Project Overview: Zomato Data Analysis

This project analyzes data from a Zomato dataset to uncover key insights into restaurant trends, customer preferences, and other attributes. Various Python libraries, including pandas, numpy, matplotlib, and seaborn, were used to clean, process, and visualize the data effectively.

Key Components of the Code:

1. Data Preparation:

   • Loaded the Zomato dataset using pandas.
   • Displayed the initial structure using df.head() and checked for missing values with df.isnull().sum().

2. Data Cleaning:

   • Applied a custom function handleData() to clean and convert restaurant ratings stored in an inconsistent format to float.

3. Visualizations and Insights:

   • Restaurant Type Distribution: Created a count plot to show the distribution of restaurant types (sns.countplot), revealing that most restaurants fall into the dining category.
   • Votes by Restaurant Type: Grouped data by restaurant type to sum up votes, visualized using a line plot. It highlighted that dining restaurants received the most votes.
   • Restaurant with Maximum Votes: Extracted and printed the name of the restaurant(s) with the highest votes.
   • Online Order Analysis: A count plot indicated that the majority of restaurants do not accept online orders.
   • Rating Distribution: A histogram was plotted to show the distribution of restaurant ratings, with most ratings between 3.5 and 4.
   • Approximate Cost Analysis: Visualized with a count plot, showing that a large number of restaurants have an approximate cost of 300 rupees for two people.
   • Online Order vs. Rating: Created a box plot that demonstrated higher ratings for online orders compared to offline orders.
   • Order Mode by Restaurant Type: A heatmap from a pivot table highlighted the preference for offline orders at dining restaurants and online orders at cafes.

Conclusions Drawn:

• Dining Preference: The majority of restaurants analyzed fall into the dining category, which also attracts more customer votes.
• Top Restaurant by Votes: "Empire Restaurant" was found to have the highest number of votes.
• Online Order Insights: Most restaurants do not offer online ordering, but those that do receive higher ratings on average.
• Cost Preferences: A significant number of couples prefer dining in places with an approximate cost of 300 rupees.
• Ordering Trends: Offline orders are favored at traditional dining spots, while cafes see a trend towards online ordering.

This analysis provides actionable insights into customer preferences and restaurant trends in the dataset.