Colic in horses is a serious and often life-threatening gastrointestinal illness and the leading cause of death in horses. The unpredictable nature and the variety of symptoms colic presents make predicting the survival probability of horses difficult. The primary objective of this project is to utilize a logistic regression machine learning model using a dataset acquired from Kaggle that contains detailed information on horse symptoms and treatments, and whether the horse lived, died, or required euthanasia.

By implementing this project, I aim to reduce reliance solely on individual veterinarians' medical experience concerning colic and enable medical staff to more effectively predict the horses' prognosis based on accurately calculated data. The goal is to assist horse owners and veterinarians in making informed decisions concerning equine health through survival probability statistics.

My motivation behind this project is more than just a Capstone project for my Bachelor of Science in Computer Science. As a horse owner myself, my logic behind undertaking this project stems from a combination of personal passion for horses and their health, and my professional expertise in leveraging technology to solve complex problems. My first-hand experiences with horses give me insight into the challenges of diagnosing and treating colic. As a professional in the tech industry, I have the technical skills necessary to develop and implement advanced machine learning models. This project allows me to apply my expertise in a meaningful and impactful way.

I have implemented a logistic regression model for predicting the survival probability of horses with colic. Doing so offers several significant benefits:

1. It enhances diagnostic accuracy by using historical data to identify patterns and relationships that might not be apparent through traditional methods, thereby improving the accuracy of prognosis. This increased accuracy will aid veterinarians in making more informed decisions, leading to better treatment outcomes.
2. The model decreases the reliance solely on individual veterinarian experience by standardizing the diagnostic process across veterinarian clinics. This will ensure consistent quality of care.
3. The system will help identify best practices and effective treatments, encouraging continuous improvement. This will lead to higher survival rates due to improved patient care, and will increase trust from horse owners.
4. The system will help with resource optimization. By predicting the survival probability accurately, veterinary clinics can better allocate their resources. High-risk cases can be prioritized, ensuring that the most critical patients receive the attention and treatment they need promptly.
5. The model can serve as a valuable educational tool for training new veterinarians. By analyzing the model's predictions and comparing them to actual outcomes, new veterinarians can learn about the key indicators, symptoms, and treatments that affect survival rates.
6. Implementing the model may encourage better record-keeping practices, as accurate and complete data is essential for the model's performance. This can lead to improved overall data quality and more reliable medical records.
7. Having a model that provides clear, data-driven predictions can improve the relationship with horse owners. Veterinarians can explain the prognosis and treatment options with greater confidence, backed by the model's predictions.
8. The system can be used to benchmark the performance of different clinics across the globe. Clinics with higher survival rates can be studied to identify best practices that can be implemented across other clinics.

This project allows me to combine my passion for horses and technology. It allows me to make a difference, knowing that my work has the potential to improve the quality of care for horses and perhaps increase the survival rate of horses with colic. I am driven by a desire to make a positive impact on equine health through the innovative application of technology, leveraging my unique combination of personal experience and professional expertise to do so.