INTRODUCTION: House price prediction is a project that aims to estimate the value of residential properties using data analysis and machine learning techniques. By analyzing factors such as location, size, and features of houses, we can develop models that forecast their prices accurately. These models can help buyers make informed decisions and sellers set competitive prices. Throughout this project, we will explore the process of building a reliable house price prediction model using historical housing data. System architecture:

Conclusion:

In this house price prediction project, we successfully developed a model that can estimate the prices of residential properties. By analyzing factors such as location, size, and features of houses, our model can provide reliable predictions.

Through data analysis and machine learning techniques, we trained our model to accurately forecast house prices. This can be beneficial for both buyers and sellers in the real estate market. Buyers can make informed decisions about property purchases, while sellers can set competitive prices and optimize their selling strategies.

While our model has shown promising results, it's important to note that the accuracy of predictions may vary based on the quality and availability of data. Additionally, market conditions and other external factors can influence actual property prices.

Overall, this project demonstrates the potential of using machine learning for house price prediction and highlights the importance of considering multiple factors when estimating property values.