Real Estate Price Prediction
Regression Model | EDA | ML Tuning
Real estate prices often vary due to a mix of unpredictable factors — location, square footage, amenities, etc. I wanted to build a model that could make sense of this complexity and deliver a reliable price estimate.
Approach:I began with exploratory data analysis on 7,000+ records to uncover hidden patterns and remove outliers, and created visualizations such as scatter plots and histograms to gain insights into the data distribution. From there, I tested multiple machine learning models using K-Fold Cross Validation to ensure performance wasn’t biased by a single split.
Tools & Technologies:
Python, Pandas, Matplotlib, Scikit-learn, K-Fold Cross Validation, GridSearchCV, Linear Regression
Outcome:
The final model, a tuned linear regression, achieved 81% accuracy, and helped me understand how even simple algorithms, when applied thoughtfully, can yield powerful results.
The final model, a tuned linear regression, achieved 81% accuracy, and helped me understand how even simple algorithms, when applied thoughtfully, can yield powerful results.