Introduction to Data Science

In data science, data analysis and machine learning techniques are applied to visualize data, understand trends, and make predictions. In this course students will learn how to obtain data, preprocess it, apply machine learning methods, and visualize the results. A student who completes the course will have enough theoretical knowledge, and enough skill with modern statistical programming languages and their libraries, to define and perform complete data science projects.

Assessing Canine Bite Incidents and Shaping Breed Perceptions: A Data-Driven Approach

Link to project code on GitHub

This project goal was to challenge stereotypes about dog breeds by analyzing bite incident data and employing machine learning techniques. By providing evidence-based insights, we hope to facilitate informed decisions for potential dog adopters, promoting responsible pet ownership and increasing successful placements.

Objectives:

Investigate bite incident likelihood based on size, breed, and age.
Analyze disparities between public perceptions of breed-related dangers and actual bite incident data.
Develop a machine learning model to predict dog bite likelihood, aiding prospective owners in making informed choices.

Methodology:

Data Collection: Gather comprehensive bite incident data, including variables such as size, breed, age, and circumstances. Augment with demographic information for assessing public perception.
Data Analysis: Apply statistical techniques and machine learning algorithms to identify patterns and correlations.
Evaluation: Validate the predictive model using cross-validation and performance metrics.

Expected Outcomes:

Understand the relationship between dog size, breed, age, and bite incidents.
Compare public perception with actual bite incident data, revealing the influence of stereotypes.
Develop a robust machine learning model to predict bite likelihood, empowering potential adopters.

Implementation:

Data Collection and Preparation: Retrieve incident data and collate perception data, ensuring data integrity.
Analysis and Model Development: Apply statistical analysis and machine learning algorithms to develop and validate predictive models.
Visualization and Reporting: Communicate findings through visualizations, reports, and tools.

Impact and Future Scope:

This project dispels breed misconceptions, improving the adoption process. The model can integrate into adoption platforms, offering personalized recommendations. Future research can explore additional factors and expand the model's scope.

By leveraging data and analytics, we hope to foster informed perspectives on canine behavior, enhancing human-dog relationships.