This project focuses on analyzing a dataset of restaurants, including various aspects such as online orders, ratings, and votes. The analysis involves visualizations and statistical exploration using Python libraries like pandas, matplotlib, and seaborn.
This project aims to provide insights into the restaurant industry through data analysis. The dataset includes information on customer reviews, online orders, ratings, and other key metrics that influence restaurant performance. The analysis explores trends, patterns, and correlations within the data.
To run the project locally, you'll need to have the following installed:
- Python 3.x
- Jupyter Notebook
- Required Python libraries (can be installed using
requirements.txt)
- Clone this repository:
git clone https://github.com/yourusername/restaurant-dataset-analysis.git
- Navigate to the project directory:
cd restaurant-dataset-analysis - Install the required dependencies:
pip install -r requirements.txt
- Start Jupyter Notebook:
jupyter notebook
- Open and run the
Restaurants_Analysis.ipynbnotebook.
The dataset used for this analysis contains restaurant information, including:
- Restaurant names
- Online order availability
- Ratings
- Votes
Make sure the dataset (restaurants.csv) is located in the correct path when running the notebook.
The analysis is divided into several key sections:
- Data Cleaning: Handling missing values, correcting data types, and filtering relevant information.
- Exploratory Data Analysis (EDA): Visualizations and summary statistics to uncover trends and insights.
- Statistical Analysis: Investigating relationships and correlations between different variables.
- Conclusion: Summarizing the findings and providing actionable insights.
Some of the key findings from the analysis include:
- The distribution of online orders across restaurants.
- Correlation between ratings and votes.
- Trends in customer preferences.