This repository contains a 'Flask Restaurant Rating Predictor'. It is a Flask-based web application that predicts restaurant ratings based on various features. The goal of this project is to provide insights to restaurant owners about factors influencing their ratings and to help customers in making informed choices.
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Data Preprocessing: The first step involved cleaning and preprocessing the data to make it suitable for training. This included handling missing values, removing outliers, and encoding categorical variables.
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Model Training: I experimented with different machine learning algorithms including Linear Regression, Decision Trees, and Random Forests. Each model was trained and validated using cross-validation to ensure robust performance.
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Model Evaluation: The performance of each model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. This helped in identifying the most accurate model for prediction.
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Model Deployment: The best-performing model was then deployed using Flask, a lightweight web server gateway interface (WSGI) web application framework. This allowed the model to be accessed via a simple web interface, enabling real-time predictions.
This project was a fantastic opportunity to apply machine learning concepts to a real-world problem. It was particularly interesting to see how different algorithms performed on the same task and how model performance can be optimized.
I’m excited about the potential applications of this project and look forward to exploring more ways to leverage machine learning in the future. 💡
I'm a Full Stack Data Scientist
- C, C++, Python
- SQL
- Machine Learning
- Deep Learning
- Data Science
👩💻 I'm currently a student
🧠 Btech Computer Science
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