Introduction
Predictive modeling plays a crucial role in various industries, including data science. In this assignment, we will explore the application of predictive modeling for cat shelter outcomes. The dataset provided contains information about shelter outcomes for cats, including attributes such as breed, time frequency, and outcome type. Our goal is to build a predictive model that can accurately predict the outcome result of cats based on the selected attributes.
Problem Statement
The problem at hand is to develop a predictive model that can effectively determine the outcome type for cats in a shelter. By selecting the appropriate attributes from the dataset, cleaning the data, and utilizing a suitable predictive model, we aim to provide insights into the likely outcomes for cats in the shelter. This predictive model will assist shelters in better managing their resources, making informed decisions, and improving the overall welfare and adoption rates of cats.
Dataset
The dataset provided, named "2022_shelter_cat_outcomes.csv," contains a comprehensive set of information regarding cat shelter outcomes. With a total of 37 attributes and 29,421 tuples, this dataset offers valuable insights into various aspects of the shelter and the cats' outcomes. By processing and analyzing this dataset, we can derive useful information and build a predictive model.
Our Approach
At CodersArts, we employ advanced data science techniques to tackle real-world problems. For this assignment, we will follow a systematic approach to clean the data, select appropriate attributes, and build a predictive model for cat shelter outcomes. Throughout the process, we will utilize industry-standard tools and techniques, ensuring the reliability and accuracy of our results.
Cleaning the Data and Attribute Selection
To start the analysis, we will first clean the data and select the relevant attributes for our predictive model. It is essential to choose attributes that provide valuable insights into the outcome type of cats. Some attributes may be redundant or have missing values, which will require careful handling. Based on our analysis, we will exclude certain attributes such as "animal_Id," "animal_type," "datetime," "wmonthyear," "outcome_subtype," "count," "dob_year," "dob_month," "dob_monthyear," "outcome_hour," "breed?," and "breed2."
Pivot Table Analysis
To gain further insights, we will create a pivot table to analyze the adoption count of cats and kittens based on weekdays. By using suitable aggregation methods, we can identify trends and patterns in adoption rates. This analysis will help us understand the most and least preferred days for adoption and the corresponding number of adoptions.
Visualization of Results
To present the adoption count on each weekday for both cats and kittens, we will employ a suitable visualization method. Visualizing the data in a single figure will provide a clear and concise representation of the adoption trends throughout the week.
Model Training and Testing
After cleaning and analyzing the data, we will partition it into training and test sets. The training set will consist of 90% of the data, while the remaining 10% will be used for testing. We will select a predictive model that yields an accuracy of above 70% for the outcome_type prediction. This model will help predict the outcome type of cats in the shelter based on the selected attributes.
In this project we address the following tasks/Questions.
Which attribute is used as the row header (group) for creating the pivot table?
Which attribute is used as the column header (pivots) for creating the pivot
Which attribute is used in the aggregation for creating the content of the pivot table?
What is the aggregation method used in the pivot table for obtaining the values?
On which day of a week have the most kittens been adopted?
How many kittens are adopted on that day (answered in question 5)?
On which day of the week have the least cats been adopted?
How many cats are adopted on that day (answered in question 7)?
Which model did you select for creating the predictive model?
How many attributes are used as input (excluding the target column) for the model?
How many tuples are included in the training dataset?
What is the accuracy value of your test result?
List the precision of the Transfer class.
List the recall of the Transfer class.
List the precision of the Adoption class.
List the recall of the Adoption class.
List the precision of the Return to Owner class.
List the recall of the Return to Owner class.
List the precision of the Euthanasia class.
List the recall of the Euthanasia class.
Explanation and Submission
Throughout the assignment, we will document our process of data cleaning, attribute selection, model training, and testing. We will provide detailed explanations of how we obtained the answers to each question, including screenshots of our workflow or code snippets if applicable. This documentation will be submitted in MS Word or PDF format, adhering to the submission requirements specified for this assignment.
If you require assistance with the solution mentioned above or have any queries regarding this project, please feel free to contact us. Our team at CodersArts is here to help you leverage the power of predictive modelling and provide you with the solutions you need to enhance your data science journey. Reach out to us via email or visit our website, and we will be glad to assist you in achieving your goals.
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