Welcome to the AirBnB Price Prediction Machine Learning project! This project leverages various machine learning algorithms to predict the prices of AirBnB listings based on a comprehensive dataset of features such as location, property type, number of bedrooms, and amenities.
Project Overview :
The goal of this project is to build and evaluate machine learning models that can accurately estimate AirBnB listing prices, providing valuable insights for hosts to price their properties competitively and for guests to find listings that fit their budget.
Table of Contents :
~ Features
~ Key Highlights
~ Tools and Technologies
~ Results
~ License
~ References
Features :
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Data Preprocessing: Cleaning and preparing data by handling missing values, encoding categorical variables, and scaling features.
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Exploratory Data Analysis (EDA): Visualizing data to uncover patterns and relationships between features and the target variable.
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Model Training: Implementing multiple regression models including Linear Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor.
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Model Evaluation: Evaluating models using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and R2 Score.
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Visualization: Plotting actual vs. predicted values to assess model accuracy.
Key Highlights :
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Achieved high predictive accuracy, with XGBoost demonstrating an R2 Score of 0.9945 and Decision Tree Regressor have an R2 Score of 0.9946.
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Conducted in-depth EDA to identify key factors affecting AirBnB pricing.
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Performed rigorous model evaluation and hyperparameter tuning to ensure optimal performance.
Tools and Technologies :
~ Programming Language: Python
~ Libraries: pandas, numpy, matplotlib, seaborn, XGBoost
~ Development Environment: Jupyter Notebook
Results :
The final model, based on XGBoost, achieves an impressive R2 Score of 0.9945, indicating high accuracy in predicting AirBnB listing prices.
License :
This project is licensed under the MIT License. See the LICENSE file for more details.
References :
https://www.kaggle.com/code/lxy21495892/airbnb-eda-pygwalker-demo/input
https://www.kaggle.com/code/tahirelfaki/airbnb-price-prediction-model-linear-regression
Contact If you have any questions, suggestions, or feedback, feel free to contact me.
Email ID : [email protected]