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Car Sales project involving the analysis of Sales data using SQL and Python to uncover insights and monitor workforce metrics, with the use of a data pipeline and Tableau dashboards.

Data analysis with python for car sales. shown which car models are the top sellers and how sales are distributed. how insights are shape the company’s next big move in the market. Created bar charts and pie charts to show the sales of different car models. Key Concepts: Data visualization, Matplotlib, Plotly, Pyhton, Numpy, Ms Excel.

As part of a Machine Learning course, we were tasked with predicting used car prices using data we extracted from a used car sales website. I performed the prediction using Multiple Linear Regression, leveraging Python for the analysis. Attached are the CSV file containing the data and the Jupyter Notebook with the code.

This is my first python data analysis project. I analyzed a cars sales dataset

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Exploratory Data Analysis with Python for Car Sales

SQL + Python analysis of used car sales data with insights on pricing, condition, and regional trends

End-to-end car sales data analysis project using Python, Pandas, and KPI analysis with an interactive dashboard and business insights.

This project covers three tasks: analyzing unemployment data with Python, predicting car prices using machine learning, and forecasting sales through data analysis.

Car sales analysis in Microsoft Excel with pivot tables, charts and a final report, preceded by data preparation in Python.

Regional car sales analysis with customer & dealer data. Analyzing sales performance across regions, customer trends, and dealer efficiency. Visualized using Python libraries like Seaborn and Plotly.

This repository contains a comprehensive car sales data analysis project using MySQL and Python. It includes data cleaning, loading, and exploratory data analysis (EDA) with visualizations. The project follows a structured pipeline to connect to a MySQL database, preprocess raw car sales data, store cleaned data, and generate insightful plots.

This is an Exploratory Data Analysis (EDA) of a car sales dataset using Python, Pandas, and Matplotlib. It covers data cleaning, visualization of sales and price distributions, and insights into manufacturer performance and feature correlations. The analysis concludes with market trends and future sales predictions.

Analysis Data of Car Sales and their Price using Python libraries numpy, Pandas, matplotlib , seaborn . In this analysis the Car sale and price of different companies with different feature and with the help of matplotlib and seaborn libraries plot different views of data

A Python project using Pandas and Scikit-Learn to analyse car sales data and predict future sales with Linear Regression. Includes CSV file handling, data analysis (totals, averages), and saving predictions to a new file.

Basic Car Sales Analysis using Python & Pandas. Includes data cleaning with dropna, fillna, value_counts, lambda functions, and isin filtering. Provides simple EDA. easy beginner-friendly approach.

This project analyzes car sales data for various makes from 2014 to 2015. Initial data cleaning performed in excel, further data cleaning and analysis performed in Python. Dashboards created with Power BI. Data set was acquired from https://www.kaggle.com/datasets/syedanwarafridi/vehicle-sales-data?resource=download&select=car_prices.csv.

Car Sales Prediction Model – A machine learning project that builds and evaluates regression models to predict car prices/sales based on key features. Includes data preprocessing, exploratory analysis, model training, and visualizations. Implemented in Jupyter Notebook with Python, scikit-learn, and TensorFlow.

Comprehensive Car Sales Data Analysis project using Python, Pandas, NumPy, Matplotlib, and Seaborn. Covers data cleaning, handling nulls, removing duplicates, and performing EDA with visual insights on brand performance, pricing trends, fuel type distribution, and sales patterns across regions and years.

This project aims to predict used car prices based on specifications and condition. It starts with data analysis from previous UAE car sales using Excel and Python. The data is visualized with Python for insights. A regression ML model is then built and deployed on a website created with Streamlit.

Web-based car dealer management system built using Python and Django. It supports car inventory, sales, and customer management with full CRUD operations. The system includes search and filtering, data analysis, ML-based car price prediction, and interactive dashboards for better business insights

Dive into the digital automotive market with this Jupyter Notebook, designed to navigate and extract car listing data from Khmer24. Utilizing Python's robust libraries, this project offers a detailed look into data scraping techniques, data handling, and insightful analysis of car sales online.

These four programs demonstrate machine learning and data analysis applications using Python. They include classification (Iris species), trend analysis (unemployment & COVID impact), and prediction models (car prices and sales) using Logistic and Linear Regression, along with data cleaning, visualization, and model evaluation.

Data Science Projects using Python (Jupyter Notebook)! Includes Car Price Prediction (Regression), Email Spam Detector (NLP), Sales Prediction (Time Series), and Unemployment Analysis (EDA & ML). Built with Pandas, NumPy, Sklearn, Flask (if deployed). Explore & contribute!

Scraped paginated car sales data using Python (BeautifulSoup/Selenium), collecting multi-make details and storing them in a CSV file for analysis. Cleaned and analyzed data with Pandas in Jupyter Notebook, visualizing pricing, fuel type, and market trends using Matplotlib and Seaborn.

This repository contains a data analysis project on BMW worldwide sales records (2010–2024) using Python. The dataset is obtained from Kaggle, and the analysis is performed with Pandas, Matplotlib, and Seaborn to explore customer preferences and market demand across different dimensions such as models, regions, years, fuel types, and car colors.