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Forecasted Airbnb 'Super host' status in Chicago with an 84% accuracy using Logistic Regression and assessed potential returns on investment employing the Herfindahl Index for strategic investment insights

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Harshraj1301/Airbnb---Chicago-Superhost-Prediction

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Airbnb Chicago Superhost Prediction and Strategic Investment Insights

This project utilizes Airbnb's datasets to address real-time challenges within the Airbnb ecosystem in Chicago through robust modeling techniques.

Table of Contents

Project Objective

Utilized Airbnb's datasets to address real-time challenges within the Airbnb ecosystem in Chicago through robust modeling techniques.

Key Highlights

Predictive Model for Superhost Status

  • Developed a Logistic Regression model to forecast Airbnb 'Superhost' status with 84% accuracy.
  • Identified key factors influencing Superhost recognition, aiding hosts in understanding and achieving Superhost status.

Strategic Investment Insights

  • Employed the Herfindahl Index to assess market concentration and competitiveness across various neighborhoods in Chicago.
  • Provided strategic recommendations for potential investors, focusing on optimizing return on investment (ROI) by identifying high-potential areas for new Airbnb listings.

Exploratory Data Analysis (EDA)

  • Analyzed trends and patterns in the raw data to differentiate between hosts and Superhosts.
  • Key findings showed that Superhosts generally have higher ratings, response averages, and booked days compared to regular hosts.

Data Preprocessing

  • Enhanced data quality by removing irrelevant features and handling missing values using Gradient Boosting for imputation.
  • Addressed multicollinearity to improve model performance and ensure robust predictions.

Investment Decision Support

  • Analyzed property prices and revenue to calculate potential returns on investment.
  • Suggested neighborhoods with high ROI potential, considering the competitive landscape and revenue generation capabilities.

Occupancy Rate Prediction

  • Forecasted occupancy rates using a Random Forest model, achieving a MAPE of 3.80% on the test data.
  • Insights revealed an 8% increase in occupancy rate for properties predicted to achieve Superhost status.

Business Insights & Recommendations

  • Promotion of Superhost Program: Continue promoting the Superhost certification to maintain high-quality standards and enhance guest satisfaction.
  • Support for New Hosts: Provide resources and incentives for new hosts to become Superhosts, particularly in areas with fewer Superhosts.
  • Neighborhood Marketing: Highlight neighborhoods with high concentrations of Superhosts in marketing campaigns.
  • Comprehensive Listings: Educate hosts on the importance of thorough and attractive property listings, emphasizing the impact of having 10-20 high-quality photos.
  • Strategic Location Focus: Leverage the reputation of high-performing neighborhoods like Lakeview and suggest competitive strategies for other areas.
  • Property Type Optimization: Recommend investing in studio apartments, as they have a higher likelihood of achieving Superhost status.

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Forecasted Airbnb 'Super host' status in Chicago with an 84% accuracy using Logistic Regression and assessed potential returns on investment employing the Herfindahl Index for strategic investment insights

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