https://github.com/ysayaovong/car-sales

An analysis of car sales data to uncover market trends and insights through data cleaning, analysis, and visualization.
https://github.com/ysayaovong/car-sales

automotive business-analysis data-analysis data-cleaning data-visualization market-trends matplotlib pandas python sales-data scikit-learn seaborn

Last synced: about 1 month ago
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An analysis of car sales data to uncover market trends and insights through data cleaning, analysis, and visualization.

• Host: GitHub
• URL: https://github.com/ysayaovong/car-sales
• Owner: YSayaovong
• Created: 2024-11-09T02:56:37.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-09T02:59:38.000Z (about 2 months ago)
• Last Synced: 2024-11-23T07:07:29.824Z (about 1 month ago)
• Topics: automotive, business-analysis, data-analysis, data-cleaning, data-visualization, market-trends, matplotlib, pandas, python, sales-data, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 130 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Car Sales Analysis Project

This repository contains a data engineering project analyzing car sales data. The project focuses on data cleaning, transformation, and exploratory data analysis (EDA) to uncover insights into car sales trends, pricing, and vehicle characteristics.

## Project Overview

The Car Sales Analysis project aims to:

- Clean and transform raw car sales data.

- Explore car sales trends by make, color, mileage, and price.

- Create visualizations to enhance understanding of the data.

- Export the cleaned and transformed dataset for further use.

## Dataset

The dataset contains the following columns:

- `Make`: Car brand or manufacturer.

- `Colour`: Car color.

- `Doors`: Number of doors.

- `Price`: Price of the car (in dollars).

The raw data required cleaning and transformation to prepare it for analysis.

## Project Structure

- **Data Import**: Load the car sales dataset for analysis.

- **Data Cleaning**: Remove unwanted characters in the `Price` column, convert data types, and handle any missing values.

- **Exploratory Data Analysis (EDA)**: Analyze car makes, color distribution, price range, and mileage impact on price.

- **Data Export**: Save the cleaned and transformed data for future use.