Analysis-of-Indian-Food-Cuisine-Dataset

🍛 Indian Cuisine Analysis

This repository presents a comprehensive analysis of an Indian Cuisine Dataset. The goal is to extract meaningful insights into dish characteristics, cuisine patterns, ratings, and cooking efforts using Python data science tools.

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📊 Features

• Load and clean a real-world Excel dataset of Indian dishes
• Analyze:
  • Missing values
  • Vegetarian vs Non-Vegetarian dishes
  • Cuisine-specific filtering (e.g., North Indian)
  • Top-rated dishes
  • Dishes filtered by course (main dish, side dish, etc.)
  • Time-based metrics: preparation, cooking, total time
• Ingredient-based search (e.g., dishes with garlic)
• Save filtered datasets as Excel files
• Visualizations:
  • Bar charts, pie charts, scatter plots, box plots, heatmaps
  • Effort vs Rating Analysis
  • Dashboard with multi-plot summary
• Outlier detection (IQR method)
• Summary metrics by cuisine and course type
• Recommendations: Best cuisines based on high rating + low effort

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📁 Dataset

• Format: Excel .xlsx
• Filename: Indain_Food_Cuisine_Dataset.xlsx
• Columns include:
  • Name of dish
  • Ingredients
  • Course name
  • Cuisine type
  • Ratings
  • Diet type
  • Preparation/Cooking/Total time

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🛠️ Installation

1. Clone this repository:

   git clone https://github.com/Mohammed-Saleh-Ishaq/Analysis-of-Indian-Food-Cuisine-Dataset.git

   Once cloned, navigate into the directory using:

   cd Analysis-of-Indian-Food-Cuisine-Dataset

2. Install required packages :

   pip install pandas matplotlib seaborn missingno openpyxl

3. Add the dataset:

   • Place Indain_Food_Cuisine_Dataset.xlsx in the root folder of the project.

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🚀 Run the Analysis

Execute the Python script:

python indian_cuisine_analysis.py

The script will:

1. Print insights to the console
2. Save filtered data as Excel files
3. Show multiple interactive plots

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📈 Sample Visuals

1. Pie chart of diet types

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2. Bar plots of top cuisines

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3. Dashboard summary (4-in-1 chart)

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🧠 Insights & Recommendations

--> 1. Cuisines with high average ratings and low average effort. --> 2. Best course types for ease or quality. --> 3. Outliers in time-based metrics. --> 4. Heatmap of correlation between time and ratings.

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📤 Output Files

--> 1. vegetarian_dishes.xlsx.
--> 2. top_rated_dishes.xlsx.
--> 3. Indain_Food_Cuisine_Dataset.xlsx.

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✅ Requirements

 --> 1. python
 --> 2. Libraries: panda , seaborn , Matplotlib , missingno , openpyxl , Vscode.

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