PROJECT 03 / MACHINE LEARNING / DATA PIPELINESCourse project
Car Price Predictor.
From vehicle data to a price estimate.
A historical automobile dataset becomes the foundation for a Ridge regression model and a Flask application that accepts vehicle features and returns a price estimate.
MY ROLE
Sole developer
COLLABORATION
Individual machine learning project
TECHNOLOGIES
Pythonscikit-learnpandasFlaskJupyter
01 / EXPLORE THE PROJECTINTERACTIVE WALKTHROUGH
See how it connects.
CAR PRICE PREDICTOR / THE MODEL PIPELINE01 / 04
A cloud of vehicle features3D VIEW
Training examplesHeld-out examplesRidge fit
HorsepowerWeightEngine sizeFuel typeIncomplete records
RAW INPUT
Different features. Different scales.
Each dot represents a vehicle with numeric and categorical attributes.
Horsepowernumeric
Curb weightnumeric
Engine sizenumeric
Fuel typecategory
Pricetarget
205historical automobile records
From raw attributes to a feature matrix.
01Select useful featuresRemove incomplete selected records
Recorded notebook results. Plot points and residuals are illustrative.
Ridge model + preprocessing↓Flask prediction interface
car-price / interactive preview
Estimate a vehicle price.
ILLUSTRATIVE ESTIMATE$17,600
Try the controls. This simulated preview demonstrates the interface; it does not run the trained model.
Conceptual feature projections and candidate fits use illustrative points. Metrics come from saved notebook output and have not been independently reproduced here. Website controls simulate an interface, without running the trained model.
02 / MY CONTRIBUTION
Where I made an impact.
I developed the project individually for a machine learning course, from dataset exploration and preprocessing through model tuning, evaluation, and the prediction interface.
03 / THE OUTCOME
What the work became.
The saved notebook reports a test R² of 0.907 and RMSE of 2679.00. These are recorded course project results on a small historical dataset, not a current vehicle valuation benchmark.