AirBnB Price Prediction Model
This data science project analyzes Airbnb Seattle data to identify key factors affecting rental prices including location, bedrooms, bathrooms, and amenities. After comprehensive data cleaning and exploratory data analysis, it implements a random forest machine learning model to predict property prices with high accuracy.
Airbnb hosts struggle to price their properties competitively without understanding the key factors that influence rental prices in their market.
Developed a machine learning model that analyzes historical Airbnb data to identify price-influencing factors and predict optimal pricing based on property characteristics and location.
Built an accurate price prediction model using random forest algorithm that helps hosts understand pricing factors and optimize their rental rates based on data-driven insights.
Machine Learning
Data Analysis
Visualization
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