Restaurant Data Analysis

• Conducted comprehensive data analysis on restaurant dataset using Python, pandas, numpy, and nltk.
• Developed interactive analyses in Jupyter Notebooks, contributing actionable insights for business decisions.
• Applied scikit-learn for machine learning clustering, identifying restaurant clusters.
• Utilized Plotly for dynamic data visualizations, including interactive maps.
• Explored correlations between price range, online delivery, and customer ratings with seaborn and matplotlib.
• Identified popular restaurant chains, providing valuable insights for strategic decision-making.