This project presents an exploratory analysis of a car dataset using R. The goal was to uncover basic insights around car pricing, fuel types, and company-wise trends by cleaning, visualizing, and analyzing key attributes.

To perform structured EDA and pre-processing on a car dataset to understand:

• Which brands dominate the dataset?
• How fuel types are distributed across different cars?
• How pricing varies across brands?
• Variation in pricing with respect to car age.

• Data Cleaning

  • Split the Car_Name column into Car_Company and Car_Model for better granularity.
  • Removed unwanted characters and standardized company names.
  • Handled missing values using mice where appropriate.

• Exploratory Data Analysis

  • Bar plots to show most frequently sold cars.
  • Boxplots for fuel efficiency comparisons across brands.
  • Scatter plots to explore relationships between different variables.

• Few Key Insights

  • Cars with higher fuel efficiency will have a higher price bracket.
  • Cars with higher mileage tend to have lower sale prices, but some high-mileage cars continue to cost more.
  • Fuel efficiencies of Audi and Toyota are the highest among all manufacturers.

• ggplot2 bar charts, boxplots, and scatter plots.
• patchwork used to arrange multiple plots.
• GGally::ggpairs() for pairwise relationship exploration.
• kableExtra for clean and styled data tables in output reports.

tidyverse     # data manipulation and visualization
lubridate     # handling date/time if needed
knitr         # rendering tables and reports
GGally        # pair plots and correlation visuals
mice          # missing value imputation
patchwork     # combining multiple ggplots
readr         # data import
kableExtra    # enhanced table output