Data-Driven Real Estate Forecasting Project

• Overview This project is focused on predicting real estate prices using advanced machine learning techniques on a dataset comprising over 200,000 data points. The goal was to uncover intricate relationships within the data to enhance predictive accuracy and provide valuable insights for stakeholders in the real estate sector.

• Key Contributions

  1. Model Implementation: Implemented and compared several machine learning models including RandomForest, XGBoost, and FeedForward Neural Network to predict real estate prices.

  2. Data Preprocessing: Developed comprehensive data preprocessing workflows involving web scraping techniques to gather additional data, handling missing values, detecting outliers, and engineering new features to improve model performance.

  3. Exploratory Data Analysis (EDA): Conducted thorough EDA to understand the distribution and correlations within the dataset. Insights gained from EDA guided data cleaning strategies and feature selection processes.

• Results and Impact The project successfully achieved significant insights into real estate pricing dynamics, highlighting factors such as location, property characteristics, and economic indicators that influence property prices. The models developed provided actionable predictions,
  aiding in strategic decision-making for investors and stakeholders in the real estate market.

• Next Steps Future iterations of this project could explore more sophisticated modeling techniques, incorporate additional datasets for richer insights, and deploy models in production environments for real-time forecasting and decision support.