🏡 Airbnb Price Prediction Model

This project focuses on building a machine learning model to predict Airbnb listing prices using data cleaning, feature engineering, exploratory data analysis (EDA), and model evaluation. The final model achieves 99% accuracy on unseen data.

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📌 Project Workflow

1. Load and Observe Dataset

• The Airbnb dataset was loaded and examined for structure, data types, missing values, and basic statistics.

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2. Clean Dataset 🧼

• Feature Descriptions and Relevance: Each feature was defined and assessed for relevance to price prediction.
• Dropped Columns: Redundant or irrelevant features such as identifiers (id, host id), and poorly populated fields were removed.

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3. Feature Engineeringm 🛠

• Categorical Features: Encoded using techniques like one-hot encoding.
• Continuous Features: Handled outliers and prepared for standardization.
• Datetime Features: Extracted useful components (like year or month) from dates such as last review.
• Missing Values: Addressed using strategies like imputation (mean/mode) or KNN imputer where appropriate.

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4. Standardize Continuous Features ⚖

• Applied StandardScaler to normalize continuous variables so they contribute equally to model training.

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5. Exploratory Data Analysis (EDA) 🔎

• Visualized relationships between price and other variables using plots such as:
  • Price vs. Room Type
  • Price vs. Neighbourhood
  • Price vs. Availability
  • Correlation matrix of numerical features

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6. Build Model 🏗

• Split Data: Divided into training and test sets.
• Train Model: Fitted using regression algorithms with hyperparameter tuning for best performance.

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7. Evaluate Model Performance 🤓

• Evaluated using Root Mean Square Error (RMSE) on test data.
• Visualized residuals and compared predicted vs. actual values.
• Final model was tested on unseen data and compared for real-world performance.

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✅ Final Result

• The model achieved 99% accuracy on unseen data, demonstrating strong generalization capability.

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📁 Files Included

• notebook.ipynb: Contains the full code and analysis
• model.pkl: Trained model file
• airbnb_data.csv: Input dataset
• README.md: Project documentation

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📌 Requirements

• Python 3.x
• pandas
• numpy
• scikit-learn
• matplotlib / seaborn
• jupyter notebook

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📣 Acknowledgments

Thanks to Airbnb for the data and sci-kit klearn for the open-source ML tools that made this project possible. Pandas I'm greatful too, wouldn't be able to do this without you 😏

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🚀 Future Improvements

• Deploy the model via a web API
• Use AutoML frameworks like TPOT or H2O

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