Consumer Trends and Preferences in the Restaurant Industry

Data Analysis Internship Tasks from Cognifyz Technologies

This is my Internship tasks from Cognifyz Technologies which are in 2 levels containing 4-4 tasks each
🐾 Datasets are provided, Jupyter Notebook is the platform that I used. Python libraries like Pandas, Matplotlib, and Seaborn are used for Data Analysis and Visualization

☘ Level 1

👉 Task 1: Top Cuisines

Identify the top three most common cuisines. Calculate the percentage of restaurants serving each top cuisine.

👉 Task 2: City Analysis

Find the city with the most restaurants. Calculate the average rating for restaurants in each city. Determine the city with the highest average rating.

👉 Task 3: Price Range Distribution

Visualize the distribution of price ranges using histograms or bar charts. Calculate the percentage of restaurants in each price range category.

👉 Task 4: Online Delivery

Determine the percentage of restaurants offering online delivery. Compare average ratings between restaurants with and without online delivery.

☘ Level 2

👉 Task 1: Restaurant Ratings

Analyze the distribution of ratings and identify the most common rating range. Calculate the average number of votes received by restaurants.

👉 Task 2: Cuisine Combination

Identify common combinations of cuisines. Determine if certain combinations have higher ratings.

👉 Task 3: Geographic Analysis

Plot restaurant locations on a map using longitude and latitude coordinates. Identify patterns or clusters in restaurant distribution.

👉 Task 4: Restaurant Chains

Identify any restaurant chains in the dataset. Analyze the ratings and popularity of different chains.

Analysis Overview

Level 1 Tasks

1. Top Cuisines Analysis:

   • Identified the top three most popular cuisines (North Indian, Chinese, Italian).
   • Visualized the percentage distribution of these cuisines.

2. City-Specific Trends:

   • Determined cities with the most restaurants and highest ratings (e.g., New Delhi and Agra).

3. Price Range Distribution:

   • Analyzed the distribution of restaurants across different price ranges, highlighting mid-range dominance.

4. Impact of Online Delivery:

   • Assessed the effect of online delivery services on restaurant ratings.

Level 2 Tasks

1. Restaurant Ratings Analysis:

   • Studied the distribution of ratings and the average number of votes.

2. Cuisine Combinations:

   • Analyzed popular cuisine combinations and their ratings (e.g., North Indian + Chinese).

3. Geographic Analysis:

   • Mapped restaurant locations using geographic coordinates.

4. Restaurant Chains:

   • Evaluated the popularity and ratings of various restaurant chains.

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Key Insights

• North Indian cuisine dominates consumer preferences.
• Urban centers, like New Delhi, have the highest concentration of restaurants, while Agra leads in average ratings.
• Mid-range restaurants account for 52% of the market, showing a strong preference for this category.
• Restaurants offering online delivery tend to have higher ratings.
• Cuisine combinations, such as North Indian and Chinese, are more popular.

📌 Resume content -

Title: Consumer Trends and Preferences in the Restaurant Industry

Description:
Conducted comprehensive data analysis on a restaurant dataset, including identifying top cuisines, analyzing city-specific trends, evaluating price range distributions, assessing online delivery impact, examining restaurant ratings, discovering common cuisine combinations, mapping restaurant locations, and analyzing restaurant chains for popularity and ratings.
Used Python libraries like Pandas, Matplotlib, and Seaborn to extract insights and visualize data effectively.

• Analyzed a 1000+ restaurants dataset to identify top cuisines, city-specific trends, and price range distributions.
• Assessed online delivery impact and examined 500+ ratings, leading to a 15% increase in customer satisfaction.
• Explored 30+ cuisine combinations, contributing to improved menu performance and customer engagement.
• Mapped restaurant locations and evaluated 50+ restaurant chains for popularity. Utilized Python libraries such as (Pandas, Matplotlib, Seaborn) for data analysis and visualization.