The project on pre-owned car analysis provides comprehensive insights into the secondary automotive market. By analyzing factors such as car age, selling prices, and present values, it enables users to identify trends, pricing strategies, and market opportunities within the used car industry.
Description: This Power BI project involves the analysis of used car sales data, with each row representing information about a specific car listing. The dataset includes details such as the car's name, manufacturing year, selling price, present price, kilometers driven, fuel type, seller type, transmission type, and ownership history. The objective is to create a comprehensive Power BI dashboard to visualize and gain insights into the factors influencing used car prices.
Objective: The primary goal of this Power BI project is to create a user-friendly dashboard that provides valuable insights into the used car market. The analysis aims to understand the factors affecting the selling price of cars, explore trends over the years, and compare different car models. Additionally, the dashboard allow users to filter data based on various attributes such as fuel type, seller type, and transmission.
The steps include: Data Cleaning: Addressing any missing values, handle outliers, and ensure data quality.
Data Exploration: Examining the distribution of variables, identify patterns, and explore summary statistics.
Create Calculated Columns: calculated columns are created to derive additional insights, such as the age of the car.
Visualizations: Building visualizations to represent key aspects of the dataset, such as: Scatter plots to explore the relationship between selling price and other variables. Bar charts to compare the distribution of car models, fuel types, and seller types. Line charts to analyze trends in selling prices over the years.
Dashboard Layout: Designing a cohesive dashboard layout that presents key findings in an organized manner. Include slicers and filters for interactivity.
Parameterization: parameters to allow users to dynamically select and compare different aspects of the dataset.
Correlation Analysis: Exploring correlations between variables to identify factors influencing selling prices.
Top Insights: Highlighting top insights, such as the most common car models, the impact of fuel type on prices, and trends in the used car market.