Create README.md

adi271001 · Jul 28, 2024 · 2cba106 · 2cba106
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+# New York City Airbnb Price Prediction: Models
+
+## 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
+
+## 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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+- **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
+
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___102_1.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___102_2.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___102_3.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___102_4.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___102_5.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___104_1.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___104_2.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___104_3.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___104_4.png?raw=true)
+![RESULT](https://github.com/adi271001/ML-Crate/blob/airbnb-price/New%20York%20City%20Airbnb%20Price%20Detection/Images/__results___104_5.png?raw=true)
+
+## 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.
+
+## 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)