Restaurant Data Analysis - Level 1 | Cognifyz Technologies Internship

Summary

In this video, Shos Goos, a data analyst intern at Cogn Technology, walks viewers through his analysis of a restaurant dataset using Python. He addresses tasks such as identifying the top three most common quizzes, analyzing restaurant ratings and city distributions, and exploring price range and online delivery data. Through detailed steps, including data cleaning, value counts, groupings, and visualizations, Shos uncovers trends like the popularity of certain cuisines, the impact of online delivery on ratings, and price range distributions. His work showcases how data analysis can reveal insightful patterns about the restaurant industry.

Takeaways

• 😀 The analysis involves exploring a restaurant dataset using Python, covering quizzes, city statistics, price ranges, and online delivery services.
• 😀 Task 1 focuses on identifying the top three most common quizzes in restaurants, with 'North Indian,' 'Chinese,' and 'Fast Food' being the most frequent.
• 😀 In Task 1, the percentage of restaurants serving the top quizzes is calculated, with 'North Indian' leading at 41% of restaurant offerings.
• 😀 Task 2 involves city analysis, revealing that New Delhi has the highest number of restaurants, with 5473 locations.
• 😀 Task 2 also calculates average restaurant ratings per city, with New York City having the highest average rating of 4.9.
• 😀 Task 3 analyzes price range distribution, showing the highest frequency in the 4000 price range (46.5%) and the lowest in the under 1000 range (6%).
• 😀 Task 3 calculates the percentage of restaurants in each price range, providing valuable insight into price distribution across restaurants.
• 😀 Task 4 examines the impact of online delivery services, finding that 2451 restaurants provide online delivery, while 710 do not.
• 😀 In Task 4, average ratings for restaurants with online delivery (3.25) are significantly higher than those without (2.47), indicating better customer satisfaction.
• 😀 The data analysis uses various Python functions like `value_counts()`, `mean()`, and `groupby()` to perform calculations and draw insights about restaurant offerings and ratings.
• 😀 Visualizations, including bar charts, histograms, and pie charts, are used to represent the data clearly and effectively, making the findings easier to interpret.

Q & A

What is the primary task in Task 1 of the analysis?

Task 1 primarily involves determining the top three most common quizzes in a dataset of restaurants based on quiz occurrences.

How are the top quizzes determined in Task 1?

The top quizzes are determined by splitting the quiz data from a comma-separated list, then using Python's `value_counts()` method to count occurrences and sorting them in descending order.

What is the result of the top three quizzes in Task 1?

The top three quizzes are North Indian (3960 occurrences), Chinese (2735 occurrences), and Fast Food (1986 occurrences).

How is the percentage of restaurants serving each quiz calculated?

The percentage is calculated by dividing the count of each quiz by the total number of restaurants and multiplying by 100.

What is the city with the highest number of restaurants according to Task 2?

New Delhi has the highest number of restaurants, with a total of 5473 restaurants.

How is the average rating for restaurants in each city calculated?

The average rating is calculated using the `groupby()` function on the 'city' column, followed by applying the `mean()` function to the ratings for each city.

Which city has the highest average rating in Task 2?

New York has the highest average rating of 4.9.

What does Task 3 focus on analyzing?

Task 3 focuses on analyzing the price range distribution for restaurants and calculating the percentage of restaurants in each price range category.

What are the price categories and their respective percentages in Task 3?

The price categories are: Category 1 (46% of restaurants), Category 2 (32%), Category 3 (14%), and Category 4 (6%).

How does online delivery affect the restaurant ratings in Task 4?

Restaurants offering online delivery have a higher average rating of 3.25, while those not offering online delivery have a lower average rating of 2.47.