The Zomato Restaurant Rating Predictor is a Flask web application that predicts the rating of restaurants on Zomato based on various features. This project aims to provide users with an interactive platform to estimate restaurant ratings and explore the factors influencing them.
- Predictive Modeling: Utilizes machine learning models to predict restaurant ratings.
- Interactive Interface: A user-friendly web application powered by Flask for easy input and visualization.
- Scalable: Designed to handle a large number of restaurants and diverse features.
- Insightful Visualizations: Presents insightful graphs and charts for a better understanding of predictions.
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Python 3.8
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Dependencies List
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Flask: A lightweight web application framework.
- Install:
pip install Flask - Purpose: Used for building web applications and serving the application in a server environment.
- Install:
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Scikit Learn: A machine learning library in Python.
- Install:
pip install scikit-learn - Purpose: Utilized for implementing machine learning models and data preprocessing in the project.
- Install:
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Pandas: A powerful data manipulation and analysis library.
- Install:
pip install pandas - Purpose: Used for handling and processing structured data.
- Install:
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NumPy: A fundamental package for scientific computing with Python.
- Install:
pip install numpy - Purpose: Provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
- Install:
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Seaborn: A data visualization library based on Matplotlib.
- Install:
pip install seaborn - Purpose: Enhances the visual appeal of statistical graphics created with Matplotlib.
- Install:
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Matplotlib: A comprehensive library for creating static, interactive, and animated plots.
- Install:
pip install matplotlib - Purpose: Essential for generating various types of plots and charts.
- Install:
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Plotly: An interactive graphing library for Python.
- Install:
pip install plotly - Purpose: Used for creating interactive and visually appealing plots and dashboards.
- Install:
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Pandas Profiling: A library for easy and quick exploratory data analysis with Pandas.
- Install:
pip install ydata_profiling - Purpose: Generates a profile report of the dataset, offering insights into data distributions, missing values, correlations, etc.
- Install:
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Pickle: A module for serializing and deserializing Python objects.
- Comes with Python standard library, no separate installation required.
- Purpose: Used for saving and loading machine learning models or other Python objects.
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Flask: Special thanks to the Flask framework for making web development in Python elegant and straightforward.
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Scikit-learn: We appreciate the Scikit-learn library for providing powerful tools for predictive modeling.
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NumPy: Heartfelt thanks to the NumPy community for developing a fundamental library that forms the backbone of numerical computing in Python.
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Pandas: Special appreciation to the Pandas development team for creating an indispensable tool for data manipulation and analysis, making our project more efficient and effective.
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Matplotlib: A big thanks to the Matplotlib developers for providing an extensive and flexible plotting library, adding a visual dimension to our data exploration and presentation.
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Seaborn: We express our gratitude to the Seaborn community for enhancing our data visualization capabilities with a high-level interface to Matplotlib, making our plots more aesthetically pleasing and informative.
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Plotly: Special thanks to the Plotly team for providing an outstanding visualization library.
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Open Source Community: Gratitude to the broader open-source community for sharing knowledge and fostering collaboration.
Email : miteshgupta2711@gmail.com
Linkedin : https://www.linkedin.com/in/mitesh-gupta/
Twitter : https://twitter.com/mg_mitesh