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Exploratory Data Analysis on Vehicle Inventory for Auction
This project builds an overview on various EDA and Data Visualization techniques on vehicle inventory data on an online auction portal.
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Difficulty: BeginnerNo prior knowledge or experience with data required.
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Duration: Approximately 4 hours
Case Overview
- Exploratory data analysis is an extensively used method to perform various data visualization and data management techniques to better understand the data.
- With ongoing manufacturing growth, the automobile sector is inundated with offers for used vehicles. To achieve better return of investments, automobile industry are using data driven solution to control their asset allocation.
- For this purpose, the data has been taken from an online auction portal and EDA has been performed to generate actionable insights that will help the organization make better business decision.
- Learn to apply various data management and data visualization techniques to summarize data in a meaningful manner.
What’s included
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this project on a desktop
Skills You Will Learn
Associated Learning Tracks
- Analyzing the status of vehicles store at different locations is important for an automotive company.
- Production, sales, inventory, and dealer data is so huge and needs to be harnessed to sustain in the competitive automobile sector.
- To understand the competition of automobile marketplace it becomes imperative to analyze inventory data from multiple locations.
- Various EDA techniques have been performed to identify patterns and irregularities.
- Statistical techniques were also used to summarize and visualize meaningful insights from the inventory dataset.
- Company wise and region wise graph distribution were plotted for better understanding of selling trends across the world.
- This analysis can be used to improve user experience on online vehicle auction platforms for buying and selling vehicles.
- Inventory analysis is a good example of asset management which also helps in achieving better ROI.
- Actionable insights from this analysis can be used by businesses to plan better pricing strategies, inventory management system and understanding customer preferences.
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
Complete courses to earn sharable certificates and badges. These awarded items will be look great in your portfolio as you showcase your skills and project experience to employers and colleagues.
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Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.
Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.
No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.
- We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
- We also work with enterprises and universities to develop new content directed towards their industry and expertise.
