PROBLEM STATEMENT
The challenge of accurate house price prediction is pivotal in real estate, impacting both buyers and sellers. Fluctuating market dynamics, economic variables, and neighborhood-specific factors contribute to the complexity. Existing models often struggle to incorporate diverse data sources and adapt to changing trends, leading to unreliable predictions. Inaccurate estimations hinder informed decision-making, causing financial disparities for both homeowners and potential buyers. Addressing this issue requires the development of robust predictive models that leverage advanced algorithms, encompassing a broad spectrum of variables. Enhancing the accuracy of house price predictions is crucial for promoting transparency, reducing financial risks, and fostering a more efficient real estate market.
OBJECTIVE
- Implement a machine learning-based house price prediction system to provide accurate and dynamic real estate valuations.
- Utilizing advanced algorithms, the aim is to analyze diverse data inputs, including market trends, economic indicators, and property-specific features.
- The model will adapt to changing market dynamics, enhance predictive accuracy, and consider spatial and temporal variations.
- Incorporating a comprehensive dataset, the system seeks to outperform traditional valuation methods, providing stakeholders with reliable and transparent insights for informed decision-making.
- The ultimate goal is to contribute to a more efficient and equitable real estate market by leveraging machine learning to predict house prices with precision and responsiveness to market fluctuations.
DATASET USED
The dataset of Banglore House Prices have been taken to train the model from Kaggle.
PYTHON LIBRARIES USED
- Numpy
- Pandas
- Matplotlib
- Scikit-Learn