Project Overview

Category: Data Science, Time Series Forecasting

Developed time series forecasting models to predict weekly listing patterns for Boston and Mexico datasets, enabling proactive business planning and resource allocation.

Background

Understanding future listing patterns is crucial for marketplace platforms and real estate businesses. Accurate forecasts enable better inventory management, pricing strategies, and resource allocation. This project applied statistical modeling techniques to predict weekly listing volumes across two different markets.

Objectives

• Build statistical models to forecast weekly listing patterns
• Compare model performance across different geographic markets (Boston and Mexico)
• Identify seasonal patterns and trends in listing behavior
• Provide actionable forecasts for business planning

Work

Technologies Used:

• Python: Data preprocessing, model development, and analysis
• R: Statistical modeling and SARIMA implementation
• Time Series Analysis: Trend, seasonality, and forecasting techniques