In this competition, Sberbank is challenging Kagglers to develop algorithms which use a broad spectrum of features to predict realty prices. Competitors will rely on a rich dataset that includes housing data and macroeconomic patterns. An accurate forecasting model will allow Sberbank to provide more certainty to their customers in an uncertain economy.
• Developed an accurate forecasting model which used a broad spectrum of features to predict reality price.
• Stacked XGBoost regression model and LightGBM regression model to improve the rank by 379 from 477th to 98th on the private leaderbroad.
• Cleaned data by dropping extreme values and imputing missing values of important attributes, and generating new significant features to improve the accuracy by 3% on the private leaderbroad using scikit-learn.
• Rank: 98th / 3274 teams (top 3%)