Decoding Zomato Customer Behavior with Python

Project Description: In this project, I conducted an in-depth analysis of Zomato's customer behavior to gain valuable insights into user preferences and engagement patterns. The primary focus was on understanding the dynamics of the restaurant industry through the lens of user data on the Zomato platform. The project involved meticulous data cleaning and preprocessing to ensure the reliability of the dataset.

Tools Used:

The analysis was powered by industry-standard tools to extract meaningful information:

• NumPy and Pandas: These tools were instrumental in cleaning and preprocessing the extensive Zomato dataset, ensuring the integrity and accuracy of the data.
• Matplotlib and Seaborn: Python libraries used for creating insightful visualizations. These visualizations highlighted crucial restaurant metrics, average ratings, top-performing restaurants, and customer preferences.

Key Questions and Insights:

• Average Rating of Each Restaurant:
  • Calculated and displayed the average rating of each restaurant using Pandas and NumPy.
  • Presented the top 20 restaurants with the highest ratings.
• Distribution of Ratings:
  • Utilized Seaborn to visualize the distribution of ratings.
  • Identified and explained a spike in the 0 rating, attributed to newly added restaurants.
• Normality Test for Ratings:
  • Conducted a normality test using the Scipy library to assess the distribution of ratings.
  • Concluded that the ratings data is not normally distributed.
• Top Restaurant Chains by Number of Outlets:
  • Displayed the top restaurant chains based on the number of outlets.
• Restaurants Accepting Online Orders:
  • Utilized a pie chart to visualize the proportion of restaurants accepting and not accepting online orders.
• Restaurants Offering Table Booking:
  • Employed a pie chart to showcase the percentage of restaurants offering and not offering table bookings.
• Types of Restaurants:
  • Explored the unique types of restaurants present in the dataset.
  • Visualized the distribution of different restaurant types.
• Restaurant with the Highest Voting:
  • Identified and visualized the restaurant with the highest average voting.
• Top 10 Cuisines:
  • Explored the top cuisines based on the frequency in the dataset.
  • Presented a bar chart to showcase the distribution of the top 10 cuisines.