This project aims to develop a rental price prediction model to assist "HashMoveis" in making real estate investment decisions. The goal is to estimate the rental value of properties listed on Airbnb, enabling HashMoveis to identify properties with the highest potential for a quick return on investment.
HashMoveis, an innovative company in the real estate market, seeks to maximize its investments by identifying properties that offer high short-term returns. Our approach uses data from the temporary rental market, specifically from Airbnb, to predict rental values and identify the best investment opportunities.
Develop a predictive model to estimate rental prices of properties in a specific city. Analyze factors that significantly influence rental prices, such as location, property size, and amenities. Provide strategic insights to HashMoveis on which properties represent the best investments.
Data Collection: Use available Airbnb data sets to collect information about properties and their rental prices. Exploratory Data Analysis (EDA): Explore and visualize data to understand patterns and trends. Feature Engineering: Create and select relevant features that impact rental prices. Predictive Modeling: Build and train machine learning models to predict rental prices. Evaluation and Optimization: Assess the model's performance and optimize it for better accuracy.
Python for data analysis and modeling. Libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib. Additional tools for data visualization and machine learning as needed.