Conducted comprehensive exploratory data analysis on Zomato restaurant dataset to uncover insights about restaurant trends, customer preferences, and market patterns. The project involved extensive data cleaning, visualization, and statistical analysis to derive actionable business insights.
Handled missing values, outliers, and inconsistent data formats across the dataset
Comprehensive analysis of numerical and categorical variables with statistical summaries
Created insightful charts and graphs to visualize restaurant trends and patterns
• Restaurant Distribution: Analyzed geographical spread of restaurants across cities
• Cuisine Preferences: Identified most popular cuisine types and regional preferences
• Price Analysis: Examined cost patterns and their correlation with ratings
• Rating Patterns: Analyzed customer rating distributions and trends
• Service Types: Compared online delivery vs dine-in preferences
• Advanced data manipulation and cleaning techniques using Pandas
• Statistical analysis and hypothesis testing methods
• Data visualization best practices with Matplotlib and Seaborn
• Business insight generation from raw data analysis
• Documentation and presentation of analytical findings
• Provided actionable insights for restaurant business strategy
• Demonstrated proficiency in end-to-end data analysis workflow
• Built foundation for advanced machine learning projects
• Enhanced understanding of real-world data challenges and solutions