🏠 House Price Prediction API (ML.NET + ASP.NET Core)

This project demonstrates how to use ML.NET to build a machine learning model that predicts house prices based on input features like size, bedrooms, and bathrooms. The trained model is then integrated into an ASP.NET Core Web API to serve predictions.

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📂 Project Structure

HousePricePredictor/ │ ├── HousePriceModel/ # Console app (training) │ └── Program.cs # Trains the model and saves it │ ├── HousePriceModelAPI/ # ASP.NET Core Web API │ ├── Controllers/ │ │ └── HousePriceController.cs │ ├── Models/ │ │ ├── HouseData.cs │ │ └── HousePrediction.cs │ ├── HousePriceModel.zip # Trained ML model (copied from console app) │ └── Program.cs │ ├── README.md

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🚀 How It Works

1. Train a regression model using ML.NET.
2. Save the model as HousePriceModel.zip.
3. Load the model in an ASP.NET Core Web API.
4. Accept house feature input and return predicted price.

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🧠 Model Input Features

• Size (float) - Total square footage
• Bedrooms (float) - Number of bedrooms
• Bathrooms (float) - Number of bathrooms

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🧪 Step-by-Step Setup

🔹 Step 1: Train the Model (Console App)

1. Open HousePriceModel/Program.cs
2. Run the console app (Ctrl+F5 or dotnet run)
3. After training, a model file named HousePriceModel.zip will be created in: /HousePriceModel/bin/Debug/net6.0/HousePriceModel.zip

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4. Copy HousePriceModel.zip into the Web API project root: /HousePriceModelAPI/HousePriceModel.zip

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🔹 Step 2: Run the Web API

1. Open the HousePriceModelAPI project
2. Run it using Visual Studio or terminal:

dotnet run
Navigate to Swagger or use Postman to test:

bash
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POST http://localhost:{port}/api/houseprice
📦 Sample Request
🔹 POST /api/houseprice
json
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{
  "size": 1800,
  "bedrooms": 3,
  "bathrooms": 2
}
🔹 Response
json
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{
  "size": 1800,
  "bedrooms": 3,
  "bathrooms": 2,
  "predictedPrice": 354800.52
}
🧪 Quick Test Endpoint
You can also test a sample prediction at:

bash
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GET /api/houseprice/test
🖼️ Screenshots

### 🧠 Home Price Prediction  App
![Training in Console App](homepricepridiction.png)

✅ Web API Response

❗ Troubleshooting
Issue	Fix
Model file not found	Make sure HousePriceModel.zip is copied to /HousePriceModelAPI
PredictedPrice = 0	Ensure your API's HouseData class has no Price property. It should match the model input schema
Could not find label column 'Label'	Add [ColumnName("Label")] to Price in training model
Console works but API doesn't	You're likely using mismatched classes or an outdated .zip in API

🙌 Credits
Built with:

.NET 6

ML.NET

ASP.NET Core Web API

📬 Contribute or Contact
Have feedback or want to contribute? Open an issue or PR!