The dataset used in this analysis contains information about car prices and their associated features. Here's a brief overview of the dataset:

Columns: The dataset consists of several columns including:

Make: The brand or manufacturer of the car (e.g., Toyota, Honda, Ford).
Model: The specific model of the car (e.g., Camry, Civic, F-150).
Year: The manufacturing year of the car.
Mileage: The total mileage (in miles) of the car.
Condition: The condition of the car, categorized as Excellent, Good, or Fair.
Price: The price of the car.
Size: The dataset contains a certain number of rows, each representing a unique car entry, and a set of columns describing various attributes of the cars.

Source: The dataset was generated synthetically for the purpose of this analysis. It was created using a Python script that simulated car prices based on random values and predefined factors to mimic real-world variability.

Purpose: The dataset is used for exploratory data analysis (EDA) and modeling tasks. It serves as a sample dataset to demonstrate data analysis techniques, such as data cleaning, visualization, and predictive modeling, in a car price prediction context.

Data Types: The dataset consists of both numerical and categorical data types. Numerical features include Year, Mileage, and Price, while categorical features include Make, Model, and Condition.

Missing Values: There are no missing values in the dataset, ensuring that the analysis can be performed smoothly without the need for imputation or handling missing data.

Overall, this dataset provides a foundation for analyzing and understanding factors influencing car prices, exploring relationships between features, and building predictive models to estimate car prices based on given attributes.