To enhance our understanding of the used cars dataset and derive actionable insights, we employed several statistical and data visualization techniques. Here’s an overview of the methodologies applied:

Descriptive Statistics
Summary Metrics: Utilized measures such as mean, median, standard deviation, and interquartile ranges to gain a foundational understanding of distributions and variability within features like year, kilometers driven, and price.

Data Visualization
Histograms and Boxplots: Used to examine the distribution and identify outliers in continuous variables such as kilometers driven, engine size, and price.
Bar Charts: Employed to visually compare the frequency and percentages across categorical variables such as fuel type, transmission, and owner type.

Data Transformation
Conversion to Categories: Changed data types of certain fields (Fuel_Type, Transmission, Owner_Type) to categories to optimize memory usage and facilitate categorical analysis.

Bivariate Analysis
Correlation Matrices and Heatmaps: Helped identify and visualize relationships between continuous variables, highlighting how different variables like price, engine size, and year interact with each other.
Pair Plots: Used to explore potential correlations and distributions across multiple dimensions segmented by fuel type.

Advanced Grouping and Segmentation
Customer Profiles: Developed detailed profiles for different car segments by analyzing grouped data, helping tailor marketing and product development strategies.
Usage Patterns: Investigated how different demographic groups (location, fuel type) use the cars differently.

By integrating these techniques, we provided a comprehensive analysis that not only described the current used car market but also offered prescriptive insights to drive business strategies and market growth.