Introduction
Buying or selling a house can be overwhelming, especially when determining a fair price. Many people face challenges deciding the right amount to negotiate, often relying on guesswork or limited market knowledge. To address this, I embarked on a project to build a house price prediction model that could offer data-driven guidance.
Problem Statement
The goal was to create a machine learning model to accurately predict house prices based on key features such as location, size, number of rooms, and other relevant factors. This would provide a data-driven tool to help buyers and sellers make more informed negotiation decisions.
Approach
Data Selection & Preparation
Exploratory Data Analysis (EDA)
Model Selection
Challenges
Data Imbalance: Addressed with stratified sampling and synthetic data.
Overfitting: Resolved using regularization and early stopping.
Lessons Learned
Data preparation and EDA are vital for successful modeling.
Experimenting with multiple algorithms ensures optimal performance.
Conclusion
The project provided valuable experience in tackling real-world problems with machine learning. This model can guide buyers and sellers to make informed decisions, and future iterations could include real-time market trends for even greater accuracy.
Link to the project repository.