5 Automotive project ideas for resume: Beginner to Advanced

In a world where wheels meet data, the automotive sector is steering towards a transformative journey powered by analytics. In this blog we’ll explore few projects that uncovers electric vehicle analysis, fuel efficiency mysteries, predict car sales trajectories, ensure fleets stay road-ready with predictive maintenance, and even dive into the intricate dynamics of ride-sharing pricing strategies.

So if you are someone who is familiar with the auto sector and wants to work in this domain then we have provided a list of 5 projects that will create a solid impact in your automotive analytics resume. So let’s check them out:

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1. Electric Vehicle analysis

Electric vehicles have been a hot topic for quite some time now. And if you’re a petrol head then you need to know about this EV revolution. For starter, an electric vehicle (EV) employs one or more electric motors for its propulsion. It can draw power from an external source through a collector system or operate independently using a battery, which may be charged via various methods such as solar panels or by converting fuel to electricity using fuel cells or a generator. Electric vehicles encompass a wide range of modes of transportation, including road and rail vehicles, surface and underwater vessels, electric aircraft, and electric spacecraft.

So in this project you’ll perform the answer questions like Which car has the fastest 0-100 acceleration? Or Which has the highest efficiency?, Does a difference in power train effect the range, top speed, efficiency?, Which manufacturer has the most number of vehicles?, How does price relate to rapid charging?

Level: Beginner 

What you’ll learn:

• Statistical analysis
• Exploratory data analysis
• Regression
• Data visualization
• Data wrangling

Tools you’ll use: 

• Pandas
• Numpy
• Matplotlib
• Seaborn

Dataset: Here

Source code: Here

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2. Fuel Efficiency Analysis

Utilizing the renowned Auto MPG Dataset, this notebook constructs a predictive model for estimating the fuel efficiency of automobiles from the late 1970s and early 1980s. The model is trained on a comprehensive set of automobile descriptions from that era, encompassing key attributes such as cylinders, displacement, horsepower, and weight.

Level: Beginner

What you’ll learn:

• Analyze fuel efficiency data of different car models.
• Identify factors affecting fuel efficiency.
• Create visualizations to compare fuel efficiency across models.
• Deep learning

Tools you’ll use:

• Tensorflow
• Matplotlib-seaborn
• Pandas
• Numpy

Dataset: AutoMPG

Source code: Here

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3. Car sales Prediction

The current Indian market exhibits a significant surge in the demand for used cars. With a slowdown in new car sales in recent times, the pre-owned car sector has not only sustained but has also surpassed the new car market in size. Cars4U, a burgeoning tech start-up, is strategically positioned to explore opportunities within this expanding market. 

In this project you’ll develop a pricing model capable of accurately forecasting the prices of used cars, enabling the business to formulate profitable strategies through dynamic pricing. For instance, with knowledge of the market price, the business can avoid selling any vehicle below this benchmark, ensuring optimal pricing strategies.

Level: Intermediate

What you’ll learn:

• Analyze historical sales data for various car models.
• Build a forecasting model to predict future market demand.
• Consider external factors like economic indicators.

Tools you’ll use:

• Pandas
• Numpy
• Scikit-learn
• Scipy
• Date time

Dataset Here: Dataset

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4. Predictive Maintenance for Fleets

In the automotive industry, predictive maintenance holds crucial significance as it helps prevent unexpected mechanical failures and reactive maintenance disruptions. Anticipating vehicle failures and strategically scheduling maintenance and repairs not only minimizes downtime but also enhances safety and increases overall productivity. In this project you’ll use the AWS predictive maintenance solution for automotive fleets that uses deep learning techniques to common areas that drive vehicle failures, unplanned downtime and repair costs.

Level: Intermediate

What you’ll learn

• Utilize sensor data from vehicles to predict maintenance needs.
• Implement a predictive maintenance model for a fleet of vehicles.
• Evaluate the model's performance over time.

Tools you’ll use

• Jupyter notebook
• Python
• AWS cloud
• Amazon S3
• Amazon SageMaker Notebook
• Amazon SageMaker Endpoint

You can start this project with the help of this Github repository

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5. Dynamic Pricing for Ride-Sharing Services

This project compares the transportation services of Uber and Lyft using a dataset comprising 693,071 data points and 57 features in Boston City, Massachusetts. Both Uber and Lyft operate in the U.S. and Canada, with specific service categories and driver requirements. The project aims to analyze factors such as price, source and destination, time, weather, and surge multiplier to provide insights into the comparative aspects of these taxi services. Additionally, a predictive model will be developed to estimate prices based on various factors like time, weather, and surge multiplier for a given source and destination.

Level: Advanced

What you’ll learn:

• Linear regression
• Geospatial analysis
• Analyze real-time data from ride-sharing services.
• Develop a dynamic pricing algorithm based on demand and supply.
• Consider factors like weather, events, and traffic.

Tools you’ll use:

• Pandas 
• Numpy
• Geopandas
• Folium

Dataset link: Here

Get a deeper understanding of this project on this Medium blog
Source code: Kaggle

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As the automotive sector increasingly relies on data analytics to drive efficiency and strategic decision-making, mastering these projects will not only equip you with valuable skills but also position you as a sought-after candidate in the job market. Whether you are a beginner seeking foundational knowledge or an intermediate/advanced user aiming for more complex challenges, these projects cater to a spectrum of skill levels.