🍽️ Restaurant Rating Predictor 🍴

Welcome to the Restaurant Rating Predictor project! This web app lets you predict the rating of a restaurant based on different features like cuisines, price range, and more. It's built using Flask, a trained machine learning model, and some spicy front-end flair! 🌶️

🛠️ How It Works

1. Enter details about the restaurant (like its city, cuisines, average cost, etc.) in the form on the homepage.
2. Hit the Predict button.
3. Our model will analyze the inputs and predict the restaurant's rating. 🎯

🧑‍🍳 Getting Started

1. Clone the repository:

git clone https://github.com/yourusername/restaurant-rating-predictor.git
cd restaurant-rating-predictor

2. Install the requirements:

pip install -r requirements.txt

3. IMPORTANT: Upload the trained model

The model file restaurant_rating_predictor.pkl is too big to be included here (GitHub wasn’t too happy about the size 😅). So, make sure to upload your trained model to the root folder. If you don’t have one yet, no worries! Train a model using your own dataset and save it like so:

import joblib

# Assuming 'model' is your trained ML model
joblib.dump(model, 'restaurant_rating_predictor.pkl')

4. Ensure your data file is in place

Make sure your dataset file (e.g., Dataset.csv) is correctly placed and the path in your code matches the file location. Update the path if necessary in the script where you load the dataset.

5. Run the app:

python app.py

6. Visit your local server:

Fire up your browser and head to http://127.0.0.1:5000/

🔮 Features

• User Input Form: Enter key restaurant details to predict ratings.
• Dark Mode: Because who doesn’t love a sleek dark theme? 🌙
• Result Page: Displays the predicted rating with a cool interface.
• Social Media Links: Connect with me on LinkedIn, GitHub, and Twitter! 🧑‍💻

📁 Project Structure

.
|-- app.py
|-- requirements.txt
|-- restaurant_rating_predictor.pkl (you need to upload this one!)
|-- static
|   |-- styles.css
|   |-- linkedin.png
|   |-- github.png
|   |-- twitter.png
|-- templates
    |-- index.html
    |-- result.html

🧃 Requirements

• Flask
• Pandas
• Scikit-learn
• Joblib

You can install them with:

pip install -r requirements.txt

🌐 Connect with Me

Let’s get social! Follow me or check out my other projects:

• LinkedIn: LinkedIn
• GitHub: GitHub
• Twitter: Twitter

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Now go predict some delicious restaurant ratings! 😋