Descriptive Analysis

This notebook contains the Descriptive Analysis portion of my Data Science internship, focused on understanding the structure and distribution of both numerical and categorical variables in a restaurant dataset.

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Task Objective

• Compute statistical summaries for key numerical columns
• Explore and interpret the distribution of categorical features like Country Code, City, and Cuisines
• Identify the most frequent cuisines and cities in the dataset

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Steps Performed

1. Numerical Analysis

Selected relevant numerical features:

• Average Cost for two
• Price range
• Aggregate rating
• Votes

For these, calculated:

• Mean, Median, Standard Deviation
• Used .describe() to generate percentiles, min/max, and spread

Key Findings:

• Cost and Votes are highly skewed (long-tail distribution)
• Most ratings lie between 2.5 and 4.0
• Median cost is ₹400; average is skewed due to some high-end entries

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2. Categorical Analysis

a. Country Code

• 15 unique country codes
• Code 1 (India) appeared most frequently (8652 times)
• Created a mapping to actual country names using .map()

b. City

• 141 unique cities
• New Delhi had the highest number of restaurants (5473)
• NCR cities dominate the dataset

c. Cuisines

• 1825 unique cuisine combinations
• North Indian is the most common standalone cuisine (936 entries)
• Long-tail distribution with many unique/rare combinations

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🔧 Tools Used

• Python
• Pandas
• Jupyter Notebook

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