Animal Lifestyle and Traits Dataset

Columns

• Breed: Specifies the cat's breed (Ragdoll, Maine Coon, or Angora). Ragdoll accounts for 41%, Maine Coon 32%, with others making up 27% of the records.
• Age_in_years: The cat's age, ranging from 0.08 to 11.3 years, with a mean of 4.85 years.
• Age_in_months: The cat's age in months, spanning 1 to 135 months, with a mean of 58.1 months.
• Gender: Indicates if the cat is male or female, with an equal distribution of 50% each.
• Neutered_or_spayed: A boolean field showing whether the cat has been neutered or spayed (58% true, 42% false).
• Body_length: The body length of the cat, ranging from 10.00 to 102.00, with a mean of 44.
• Weight: The cat's weight, from 0.50 to 12.10, with a mean of 5.49.
• Fur_colour_dominant: The primary fur colour, with 'seal' being most common at 28% and 'white' at 25%.
• Fur_pattern: Describes the fur pattern, with 'solid' being the most prevalent at 43% and 'colorpoint' at 32%.
• Eye_colour: The colour of the cat's eyes, with 'blue' being most common at 51% and 'yellow' at 24%.
• Allowed_outdoor: A boolean field indicating if the cat is allowed outdoors (9% true, 91% false).
• Preferred_food: The cat's preferred food type, either 'wet' (70%) or 'dry' (30%).
• Owner_play_time_minutes: The amount of time in minutes the owner plays with the cat, ranging from 0 to 60 minutes, with a mean of 23 minutes.
• Sleep_time_hours: The cat's sleep duration in hours, ranging from 8 to 22 hours, with a mean of 15.9 hours.
• Country: The country of residence, with USA (62%) and UK (13%) being the most frequent.
• Latitude: The geographical latitude of the cat's location, ranging from 37.8 to 53.8, with a mean of 44.4.
• Longitude: The geographical longitude of the cat's location, ranging from -123 to 13.4, with a mean of -60.2.

Distribution

Usage

• Machine Learning: Enabling tasks such as classification (e.g., breed identification), regression (e.g., predicting weight or age), and clustering.
• Exploratory Data Analysis (EDA): For gaining insights into cat characteristics and behaviours.
• Data Visualisation: To graphically represent trends and patterns within the feline data.
• Data Cleaning: The 'dirty' version offers a practical challenge for refining data preprocessing skills.
• Geospatial Analysis: Utilising the country, latitude, and longitude data to explore geographical distributions.

Coverage

• Geographic Scope: The dataset includes data from various countries, predominantly the USA (62%) and UK (13%), with corresponding latitude and longitude values provided.
• Demographic Scope: It covers three specific cat breeds: Maine Coon, Ragdoll, and Angora. Information on gender (male and female) and neutered/spayed status is also included.
• Time Range: While no specific time range for data collection is given, individual cat ages range from 0.08 to 11.3 years, providing an age distribution.
• Data Availability: The data is artificially generated, ensuring a balanced representation across different attributes such as gender and providing a controlled environment for analytical tasks.

License

Who Can Use It

• Data Scientists and Machine Learning Engineers for building and testing models (e.g., predicting cat health based on attributes).
• Data Analysts and Researchers for exploring animal behaviours, characteristics, and distributions.
• Students and Beginners in data science for learning data cleaning, EDA, visualisation, and fundamental ML techniques.
• Anyone interested in geospatial studies related to animal populations or characteristics.

Dataset Name Suggestions

• Feline Breed Characteristics Dataset
• Domestic Cat Attributes for ML
• Artificially Generated Cat Data
• Animal Lifestyle and Traits Dataset
• Cat Population Metrics

Attributes