Download the dataset below to solve this Data Science case study on Dating Recommendations.
Accurate dating recommendations enhance the user experience by providing personalized dating recommendations to facilitate meaningful connections. We have access to a dataset containing user profiles with various attributes such as age, gender, height, interests, education level, occupation, and more. These profiles are associated with user behaviours, including swiping history and frequency of app usage.
The dataset consists of the following columns:
- User ID: A unique identifier for each user.
- Age: The age of the user.
- Gender: The gender of the user (e.g., Male, Female).
- Height: The height of the user.
- Interests: A list of interests or hobbies expressed by the user.
- Looking For: The user’s dating preferences, such as “Long-term Relationship,” “Marriage,” etc.
- Children: Indicates whether the user has children (“Yes,” “No,” “Maybe”).
- Education Level: The user’s highest education level.
- Occupation: The user’s current occupation.
- Swiping History: A numerical score representing the user’s interactions (e.g., likes/dislikes) with other profiles.
- Frequency of Usage: How often the user engages with the dating app (e.g., “Daily,” “Monthly”).
Your task is to design and implement a data-driven matchmaking and recommendation system for the dating app. The objective is to generate meaningful dating recommendations by considering user-profiles and behaviours while respecting their preferences.
