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| # New York City Airbnb Price Prediction | ||
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| ## Goal | ||
| The goal of this project is to predict the prices of Airbnb listings in New York City using various regression models. We will evaluate the performance of these models using metrics such as RMSE, R² score, MAE, and MSE. | ||
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| ## Dataset | ||
| The dataset for this project is sourced from the [New York City Airbnb Open Data on Kaggle](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data/data). | ||
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| ## Description | ||
| The dataset contains information about Airbnb listings in New York City, including features such as the name, host ID, neighbourhood, latitude, longitude, room type, price, minimum nights, number of reviews, last review date, reviews per month, calculated host listings count, availability, and more. | ||
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| ## What I Had Done | ||
| 1. **Exploratory Data Analysis (EDA):** | ||
| - Analyzed the distribution of prices. | ||
| - Visualized the relationship between different features and the target variable (price). | ||
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| 2. **Data Preprocessing:** | ||
| - Handled missing values. | ||
| - Encoded categorical variables. | ||
| - Split the data into training and testing sets. | ||
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| 3. **Model Implementation:** | ||
| - Implemented multiple regression models. | ||
| - Performed hyperparameter tuning for each model. | ||
| - Evaluated the performance of the models. | ||
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| ## Models Implemented | ||
| - Linear Regression (LR) | ||
| - Ridge Regression (Ridge) | ||
| - Lasso Regression (Lasso) | ||
| - ElasticNet Regression (ElasticNet) | ||
| - K-Nearest Neighbors Regression (KNN) | ||
| - Decision Tree Regression (CART) | ||
| - Random Forest Regression (RF) | ||
| - Gradient Boosting Machine (GBM) | ||
| - XGBoost | ||
| - LightGBM | ||
| - CatBoost | ||
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| ## Libraries Needed | ||
| - pandas | ||
| - numpy | ||
| - scikit-learn | ||
| - xgboost | ||
| - lightgbm | ||
| - catboost | ||
| - matplotlib | ||
| - seaborn | ||
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| ## EDA Results | ||
| - The price distribution is skewed to the right. | ||
| - Room type and neighbourhood have a significant impact on the price. | ||
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| ## Performance of the Models Based on Accuracy Scores | ||
| - **Linear Regression (LR):** | ||
| - RMSE: 70.0431 | ||
| - R² Score: 0.6656 | ||
| - MAE: 42.088 | ||
| - MSE: 4906.0328 | ||
| - Execution Time: 0.04 seconds | ||
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| - **Ridge Regression (Ridge):** | ||
| - Best parameters: {'alpha': 1.0} | ||
| - RMSE: 70.0438 | ||
| - R² Score: 0.6656 | ||
| - MAE: 42.0872 | ||
| - MSE: 4906.1288 | ||
| - Execution Time: 2.1 seconds | ||
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| - **Lasso Regression (Lasso):** | ||
| - Best parameters: {'alpha': 0.1} | ||
| - RMSE: 70.1052 | ||
| - R² Score: 0.665 | ||
| - MAE: 42.0402 | ||
| - MSE: 4914.7403 | ||
| - Execution Time: 1.76 seconds | ||
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| - **ElasticNet Regression (ElasticNet):** | ||
| - Best parameters: {'alpha': 0.1, 'l1_ratio': 0.9} | ||
| - RMSE: 70.3563 | ||
| - R² Score: 0.6626 | ||
| - MAE: 42.0211 | ||
| - MSE: 4950.0056 | ||
| - Execution Time: 3.94 seconds | ||
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| - **K-Nearest Neighbors Regression (KNN):** | ||
| - Best parameters: {'n_neighbors': 5} | ||
| - RMSE: 39.7241 | ||
| - R² Score: 0.8924 | ||
| - MAE: 22.0858 | ||
| - MSE: 1578.0056 | ||
| - Execution Time: 6.23 seconds | ||
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| - **Decision Tree Regression (CART):** | ||
| - Best parameters: {'max_depth': None, 'min_samples_leaf': 1} | ||
| - RMSE: 10.2621 | ||
| - R² Score: 0.9928 | ||
| - MAE: 1.1928 | ||
| - MSE: 105.3113 | ||
| - Execution Time: 3.15 seconds | ||
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| - **Random Forest Regression (RF):** | ||
| - Best parameters: {'max_depth': None, 'n_estimators': 50} | ||
| - RMSE: 6.9945 | ||
| - R² Score: 0.9967 | ||
| - MAE: 0.915 | ||
| - MSE: 48.9226 | ||
| - Execution Time: 65.45 seconds | ||
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| - **Gradient Boosting Machine (GBM):** | ||
| - Best parameters: {'learning_rate': 0.1, 'n_estimators': 50} | ||
| - RMSE: 34.4356 | ||
| - R² Score: 0.9192 | ||
| - MAE: 19.4025 | ||
| - MSE: 1185.8113 | ||
| - Execution Time: 25.74 seconds | ||
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| - **XGBoost:** | ||
| - Best parameters: {'learning_rate': 0.1, 'n_estimators': 50} | ||
| - RMSE: 8.4594 | ||
| - R² Score: 0.9951 | ||
| - MAE: 4.6483 | ||
| - MSE: 71.5611 | ||
| - Execution Time: 3.74 seconds | ||
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| - **LightGBM:** | ||
| - Best parameters: {'learning_rate': 0.1, 'n_estimators': 50} | ||
| - RMSE: 8.9302 | ||
| - R² Score: 0.9946 | ||
| - MAE: 4.7429 | ||
| - MSE: 79.7482 | ||
| - Execution Time: 9.23 seconds | ||
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| - **CatBoost:** | ||
| - Best parameters: {'depth': 6, 'iterations': 50, 'learning_rate': 0.1} | ||
| - RMSE: 22.0192 | ||
| - R² Score: 0.967 | ||
| - MAE: 13.5157 | ||
| - MSE: 484.847 | ||
| - Execution Time: 11.29 seconds | ||
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| ## Conclusion | ||
| From the results, we observe that Random Forest Regression (RF) performed the best in terms of RMSE, R² score, MAE, and MSE. It achieved an RMSE of 6.9945, R² score of 0.9967, MAE of 0.915, and MSE of 48.9226, albeit with a longer execution time compared to other models. K-Nearest Neighbors (KNN) and XGBoost also performed well with respectable accuracy and execution times. | ||
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| ## Signature | ||
| - **Name:** Aditya D | ||
| - **Github:** [https://www.github.com/adi271001](https://www.github.com/adi271001) | ||
| - **LinkedIn:** [https://www.linkedin.com/in/aditya-d-23453a179/](https://www.linkedin.com/in/aditya-d-23453a179/) | ||
| - **Topmate:** [https://topmate.io/aditya_d/](https://topmate.io/aditya_d/) | ||
| - **Twitter:** [https://x.com/ADITYAD29257528](https://x.com/ADITYAD29257528) |