I'll help you create a recommendation engine for traditional Indian food. Let's break this down into parts: backend (Python/ML), frontend (HTML/CSS), and the integration.

Let's start with the backend Python code for data processing and the ML model:

Now, let's create a Flask API to serve the recommendations:

Now, let's create the frontend interface:

Let's add the CSS styling:

Finally, let's add the JavaScript code to handle the interactions:

To run this project:

1. First, install the required Python packages:

2. Start the Flask backend:

3. Open the index.html file in a web browser or serve it using a local server.

The application will:

• Load the food dataset from the provided URL
• Create a recommendation model using TF-IDF and cosine similarity
• Allow users to select a region and festival
• Display recommended traditional Indian dishes based on the selection
• Provide a responsive and user-friendly interface

The recommendation engine uses:

• TF-IDF vectorization to convert text data into numerical features
• Cosine similarity to find similar items
• Flask for the backend API
• Simple and clean frontend design with HTML, CSS, and JavaScript

You can enhance this further by:

1. Adding images for each dish
2. Including recipe details and cooking instructions
3. Adding user ratings and reviews
4. Implementing user accounts to save preferences
5. Adding more detailed filtering options

Would you like me to explain any part in more detail or make any specific enhancements to the code?

I'll create a Jupyter Notebook code that performs exploratory data analysis (EDA) and builds the recommendation engine. This will help you understand the data and model better.

This Jupyter Notebook includes:

1. Data Loading and Initial Exploration

   • Loads the dataset from the provided URL
   • Displays basic information about the dataset
   • Shows summary statistics

2. Data Analysis

   • Creates visualizations of food distribution by region
   • Analyzes festival-wise food distribution
   • Uses seaborn for statistical visualizations

3. Interactive Visualizations

   • Creates interactive treemap and sunburst charts using Plotly
   • Shows hierarchical relationships between regions and festivals

4. Recommendation Engine Building

   • Implements TF-IDF vectorization
   • Calculates cosine similarity
   • Creates the recommendation function

5. Recommendation Analysis

   • Analyzes similarity scores
   • Visualizes recommendation strengths

6. Cross-Regional Analysis

   • Creates a heatmap of food distribution
   • Analyzes relationships between regions and festivals

7. Festival Seasonality Analysis

   • Analyzes seasonal patterns in festival foods
   • Creates stacked bar charts for seasonal distribution

8. Model Saving

   • Saves the trained model components for later use

To use this notebook:

1. Install required packages:

2. Start Jupyter Notebook:

3. Open the notebook and run each cell sequentially.

The notebook provides detailed analysis and visualizations that will help you understand:

• Distribution of foods across different regions
• Festival-wise food patterns
• Seasonal trends in traditional foods
• Effectiveness of the recommendation system

You can enhance this notebook by:

1. Adding more sophisticated visualization techniques
2. Implementing different similarity metrics
3. Adding sentiment analysis for food descriptions
4. Creating more interactive visualizations
5. Adding feature importance analysis

Would you like me to explain any specific part of the analysis or add more visualization techniques?