The Restaurant Rating Prediction Project is a machine learning-based application that predicts the ratings of restaurants based on various features. Leveraging data analysis and predictive modeling, this project aims to provide insights into the factors influencing restaurant ratings, ultimately assisting users in making informed decisions about where to dine.
- Data Exploration: Gain insights into the dataset through exploratory data analysis (EDA) to understand the distribution of features, correlations, and potential trends.
- Predictive Modeling: Utilize machine learning algorithms to build a predictive model capable of estimating restaurant ratings based on input features.
- User-Friendly Interface: Interact with the model through an intuitive user interface that allows users to input restaurant features and receive a predicted rating.
https://drive.google.com/file/d/1h9lewh8SyR4IYSjRmmt4kvPqJ4GCZ5BD/view?usp=drive_link
Implementing MLOps ensures a seamless and automated machine learning lifecycle. Continuous Integration and Continuous Deployment (CI/CD) pipelines automate testing, model training, and deployment processes.
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DAGs Hub: DAGs Hub is used to orchestrate and automate workflows. View and manage your Directed Acyclic Graphs (DAGs) with DAGs Hub.
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CI/CD Pipeline: The CI/CD pipeline is set up to automatically trigger testing, model training, and deployment steps on each code push.
Deploying the Restaurant Rating Prediction Project on AWS ensures scalability and accessibility. The application is hosted on an AWS instance for seamless access.
This project is licensed under the MIT License.