This project is a comprehensive analysis of Zomato's dataset to uncover insights about customer preferences, restaurant ratings, and spending patterns. Using Python's data analysis libraries, we performed data cleaning, transformation, and exploratory data analysis (EDA) to answer specific business questions related to restaurant types, ratings, and order modes.
To run this project, ensure you have the following Python libraries installed:
- Pandas
- Numpy
- Matplotlib
- Seaborn
Install the libraries using pip if you haven't already.
The dataset used for this analysis is named Zomato data.csv. Ensure the file is in the same directory as your script or provide the correct path.
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Load and Inspect the Data We start by loading the dataset and inspecting the first few rows to understand its structure and content.
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Data Cleaning: Handling the "Rate" Column The "rate" column contains ratings in a string format (e.g., 4.5/5). We need to convert this into a float for analysis.
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Data Summary We inspect the summary of the dataframe to check for any null values and understand the data types. [Conclusion: There are no null values in the dataframe, so no further data cleaning is necessary.]
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Exploratory Data Analysis (EDA)
Conclusion: Dining restaurants primarily accept offline orders, while cafes receive more online orders.
This analysis provides insights into customer behavior and preferences on Zomato. Key findings include the dominance of Dining category orders, higher ratings for online orders, and spending patterns of couples. These insights can be used by restaurants and Zomato to improve their services and tailor their offerings to customer needs.
Further Analysis: Extend the analysis to include other factors such as location-based preferences or time-based order patterns. Modeling: Develop predictive models to forecast restaurant ratings or customer spending.