Predictive Real Estate Pricing
R² score > 0.85
Predictive Real Estate was built from a vision: property value shouldn't be a mystery. Developed during my Prodigy Info Tech journey, I created a model where features speak prices, where data reveals patterns, and where R² > 0.85 isn't just accuracy - it's confidence in every prediction.
1 / 2R² score > 0.85
Accurate price predictions
SnapshotAt a glance
ML Engineer (Prodigy Info Tech)
1 month
R² score > 0.85
Accurate price predictions
Technologies Used
5 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Provide accurate house price predictions using machine learning.
Core Process
The process of Developing it.
Analyzed real estate datasets, performed feature engineering, trained linear regression models, and evaluated performance.
Build notesWhat I built
03Developed a predictive real estate pricing model using linear regression with Scikit-learn, achieving R² score greater than 0.85 by performing comprehensive feature engineering, analyzing property features, and identifying key factors that influence house prices, providing confidence in every prediction.
Built a data analysis pipeline using Pandas and NumPy that processes real estate datasets, performs feature correlation analysis, and handles outliers effectively, creating a model where features speak prices and data reveals patterns that guide property valuation decisions.
Created comprehensive data visualizations using Matplotlib that illustrate price trends, feature correlations, and model performance, enabling buyers and sellers to understand property value through clear, data-driven insights that make real estate pricing transparent and accessible.
Helping buyers and sellers understand property value through data science.
Features
- House price prediction
- Feature correlation analysis
- Linear regression model
- Data visualization
Challenges
- Feature selection
- Model accuracy
- Handling outliers
