Car Price Prediction

About Dataset

Used Car Price Prediction Dataset is a comprehensive collection of automotive information extracted from the popular automotive marketplace website, https://www.cars.com. This dataset comprises 4,009 data points, each representing a unique vehicle listing, and includes nine distinct features providing valuable insights into the world of automobiles.

1. Brand & Model: Identify the brand or company name along with the specific model of each vehicle.

2. Model Year: Discover the manufacturing year of the vehicles, crucial for assessing depreciation and technology advancements.

3. Mileage: Obtain the mileage of each vehicle, a key indicator of wear and tear and potential maintenance requirements.

4. Fuel Type: Learn about the type of fuel the vehicles run on, whether it's gasoline, diesel, electric, or hybrid.

5. Engine Type: Understand the engine specifications, shedding light on performance and efficiency.

6. Transmission: Determine the transmission type, whether automatic, manual, or another variant.

7. Exterior & Interior Colors : Explore the aesthetic aspects of the vehicles, including exterior and interior color options.

8. Accident History: Discover whether a vehicle has a prior history of accidents or damage, crucial for informed decision-making.

9. Clean Title: Evaluate the availability of a clean title, which can impact the vehicle's resale value and legal status.

10. Price: Access the listed prices for each vehicle, aiding in price comparison and budgeting.

This dataset is a valuable resource for automotive enthusiasts, buyers, and researchers interested in analyzing trends, making informed purchasing decisions or conducting studies related to the automotive industry and consumer preferences. Whether you are a data analyst, car buyer, or researcher, this dataset offers a wealth of information to explore and analyze.

Explority Data Analysis

In this project, we utilized Matplotlib and Plotly Express (PyExpress) libraries to perform advanced data visualization and feature analysis. Visualizations play a critical role in understanding complex datasets, identifying patterns, and improving the performance of machine learning models.

By integrating visualization into the data analysis pipeline, we gain a better understanding of how features interact and impact the outcome. This reduces the trial-and-error aspect of feature selection and supports data-driven decisions. Additionally, interactive visualizations make it easier to communicate findings to team members or stakeholders.By combining static and interactive visualizations, this project not only enhances the analysis process but also creates a foundation for future reproducible and scalable analytics workflows.