ZOMATO DATA ANALYSIS USING PYTHON AND POWER BI DASHBOARD

This research demonstrates how data analytics techniques can be applied to analyse restaurant datasets and extract meaningful business insights. By using Python for data preprocessing and analysis, and Microsoft Power BI for visualization, the study successfully transforms raw restaurant data into clear and interpretable insights. The combination of these tools provides an effective approach for handling large datasets and presenting analytical results in an understandable format. The data preprocessing and exploratory analysis stages helped identify important patterns related to cuisine popularity, restaurant pricing trends, and customer rating distribution. The analysis revealed that certain cuisines dominate the market, while restaurants offering moderate pricing often receive higher customer satisfaction ratings. These insights highlight the importance of understanding customer preferences and market demand in the restaurant industry. The interactive dashboards developed using Power BI provide a user-friendly platform for visualizing and exploring restaurant performance metrics. Through graphical representations and interactive filters, stakeholders can easily analyse city-wise restaurant distribution, average ratings, and popular cuisine categories. Such visual insights can assist restaurant owners, analysts, and decision makers in developing better marketing strategies, pricing policies, and service improvements. Overall, the proposed analytical framework demonstrates the potential of combining data analytics and business intelligence tools to support data-driven decision making in the restaurant industry. By analysing large-scale datasets from platforms such as Zomato, businesses can gain a deeper understanding of customer behaviour and market dynamics.

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