Skip to content

README.md

Latest commit

 

History

History
124 lines (87 loc) · 2.96 KB

README.md

File metadata and controls

124 lines (87 loc) · 2.96 KB
Raw

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.

📊 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

📁 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

🛠️ 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

  1. Add the dataset:

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

🚀 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

📈 Sample Visuals

  1. Pie chart of diet types Pie_chart _Figure_3
  1. Bar plots of top cuisines Bar Chart _Figure_2
  1. Dashboard summary (4-in-1 chart) Bar plot_Figure_11

🧠 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.

📤 Output Files

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

✅ Requirements

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