Frame the question
Predict a numeric sale price from property features. Start with floor area, then consider location, age and property type.
01 / THE IDEA
Start small. Learn the whole thinking process.
Imagine a student looking at property listings and asking, “How much does floor area explain the price?” Linear regression gives them a transparent first model: a line fitted to examples. The goal is to understand the inputs, the target and the errors—not to claim an accurate market valuation from a tiny dataset.
Real-world use case · Proposed extension
A local housing research team could use a properly validated baseline to flag listings that differ substantially from comparable sales for manual review. It would need local, licensed transaction data and additional features before anyone could rely on it.
02 / THE BUILD
From a first line of code
to a responsible prototype.
Build a baseline
Use Python and scikit-learn to fit a linear regression model. Plot the data and inspect how predictions change with area.
Test honestly
Replace the toy data with permitted real data. Separate training and test sets; compare MAE and RMSE against a median-price baseline.
Explain the result
Show the inputs, estimated range, dataset coverage and limitations. Keep a human in the loop for any real decision.
03 / SOURCE SNAPSHOT
What the original starter actually does.
The supplied HTML project includes a short Python example using NumPy, Matplotlib and scikit-learn. It fits a regression line to ten synthetic floor-area and price pairs, plots the fit, and makes one example prediction. The values are in US dollars, not Indian rupees.
SOURCE EXCERPTPython
model = LinearRegression()
model.fit(X, y)
y_pred = model.predict(X)
print("Mean squared error:", mean_squared_error(y, y_pred))
new_area = np.array([[1600]])
predicted_price = model.predict(new_area)[0]Important limitation
The starter measures error on the same ten rows used to train the model. That is not an unbiased test of future performance. The source page also lists classification metrics for this regression task; use MAE/RMSE and a held-out test set instead. The chart above is an illustration, not measured model output.
04 / BEYOND THE CLASSROOM
A startup idea, not a startup claim.
PROPOSED BUSINESS CONCEPT
Comparable-listing review for local property teams
A lightweight dashboard for small real-estate research teams: ingest licensed local sales data, surface comparable properties, show a model-assisted price range and explain why a listing deserves a closer look. A human analyst remains responsible for the decision.
WHO IT COULD SERVELocal housing analysts & small agencies
FIRST MVPOne city, reviewed data, comparable-sales report
KEY RISKPatchy data and neighbourhood bias
This concept is a potential application pathway. The supplied starter does not include a dashboard, real transaction data, reliable price ranges or a deployed service.
05 / WHAT YOU TAKE AWAY
Skills worth showing your work for.
- Explain the difference between a fitted line and a reliable price estimate.
- Prepare tabular data and identify missing values or data leakage.
- Evaluate a regression model on held-out examples, not its training rows.
- Communicate uncertainty and where the model should not be used.
One project. A clear starting point.
All machine learning projects Explore the wider AI projects page while the rest of the library is being prepared.