House Price Predictor

🎯 Predicting House Prices: A Business Perspective

Machine learning is transforming industries by enabling smarter, faster, and more accurate decisions. One of the most impactful use cases is regression — the prediction of a continuous value, such as price, demand, or risk. In this whitepaper, we demonstrate how ML.NET can be used to build a regression model that predicts house prices based on features like size, number of rooms, and location score. This real-world example showcases how accessible and valuable machine learning can be for businesses.

Whether you're in real estate, retail, manufacturing, or finance, being able to predict numeric outcomes can help you plan better, price more effectively, and allocate resources more efficiently. 

Use the form below to test the live model. Enter values such as property size, number of rooms, and location score — and see the predicted house price in real time.

📈 Understanding Model Metrics

Once trained, a regression model is evaluated using several key metrics. Each one helps us understand a different aspect of model performance:

R-Squared (R²)

This measures how well the model explains the variability in the data. A score of 1.0 is perfect; 0.9+ is excellent in many domains.

Mean Absolute Error (MAE)

This is the average absolute difference between predicted and actual prices. A lower MAE indicates more accurate predictions. For example, an MAE of $10,000 means the model's predictions are typically within $10,000 of actual prices.

Root Mean Squared Error (RMSE)

This is the square root of the MSE, bringing the error back to the original unit (e.g. dollars). It's intuitive and often used in business contexts.

These metrics are essential in evaluating whether a model is good enough for production use, or if it needs more tuning.

⚙️ Training

During training, the model attempts to learn the relationship between the input features and the target price. Internally, this involves assigning initial weights to each feature and calculating a predicted price using a forward pass. The error (difference between the predicted and actual price) is then used to adjust those weights using backward propagation, where the model learns which features to emphasize more or less. This process is repeated across the entire dataset over multiple iterations until the model reaches a state where prediction errors are minimized.

🔄 Feedback Loop and Fine-Tuning

A model is only as good as the data and assumptions behind it. Once deployed, continuous feedback (such as comparing predicted vs actual outcomes) allows you to fine-tune the model over time. You might adjust:

• The data (cleaning, enriching, or filtering it)

• The algorithm (e.g., switching from SDCA to FastTree)

• The hyperparameters (e.g., learning rate, number of trees)

Fine-tuning helps the model adapt to new patterns and remain relevant as the market or environment evolves.

🧹 The Importance of Data Quality

Machine learning models learn from the patterns in your data — so if the data is poor, the predictions will be too. Key principles:

• Use clean, complete data

• Ensure consistent formats (e.g., square feet should always be in the same unit)

• Avoid biased samples (e.g., only luxury homes)

The better the data, the more accurate and reliable your model will be. 

💼 Why Business Should Care

Regression models reduce guesswork, optimize pricing, and improve planning. An accurate regression model can:

• Help real estate agents price properties competitively

• Assist banks in risk-based lending decisions

• Improve demand forecasting in retail or manufacturing

This leads to better customer satisfaction, smarter planning, and significant cost savings. 

📣 Let’s Build Together

If you’re ready to unlock the value of machine learning in your business — whether it’s pricing, forecasting, or risk modelling — get in touch. With expertise in ML.NET and real-world deployment, we’ll help you deliver impactful, intelligent solutions.