In this project, we aim to provide valuable insights to a client managing an animal shelter. Our goal is to develop a model that predicts the likelihood of an animal being adopted, while also gaining a deeper understanding of the factors that influence adoption rates. Through data analysis and visualization, we will explore relationships between various features and outcomes, helping the client make informed decisions about animal donations and improve the overall adoption process.
##Problem Statement The client operates an animal shelter and is interested in understanding which animals are more likely to be adopted and the underlying reasons for successful adoptions. They have provided a dataset containing information about animal intakes and outcomes, including details such as age, breed, color, intake conditions, and outcome types.
I have used Animal Shelter Data provided on Kaggle. The dataset contains almost 80000 records with features such as age, animal, breed, color, found location, intake condition, sex and outcome etc. Through data analysis and visualization, I explore relationships between various features and outcomes, helping the client make informed decisions about animal donations, improve the overall adoption process and shelter sustainability.
To start Exploratory Data Analysis (EDA) process, I have identified key questions that can provide insights into the factors influencing adoption likelihood.
- What is the distribution of outcome types (e.g., adoption, transfer, return to owner) in the dataset, and which is the most common?
- Do certain animal characteristics like color or breed influence adoption rates?
- Are there specific months or seasons when adoptions are more likely to occur?
- Are there any patterns in the day of the week or hour of intake that correlate with adoption rates?
- What is the average length of time an animal spends in the shelter before adoption, and does it vary by outcome type?
- Are there any common outcome subtypes that are strongly associated with adoption or non-adoption?
THe detail report of EDA can be found on the below link.
Data driven compassion using data science to maintain a suntainable shelter home
- EDA.ipynb --- Exploratory data analysis notebook
- README.md --- readme file
- requirements.txt --- requirements to run the project and notebook
pip install -r requirements.txt
Dataset is provided at kaggle platform under the following lincense. Lincense: U.S. Government Works Author: Saurabh Bhardwaj