This project focuses on analyzing customer reviews for the Maruti Brezza, a prominent model by Maruti Suzuki. The objective is to gather insights from these reviews by scraping data from leading car review websites, cleaning it using Microsoft Excel, and conducting analysis using Python's pandas library. Additionally, an interactive dashboard is created in Power BI to visualize the analysis results. By understanding customer sentiments and identifying common complaints, the project aims to provide actionable recommendations to enhance the Maruti Brezza's competitiveness in the market.
The primary goal of this project is to extract valuable insights from customer reviews of the Maruti Brezza. This involves web scraping data from top car review sites, including aspects like performance, comfort, features, and overall satisfaction. Through data cleaning in Microsoft Excel and analysis using Python's pandas library, the project aims to identify prevalent sentiments, key aspects, and common complaints regarding the Maruti Brezza. Furthermore, an interactive dashboard is created in Power BI to visualize the analysis results and provide stakeholders with intuitive access to the insights. The ultimate objective is to offer actionable recommendations to Maruti Suzuki based on these insights, facilitating potential improvements that could lead to enhanced customer satisfaction and increased sales.
-
Sentiment Analysis: Understanding overall sentiment towards the Maruti Brezza based on customer reviews.
-
Aspect Analysis: Identifying key aspects of the car mentioned in reviews, such as performance, comfort, features, etc.
-
Common Complaints: Determining the most frequently cited complaints and areas for improvement.
-
Recommendations: Providing specific recommendations to Maruti Suzuki to address common complaints and enhance the Maruti Brezza's appeal in the market.
-
Dashboard
This project is licensed under the MIT License - see the LICENSE.md file for details.