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Exploratory Data Analysis
Master pandas and seaborn to uncover trends, seasonal patterns, and feature correlations in real automotive data.
Explore
4.6 from 112 ratings
Machine Learning for Automotive
ProoV · Intermediate
About this project
Build ML models to predict resale values for VW Golf and Audi A4 using real UK market data. Compare Linear Regression with Random Forest and deliver business recommendations in this simulated case study.
Ideal for entry-level careers in
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inMachine Learning for Automotive
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Associated with ProoV
A graded ProoV project, completed step by step and marked against a published rubric.
❖machine-learning, regression and more skills
What you'll do
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Introduction & Project Brief
Get introduced to the ProoV platform, the Volkswagen Group case study, and the core problem: predicting used car resale values using real UK market data.
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Section 1: Data Exploration
Load VW and Audi datasets. Merge into a single DataFrame. Perform Exploratory Data Analysis (EDA) including correlation heatmaps, boxplots by brand, and scatter plots comparing price to mileage and year.
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Section 2: Feature Engineering
Engineer derived features like car age and mileage per year to capture usage intensity. Handle outliers and one-hot encode categorical variables like transmission and fuel type.
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Section 3: Model Building
Establish a Linear Regression baseline, then train a RandomForestRegressor. Compare R², MAE, and RMSE. Plot feature importances to discover the strongest price predictors.
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Section 4: Business Impact
Interact with the in-app Price Predictor to validate model intuitions. Write an executive summary for the CPO team outlining the winning model, top features, and specific price insights.
What you'll learn
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Feature Engineering
Transform raw data into powerful predictive signals. Handle missing values, outliers, and categorical encoding.
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Predictive Modeling
Train and evaluate Scikit-Learn models like Random Forests to predict precise vehicle resale values.
What students say
This project gave me hands-on experience with the complete machine learning workflow, from data cleaning and exploratory data analysis to feature engineering, model training, evaluation, and business communication. I learned how to transform raw automotive data into meaningful f…
This project helped me understand how machine learning can be applied to real business problems. I learned how to analyze data, build prediction models, evaluate their performance, and convert technical results into business recommendations. It was a great hands-on experience th…
Made me understand the working for Decision trees in depth. Now i have an idea how to train models and find importance values of features
Hands-on experience with end-to-end data cleaning, feature engineering, and deploying regression models (Linear Regression vs. Random Forest) on real automotive datasets.
"The notebook navigation was a bit tricky to follow at first — clearer section markers or a jump-to-cell feature would help."
I learned how machine learning can solve real-world business problems and gained practical experience in data cleaning, model building, and evaluating results.