The Global Restaurant Data Analysis project aims to analyze a diverse dataset containing information about restaurants from different countries and regions around the world. The dataset includes details such as restaurant names, locations, cuisines, ratings, reviews, and other relevant metrics. analyzed the data using pandas and numpy frameworks
- pycharm or vs code editor
- numpy
- pandas
- matplot lib
pip install pandaspip install numpypip3 install pandaspip3 install numpy
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Top Cuisines Determine the top three most common cuisines in the dataset. Calculate the percentage of restaurants that serve each of the top cuisines
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City Analysis Identify the city with the highest number of restaurants in the dataset. Calculate the average rating for restaurants in each city. Determine the city with the highest average rating.
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Price Range Distribution Create a histogram or bar chart to visualize the distribution of price ranges among the restaurants. Calculate the percentage of restaurants in each price range category.
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Online Delivery Determine the percentage of restaurants that offer online delivery. Compare the average ratings of restaurants with and without online delivery.
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Restaurant Ratings Analyze the distribution of aggregate ratings and determine the most common rating range. Calculate the average number of votes received by restaurants.
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Cuisine Combination Identify the most common combinations of cuisines in the dataset. Determine if certain cuisine combinations tend to have higher ratings.
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Geographic Analysis Plot the locations of restaurants on a map using longitude and latitude coordinates. Identify any patterns or clusters of restaurants in specific areas.
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Restaurant Chains Identify if there are any restaurant chains present in the dataset. Analyze the ratings and popularity of different restaurant chains
you can find the results on analysis.ipynb
