🍽️ Restaurant Ratings - Data Exploration and Preprocessing

This notebook contains Task 1 from my Data Science internship, where I performed detailed Exploratory Data Analysis (EDA) and Data Preprocessing on a restaurant dataset.

🔍 Task Objective

Analyze and prepare the dataset by understanding its structure, handling missing values, correcting data types, and exploring the target variable.

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Key Steps Performed

1. Dataset Overview

• Loaded CSV data using pandas
• Checked the number of rows and columns (df.shape)
• Previewed the first few records with df.head()

2. Missing Value Treatment

• Identified 9 missing values in the Cuisines column
• Handled them using fillna("Unknown")

3. Data Type Conversion

• Converted 4 columns (Has Table booking, Has Online delivery, Is delivering now, Switch to order menu) from "Yes"/"No" (object) to True/False (boolean)

4. Target Variable Analysis - Aggregate rating

• Counted and visualized rating distribution
• Identified a high number of unrated entries (0.0)
• Noted class imbalance, with most ratings falling between 3.0 and 4.0

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Tools Used

• Python
• Pandas
• Jupyter Notebook

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