Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/simran2911/restaurant_rating_pridiction

This github repository have restaurant rating analysis and understand what contributes to high or low ratings on the platform and how these insights can be leveraged for business decision-making.
https://github.com/simran2911/restaurant_rating_pridiction

jupyter-notebook machine-learning numpy pandas powerbi python sklearn statistics tableau

Last synced: 7 days ago
JSON representation

This github repository have restaurant rating analysis and understand what contributes to high or low ratings on the platform and how these insights can be leveraged for business decision-making.

Awesome Lists containing this project

README 

        
## Project Overview:



The objective of this project is to analyze and visualize the ratings and reviews data from Zomato to gain insights into customer preferences, restaurant performance, and factors influencing ratings. By exploring this data, we aim to understand what contributes to high or low ratings on the platform and how these insights can be leveraged for business decision-making.



## Key Components:



# Data Collection:



- Extracting restaurant data including ratings, reviews, cuisine types, locations, and other relevant metadata from Zomato's API or datasets.



# Data Cleaning and Preparation:



- Cleaning the data to handle missing values, outliers, and inconsistencies to ensure accurate analysis.

- Preparing the data for analysis by structuring it into a suitable format for visualization and statistical modeling.



# Exploratory Data Analysis (EDA):



- Conducting EDA to understand the distribution of ratings, review sentiments, and relationships between variables such as cuisine types, locations, and ratings.

- Visualizing trends and patterns using charts, graphs, and heatmaps to identify popular cuisines, highly rated restaurants, and geographic preferences.



## Sentiment Analysis:



- Using natural language processing techniques to analyze customer reviews sentimentally. This helps in understanding the sentiment associated with different rating categories.



# Predictive Modeling:



- Building predictive models to forecast restaurant ratings based on factors such as cuisine, location, price range, etc.

- Evaluating model performance and deriving actionable insights for improving restaurant ratings.



## Interactive Dashboard:



- Developing an interactive dashboard using tools like Tableau or Power BI to visualize key findings and insights.

- Providing filters for users to explore ratings by cuisine, location, price range, etc., and to compare performance metrics across restaurants.



# Benefits:



- Insight into Customer Preferences: Understanding which factors influence customer ratings and preferences.



- Competitive Analysis: Benchmarking restaurant performance against competitors in terms of ratings and reviews.



- Business Strategy Optimization: Informing marketing strategies, menu enhancements, and operational decisions based on customer feedback and preferences.



# Conclusion:

This Zomato rating analysis project aims to provide valuable insights into customer behavior and restaurant performance based on Zomato's extensive dataset. By leveraging data analytics and visualization techniques, the project enables stakeholders to make data-driven decisions that enhance customer satisfaction and drive business growth in the competitive restaurant industry.