Gift Recommendation System based on Customer Segmentation using Machine Learning
The Gift Recommendation System is an application that provides personalized gift recommendations based on user preferences and recipient characteristics. It utilizes advanced algorithms to analyze user input, and previous ratings to generate tailored gift suggestions. The system aims to simplify the gift selection process and enhance the overall gifting experience.
Personalized Preferences: Users provide responses to specific questions regarding the requirements of the gift recipient, such as hobbies, interests, favorite brands, colors, and styles. Cluster-Based Recommendations: The system uses a database of previous user ratings to create clusters based on attributes like age, gender, and interests. Users are then assigned to specific clusters to receive targeted recommendations. Top n Recommended Products: Based on the user's cluster and preferences, the system presents some top recommended products that align with the recipient's characteristics and preferences. User Evaluation and Feedback: Users have the opportunity to evaluate the recommended items and assign relevance ratings to each of them, providing feedback to improve future recommendations. Machine Learning Model Training: The user's data, including their preferences and feedback, is incorporated into the dataset, and the machine learning model is retrained to enhance future recommendations. Screenshots [Include relevant screenshots or visual representations of the app's user interface to provide a visual understanding of the system.]
Front-end: HTML, CSS, JavaScript Back-end: Python, Django framework Machine Learning: Scikit-learn library Database: MongoDB, CSV
On the app's homepage, click on the "Start" or "Get Started" button to initiate the gift recommendation process. You will be prompted to answer specific questions regarding the preferences and characteristics of the gift recipient. These questions may include details about hobbies, interests, favorite colors, and more. Provide accurate and relevant information to ensure the system generates personalized recommendations that align with the recipient's tastes.
After providing your responses, the app's algorithms will analyze your inputs and categorize you into a specific cluster based on attributes like age, gender, and interests. The system will consider the preferences of users within the same cluster to offer recommendations that are likely to resonate with your recipient's profile. These cluster-based recommendations ensure that the suggestions are tailored to the recipient's demographic and preferences.
Once the system has identified your cluster and analyzed your inputs, it will present a list of the top 5 recommended products. These recommendations are based on the preferences of users within your cluster and aim to provide a diverse range of gift options that suit your recipient's interests. Browse through the list to find items that resonate with you and your recipient.
Evaluate each of the recommended items by assigning a relevance rating to them. This rating reflects how well each item aligns with your recipient's preferences. Honest and accurate ratings provide valuable feedback to the system, allowing it to further refine its recommendations and improve the accuracy of future suggestions. Your feedback contributes to the continuous enhancement of the gift recommendation process.
Your preferences, along with your evaluation and feedback, are incorporated into the system's dataset. The machine learning model is then retrained using this updated dataset, enabling it to learn from your interactions and improve its predictions over time. As you use the app and provide feedback, you contribute to enhancing the overall effectiveness of the recommendation system.
Contact Twinshu Parmar: twinshup@gmail.com