Recommendation System"

Introduction:

"PawFit" aims to revolutionize the way owners select gear for their dogs, particularly focusing on harnesses and boots. This project leverages machine learning techniques to estimate optimal boot sizes based on harness measurements, ensuring a comfortable and secure fit for working dogs, such as avalanche rescue canines. The system provides personalized recommendations to enhance the safety and performance of these heroic animals.

These are some of the features that can be added to make it better:

  1. Streamlined User Interface: Implement a user-friendly interface using Streamlit, allowing owners to input their dog's measurements and preferences effortlessly.

  2. Machine Learning Model: Develop a robust machine learning model (e.g., Linear Regression, Random Forest) to predict the optimal boot size based on harness measurements. Experiment with various algorithms to improve accuracy.

  3. Data Collection and Augmentation: Gather a diverse dataset of dog measurements, including breed, weight, and activity level, to enhance the model's performance. Augment the dataset using techniques like data synthesis to address class imbalances.

  4. Real-time Feedback: Incorporate real-time feedback mechanisms to refine the model continually. Allow users to provide feedback on recommended boot sizes, which can be used for model retraining.

  5. Dynamic Visualization: Utilize interactive visualization tools (e.g., Plotly, Matplotlib) to illustrate the relationship between harness size, boot size, and dog characteristics. Enable users to explore data patterns intuitively.

  6. Multi-Platform Deployment: Extend the application's reach by deploying it as a web application accessible from desktop and mobile devices. Employ cloud services (e.g., AWS, Heroku) for scalable deployment.

  7. Integration with E-commerce Platforms: Partner with pet gear retailers to integrate PawFit recommendations into their e-commerce platforms. Enable seamless purchasing of recommended products directly from the application.

  8. Accessibility and Inclusivity: Ensure accessibility features (e.g., screen reader support, keyboard navigation) to accommodate users with disabilities. Offer multilingual support to cater to diverse user demographics.

  9. Security and Privacy: Implement robust security measures to protect user data and ensure compliance with privacy regulations (e.g., GDPR, CCPA). Utilize encryption techniques for data transmission and storage.

Evaluation:

  1. Accuracy and Performance: Evaluate the model's accuracy and performance metrics (e.g., Mean Absolute Error, R-squared) using cross-validation techniques. Compare different machine learning algorithms to identify the most effective approach.

  2. User Satisfaction: Conduct user surveys and interviews to assess user satisfaction with the application's usability, usefulness, and overall experience. Incorporate user feedback to guide iterative improvements.

  3. Impact Assessment: Measure the impact of PawFit on the safety and well-being of working dogs, such as reduced instances of discomfort or injuries. Collaborate with canine rescue organizations to gather feedback on the system's effectiveness.

Conclusion:

"PawFit" represents a pioneering solution in the domain of canine gear selection, leveraging advanced technology to ensure optimal comfort and performance for working dogs. By combining machine learning, interactive interfaces, and real-time feedback mechanisms, PawFit sets a new standard for personalized pet care. This project not only showcases technical prowess but also demonstrates a commitment to animal welfare and innovation in the pet industry.

Future Directions:

  • Expand the scope of PawFit to include additional types of dog gear, such as collars, leashes, and protective vests.

  • Integrate sensor technologies (e.g., IoT devices, wearables) to collect real-time physiological data from dogs and enhance gear recommendations.

  • Collaborate with veterinary professionals to incorporate health-related considerations into gear sizing recommendations, addressing issues like joint support and injury prevention.

By addressing these aspects comprehensively, PawFit has the potential to make a significant impact on the lives of working dogs and their dedicated handlers, while also advancing the field of animal-centric technology.