Personalized Recommendation System using Machine Learning
The Personalized Recommendation System is an AI-based platform that provides personalized product recommendations to users based on their previous purchasing history, search queries, and browsing behavior. The platform aims to improve the user experience and increase user engagement by suggesting products that are most relevant to their interests.
1.Data Collection and Preprocessing:
The development team will collect data on user behavior, including user search queries, purchasing history, and browsing behavior. The data will be preprocessed to remove noise and outliers, and transformed into a format that can be used by machine learning algorithms.
2. Feature Engineering:
The team will perform feature engineering to extract relevant features from the data, such as user preferences, item popularity, and item similarity.
3. Machine Learning Models:
The team will perform feature engineering to extract relevant features from the data, such as user preferences, item popularity, and item similarity.
4. Testing and Evaluation:
The platform will be extensively tested and evaluated to ensure that the personalized recommendations are accurate and relevant to the user’s interests.
Project Timeline
Data Collection and Preprocessing:
4 weeks
Feature Engineering:
4 weeks
Machine Learning Models:
10 weeks
Integration with the Platform:
4 weeks
Testing and Evaluation:
6 weeks
Deployment and Maintenance:
Ongoing
Project Team
Project Manager:
Responsible for overseeing the project, managing timelines and resources, and ensuring that the project is completed on time and within budget.
Data Scientist:
Responsible for collecting and preprocessing data, performing feature engineering, and developing machine learning models.
Software Developers:
Responsible for integrating the machine learning models with the platform, testing and evaluating the platform, and deploying and maintaining the platform.
Ready To Move
into the WORLD OF AI
