The Monthly Car Sales Analysis project uses data analytics, machine learning, and time series forecasting to track and predict car sales trends. It explores patterns by brand, model, region, and season, identifies consumer preferences, and generates insights that support marketing, inventory, and strategic decision-making in the automobile industry

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Project examines factors affecting the change in car sales between 2019 and 2020 utilizing real world data, python, pandas, matplotlib and jupyter notebook.

Analysis of second-hand car sales data using regression, clustering, and ANN for price prediction and pattern discovery.

The Monthly Car Sales Analysis project uses data analytics, machine learning, and time series forecasting to track and predict car sales trends. It explores patterns by brand, model, region, and season, identifies consumer preferences, and generates insights that support marketing, inventory, and strategic decision-making in the automobile industry

Analysis of popular basic dataset "Titanic", "Car sales" in R.

Assignment About the data: Let’s consider a Company dataset with around 10 variables and 400 records. The attributes are as follows: Sales -- Unit sales (in thousands) at each location Competitor Price -- Price charged by competitor at each location Income -- Community income level (in thousands of dollars) Advertising -- Local advertising budget for company at each location (in thousands of dollars) Population -- Population size in region (in thousands) Price -- Price company charges for car seats at each site Shelf Location at stores -- A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site Age -- Average age of the local population Education -- Education level at each location Urban -- A factor with levels No and Yes to indicate whether the store is in an urban or rural location US -- A factor with levels No and Yes to indicate whether the store is in the US or not The company dataset looks like this: Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.

Here, The data of different cars is given with their specifications. This data is availabe as a CSV file. We are going to analyze this dataset using the Pandas DataFrame

Global Car Sales Analysis from 2005 to 2022 Dashboard Using Python

KPI for Cars sales analysis for Win and Loss - A Data Analysis using Excel and Tableau

eBay car sales - data analysis

Analysed a Cars24 dataset to assess available cars by model, year, ownership, and price. Implemented features for selecting car fuel type, mileage, and seating capacity.

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This project analyzes car sales data to track YTD, MTD, and YOY performance. It includes interactive dashboards showing sales trends, body style and color distribution, average price analysis, and key KPIs to support data-driven business decisions.

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Statistical Analysis on Sales of Cars in Uk

In this project I explored India's largest used car dealer cardekho.com and its sales for market segmentation and used predictive analytics to determine the type of car most likely to meet customer demands.

This R script analyzes the "mtcars" dataset, exploring variables like mpg, cylinder count, and horsepower. It visualizes the data and identifies the most fuel-efficient car model based on mpg, while also comparing average mpg between automatic and manual transmission cars.

An interactive Car Sales Performance Dashboard that analyzes automobile sales data to uncover trends, revenue insights, and performance across models and regions. Built as an end-to-end data analytics and business intelligence project.

The objective of this project is to design and develop a dynamic and interactive Car Sales Dashboard using Power BI. The dashboard will visualize critical KPIs related to our car sales, helping us understand our sales performance over time and make data-driven decisions.

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I Analyzed 200+ automobile records to build an interactive Excel dashboard using data cleaning, pivot tables, and visualizations to explore patterns in fuel type, engine type, and vehicle characteristics. Provided insights into key factors influencing car features and performance, supporting data-driven comparisons and decision-making.

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As the world shifts towards sustainable transportation, electric vehicles (EVs) are gaining momentum like never before. This Tableau dashboard dives into EV sales trends and market growth, offering a clear picture of how the industry is evolving.

Here we going to have a look on Car sales data analysis of Different brands sales

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