This project provides an in-depth analysis of Zomato's restaurant dataset to uncover meaningful insights, trends, and business recommendations.
It covers the complete data analysis workflow — from understanding the problem to visualizing insights with meaningful plots.
In this project, we:
- Introduce the Zomato dataset and its business relevance
- Clean and preprocess the raw data
- Perform Exploratory Data Analysis (EDA)
- Visualize insights using various graphs
- Answer key business questions using data
The project is perfect for beginners as well as learners looking to strengthen their data analysis & visualization skills.
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook / Google Colab
The dataset contains restaurant-related information such as:
- Restaurant Name
- Location
- Cuisine
- Ratings
- Price Range
- Votes
- Online Delivery options
- And more…
(You can add dataset source link if needed.)
The project answers multiple business questions using visualizations such as:
- Count Plot
- Line Graph
- Histogram
- Bar Graph
- Box Plot
- Heatmap
Each visualization helps us understand:
- Rating distribution
- Location trends
- Price range patterns
- Cuisine popularity
- Correlation between features
- 00:00 – 02:27: Introduction
- 02:28 – 08:12: Understanding the Business Problems
- 08:13 – 15:18: Uploading the Dataset & Importing Libraries
- 15:19 – 24:45: Data Cleaning & Preprocessing
- 24:46 – 25:48: Q1 — Count Plot
- 25:49 – 31:28: Q2 — Line Graph
- 31:29 – 35:29: Q3 — Histogram Plot
- 35:30 – 40:08: Q4 — Bar Graph
- 40:09 – 42:53: Q5 — Box Plot
- 45:54 – 48:40: Q6 — Heatmap
- 48:41 – 50:07: Free Courses Mentioned
The notebook includes:
- Distribution plots
- Trend lines
- Category-based comparisons
- Correlation heatmap
- Outlier detection using boxplot
These help uncover hidden patterns and support meaningful insights.
- Clone the repository:
git clone <your-repo-link>